Efficient ViTs

ViT: Vision Transformer

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20 papers in the last four weeks, up 300% on the four weeks before. 0.2% of all new papers.

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Latest papers 152

May 22, 2026cs.CV

Good Token Hunting: A Hitchhiker's Guide to Token Selection for Visual Geometry Transformers

Visual geometry transformers have become powerful architectures for multi-view 3D reconstruction, enabling joint prediction of multiple 3D attributes in a feed-forward manner. However, their computational cost grows quadratically with the input sequence length due to the global attention layers inside these models. This limits both their scalability and efficiency. In this work, we address this challenge with a simple yet general strategy: restricting the number of key/value tokens that each query interacts with during global attention. To achieve effective token selection, we introduce a two-stage framework. First, an inter-frame selection step operates at the frame level to identify frames that should be preserved. Second, an intra-frame selection step further discards more redundant tokens within the selected frames. Our analysis highlights the advantage of a diversity-based strategy for inter-frame selection, which ensures broad coverage of the scene. For intra-frame selection, we show that layer-aware sparsification is necessary, with the selection process guided by the entropy of the global attention pattern. Our approach offers a superior speed-accuracy trade-off compared to existing solutions. Extensive experiments show that it accelerates visual geometry transformers by over 85% for scenes with 500 images while maintaining, or even improving, baseline performance, which hints that how our token selection strategy can play a crucial role in future applications of visual geometry transformers. Our project website is available at https://zsh2000.github.io/good-token-hunting.github.io.

Shuhong Zheng, Michael Oechsle, Erik Sandström +3

University of Toronto & Vector Institute · Google · Technical University of Munich

3D ViTs Multi-View 3D Reconstruction Efficient ViTs Visual Token Pruning

May 22, 2026cs.CV

Recursive Block-Diagonal Coupling for Resource-Efficient Training of Vision Models

Training high-capacity vision models from scratch requires substantial computational resources. To improve training efficiency of a wide target model, existing growth methods often assume the availability of narrower models, obscuring the true computational cost of the entire pipeline. We propose an efficient training protocol, RBDC, that builds wide models by coupling in a parameter-free block-diagonal way narrower, independently trained models in a recursive way. This allows a flexible allocation of the training budget available across all the models involved. Evaluated with vision transformers (DeiT) and convolutional networks (ResNet) on ImageNet, our RBDC training protocol shows a much better efficiency than models trained from scratch with the standard protocol, yielding 30% FLOPs reduction at similar test accuracies. It also achieves higher performances at same training FLOPs than training protocols from the model growth literature. Finally, we show that our models can serve as better backbones than their original counterparts for downstream object detection and instance segmentation tasks.

Maxim Henry, Adrien Deliège, Sébastien Piérard +1

Montefiore Institute, University of Liège, Liège, Belgium

Deep Learning Optimization Efficient ViTs Image Classification

May 21, 2026cs.LG

ASAP: Attention Sink Anchored Pruning

Vision Transformers (ViTs) face severe computational bottlenecks due to the quadratic complexity of self-attention at high resolutions. Existing token reduction methods rely on local metrics - such as single-layer attention scores - that are inherently vulnerable to the attention sink phenomenon, where uninformative tokens are paradoxically preserved over salient foreground objects. We propose ASAP (Attention Sink Anchored Pruning), a training-free framework that recasts this sink as a feature. Modeling ViT information flow as a Lazy Random Walk, ASAP identifies the sink as a dominant accumulator of probability mass. By computing the diffusion distance to the sink within the cumulative transition matrix, ASAP partitions tokens via Radial Diffusion Clustering and compresses background redundancy through Transition Weight Pooling in a single shot. Extensive experiments across image, video, and vision-language tasks demonstrate ASAP outperforms state-of-the-art methods, accelerating throughput by up to 48% while maintaining - or even exceeding - baseline accuracy.

Jaehyuk Lee, Hanyoung Kim, Yanggee Kim +1

Department of Mathematics Korea University Seoul, Republic of Korea · Korea University Seoul, Republic of Korea

Self-Attention Efficient ViTs Attention Sinks Visual Token Pruning Visual Token Compression

May 21, 2026cs.CV

Accelerating Vision Foundation Models with Drop-in Depthwise Convolution

Pretrained vision foundation models deliver strong performance across tasks with limited fine-tuning. However, their Vision Transformer (ViT) backbones impose high inference costs, limiting deployment on resource-constrained devices. In this work, we accelerate large-scale pretrained ViTs while preserving their feature extraction capabilities by exploiting the intrinsic convolution-like behavior of some attention heads. Specifically, we introduce an efficient depthwise convolution-based layer that serves as a drop-in replacement for these heads. Additionally, we propose simple strategies to identify which heads can be replaced and introduce a fine-tuning procedure that recovers downstream task performance. Across both image classification and segmentation tasks, our method achieves 17-20% percent inference speedup with minimal performance degradation. We validate the approach through detailed derivations, extensive experiments, and efficiency benchmarks. The reference implementation is publicly available.

Carmelo Scribano, Mohammad Mahdi, Nedyalko Prisadnikov +5

University of Modena and Reggio Emilia, Modena, Italy. · INSAIT, Sofia University “St. Kliment Ohridski”, Sofia, Bulgaria

Vision Foundation Models Vision Foundation Model Adaptation Efficient ViTs Convolutional Neural Networks

May 20, 2026cs.CV

Machine Intelligence that Understands Visual and Linguistic Information and Interacts with Humans and Environments

Advancements at the intersection of computer vision and natural language processing are crucial for applications like assistive tech, multimedia querying, and robotics. This dissertation proposes novel architectures to improve intelligent agents across three key vision-language tasks: image captioning, visual dialog, and interactive instruction following. First, we address limitations in visual representation for image captioning. Traditional models rely on region-based features from CNN detectors, which lack global context and suffer from high computational overhead. We propose GRIT (Grid and Region-based Image captioning Transformer), a transformer-only architecture. By integrating grid and region features using a DETR-based detector, GRIT enables end-to-end training and out-performs prior methods in both inference accuracy and speed. Second, we tackle visual dialog, which requires multi-turn conversation about an image. The challenge lies in efficiently modeling interactions between multiple inputs (image, question, history). We introduce LTMI (Light-weight Transformer for Many Inputs). Utilizing a specialized attention block, an LTMI layer matches the representational power of a standard Transformer extension while utilizing less than one-tenth of its parameters, as validated on the VisDial dataset. Finally, we study interactive instruction-following for embodied AI using the ALFRED dataset. We propose a framework featuring a two-stage instruction interpretation: it first decodes language directives independently of visual context to predict a tentative action-object sequence, which is then fused with visual features for final execution. Using multiple egocentric views and hierarchical attention, our method accurately localizes objects and achieves a state-of-the-art unseen success rate of 8.37%.

Van Quang Nguyen

TOHOKU UNIVERSITY · Graduate School of Information Sciences · Department of System Information Sciences

Image Captioning Efficient ViTs Embodied AI VLMs for Robotics

May 19, 2026cs.CV

Faster or Stronger: Towards Flexible Visual Place Recognition via Weighted Aggregation and Token Pruning

Visual Place Recognition (VPR) aims to match a query image to reference images of the same place in a large-scale database. Recent state-of-the-art methods employ Vision Transformers (ViTs) as backbone foundation models to extract patch-level features that are robust to viewpoint, illumination, and seasonal variations, which are then aggregated into a compact global descriptor for retrieval. Most existing aggregation methods uniformly pool patch tokens into learned clusters, despite the fact that different clusters often encode distinct spatial or semantic patterns and contribute unequally to VPR performance. To address this limitation, we propose Weighted Aggregated Descriptor (WeiAD), which assigns weights to clusters during aggregation, producing more discriminative global representations. Beyond accuracy, retrieval latency is a critical concern for large-scale deployments and resource-constrained edge devices. Prior work mainly reduces latency by compressing global descriptors, while overlooking the cost of feature extraction, an issue exacerbated by ViT-based backbones. We therefore introduce WeiToP, a VPR-oriented token pruning framework that reduces feature extraction cost via self-distillation, where aggregation-induced token importance supervises a lightweight pruning module attached to an early transformer layer, enabling inference-time token pruning. After a single joint training phase, WeiToP enables plug-and-play token pruning at inference time, allowing flexible and on-demand control over the accuracy-efficiency trade-off without additional training. Moreover, WeiToP outperforms existing token pruning methods adapted from general vision tasks.

Zichao Zeng, June Moh Goo, Junwei Zheng +4

University College London, London, UK · Karlsruhe Institute of Technology, Karlsruhe, Germany · Shenzhen University, Shenzhen, China +1

Visual Place Recognition Efficient ViTs Visual Token Pruning

May 18, 2026cs.CV

LESSViT: Robust Hyperspectral Representation Learning under Spectral Configuration Shift

Modeling hyperspectral imagery (HSI) across different sensors presents a fundamental challenge due to variations in wavelength coverage, band sampling, and channel dimensionality. As a result, models trained under a fixed spectral configuration often fail to generalize to other sensors. Existing Vision Transformer (ViT) approaches either rely on implicit spectral modeling with fixed channel assumptions or adopt explicit spatial-spectral attention with prohibitive computational cost, leading to a fundamental trade-off between efficiency and expressiveness. In this work, we introduce Low-rank Efficient Spatial-Spectral ViT (LESSViT), a sensor-flexible architecture for cross-spectral generalization. LESSViT is built on LESS Attention, a structured low-rank factorization that models joint spatial-spectral interactions through separable spatial and spectral components, reducing the complexity of full spatial-spectral attention from

O(N^2 C^2)

to

O(rNC)

, where

N

is the number of spatial tokens,

C

is the number of spectral channels, and

r

is the rank of the low-rank approximation. We further incorporate channel-agnostic patch embedding and wavelength-aware positional encoding to support flexible spectral inputs. To enable efficient and robust pretraining, we introduce a hyperspectral masked autoencoder (HyperMAE) with decoupled spatial-spectral masking and hierarchical channel sampling. We evaluate LESSViT under a cross-spectral generalization setting that simulates cross-sensor variability. Experiments on the SpectralEarth benchmark demonstrate that LESSViT improves robustness under spectral shifts while remaining competitive in-distribution, and explicit and efficient spatial-spectral modeling is essential for scalable and generalizable hyperspectral representation learning.

Haozhe Si, Yuxuan Wan, Yuqing Wang +2

Department of Electrical and Computer Engineering · 2Siebel School of Computing and Data Science

Representation Learning Masked Autoencoders Hyperspectral Imaging Efficient ViTs

May 18, 2026cs.CV

Token-Space Mask Prediction for Efficient Vision Transformer Segmentation

Query-based Vision Transformer segmentation models typically reconstruct dense spatial feature maps to predict masks, inheriting design patterns from convolutional architectures. We show that this explicit image-space reconstruction is not required. We introduce TokenMask, a token-space mask head that computes mask logits directly from query-token affinities and performs interpolation in logit space rather than feature space. This reformulation preserves the original linear scoring mechanism while simplifying the computational structure. Across diverse ViT backbones, datasets and segmentation tasks, TokenMask consistently improves efficiency over prior approaches by reducing computational and memory requirements while maintaining competitive accuracy, leading to tangible speedups on NVIDIA Jetson AGX Orin using TensorRT FP16 inference. Overall, TokenMask yields a simpler and more deployment-friendly design for embedded vision systems.

Calvin Galagain, Martyna Poreba, François Goulette

Universit´e Paris-Saclay, CEA List · U2IS, ENSTA Paris, Institut Polytechnique de Paris · F-91120 Palaiseau, France

Vision Transformer Efficient ViTs Semantic Segmentation

May 17, 2026cs.CV

SparseSAM: Structured Sparsification of Activations in Segment Anything Models

The Segment Anything Model (SAM) achieves strong open-vocabulary segmentation, but its ViT-based image encoders dominate inference latency and memory. Existing activation compression methods, such as token merging, reduce the token length to process, yet introduce non-trivial runtime overhead and encounter catastrophic quality drop under high compression. Other methods applying Sparse Attention focus on attention alone, leaving the MLP fully dense and capping achievable speedup. We propose SparseSAM, a (i) training-free structured sparsification framework that jointly accelerates attention and MLP layers while preserving token identity. SparseSAM introduces (ii) Stripe-Sort Attention, which uses a deterministic Z-order permutation to transform dense attention into static hardware-friendly sparse patterns, eliminating dynamic masking overhead. SparseSAM further introduces a (iii) Residual-Consistency MLP that routes only informative tokens through the MLP while propagating remaining tokens through the residual pathway. Across four segmentation benchmarks, SparseSAM loses only 0.004 mIoU at a 0.4 density and 0.021 mIoU at 0.3, a 2.10x reduction in accuracy loss versus token merging advances, while achieving 2x faster inference and 2.8x memory reduction.

Hoai-Chau Tran, Chi H. Nguyen, Duy M. H. Nguyen +3

University of Illinois at Urbana-Champaign · VinUni-Illinois Smart Health Center, VinUniversity · College of Engineering & Computer Science, VinUniversity +3

Efficient Transformer Inference Image Segmentation Promptable Image Segmentation Efficient ViTs Sparse Attention Sparse Neural Networks+1

May 17, 2026cs.CV

Systematic Evaluation of Vision Transformers for Automated Cervical Cancer Classification: Optimization, Statistical Validation, and Clinical Interpretability

Manual Pap smear analysis for cervical cancer screening is limited by inter-observer variability, time constraints, and restricted expert availability. Although convolutional neural networks (CNNs) have automated cervical cell classification, they remain limited in modeling long-range spatial dependencies and often lack clinical interpretability. In this study, Vision Transformer (ViT) architectures were systematically optimized to enhance automated cervical cancer screening, which resulted in improved interpretability. The Herlev dataset (917 images: 242 normal, 675 abnormal) was utilized to optimize ViT-Tiny, a lightweight Vision Transformer architecture designed for reduced computational complexity, through a comprehensive evaluation of augmentation strategies, class weighting, and hyperparameters. The optimal configuration achieved 94.9%-95.2% cross-validation accuracy, in which random horizontal flipping and class weighting (0.7 x 1.3) were identified as most effective. Gradient-weighted Class Activation Mapping (Grad-CAM) analysis confirmed that model attention corresponded to clinically relevant morphological features, which include nuclear regions, cell boundaries, and chromatin texture, which align with cytopathological criteria. These findings indicate that Vision Transformers can deliver accurate and interpretable decision support for cervical cancer screening, which fulfills both clinical performance and transparency requirements essential for medical AI deployment.

Nisreen Albzour, Sarah S. Lam

School of Systems Science and Industrial Engineering, Binghamton University, Binghamton, NY 13902, USA

Vision Transformer Efficient ViTs Medical Image Classification Explainable Medical Image Analysis

May 15, 2026cs.LG

From Sparsity to Simplicity: Enabling Simpler Sequential Replacements via Sparse Attention Distillation

Self-attention serves as the core foundation of large-scale transformer pretraining, but its quadratic token interaction cost makes inference expensive. Replacing attention with simpler sequential modules is appealing, yet naive substitution is often lossy, especially at larger scales. This paper revisits attention replacement through the lens of sparsity. Based on the observation of diverse sparsity patterns across transformer layers, we posit that pretrained transformers decompose the complex token dependency across tokens into various sequence-to-sequence mappings of diverse complexities, where some layer functionalities can be approximated and replaced with much simpler sequential modules without loss. We evaluate this premise using a plug-and-play layer-wise distillation framework to approximate and replace attention functionalities in pretrained vision transformer models. Controlled group-wise replacements under a fixed training budget reveal a clear pattern: substituting layers with sparser attention incurs substantially smaller accuracy drops than replacing denser ones. We further impose explicit attention sparsity on the pretrained ViT via AViT-style token retention and perform sparsity-guided distillation for sequential replacing models, where we see increasing teacher sparsity consistently reduces the student-teacher gap. The proposed method achieves efficient attention replacement for reduced parameter size and latency through the guidance of attention sparsity.

Yuxin Ren, Maxwell D Collins, Miao Hu +1

University of Arizona, Tucson AZ, USA · TetraMem, Inc., San Jose CA, USA

Self-Attention Vision Transformer Efficient ViTs Sparse Attention

May 14, 2026cs.CV

Representative Attention For Vision Transformers

Linear attention has emerged as a promising direction for scaling Vision Transformers beyond the quadratic cost of dense self-attention. A prevalent strategy is to compress spatial tokens into a compact set of intermediate proxies that mediate global information exchange. However, existing methods typically derive these proxy tokens from predefined spatial layouts, causing token compression to remain anchored to image coordinates rather than the semantic organization of visual content. To overcome this limitation, we propose Representative Attention (RPAttention), a linear global attention mechanism that performs token compression directly in representation space. Instead of constructing intermediate tokens from fixed spatial partitions, it dynamically forms a compact set of learned representative tokens to enable semantically related regions to communicate regardless of their spatial distance, by following a lightweight Gather-Interact-Distribute paradigm. Spatial tokens are first softly gathered into representative tokens through competitive similarity-based routing. The representatives then perform global interaction within a compact latent space, before broadcasting the refined information back to all spatial tokens via query-driven cross-attention. Via replacing coordinate-driven aggregation with representation-driven compression, RPAttention preserves global receptive fields while adaptively aligning token communication with the content structure of each input.RPAttention reduces the dominant token interaction complexity from quadratic to linear scaling with respect to the number of spatial tokens, while maintaining expressive global context modeling. Extensive experiments across diverse vision transformer backbones on image classification, object detection, and semantic segmentation demonstrate the effectiveness of our design.

Yuntong Li, Hainuo Wang, Hengxing Liu +2

College of Intelligence and Computing, Tianjin University, Tianjin 300350, China

Vision Transformer Efficient ViTs Linear Attention Visual Token Compression

May 14, 2026cs.CV

TurboVGGT: Fast Visual Geometry Reconstruction with Adaptive Alternating Attention

Recent feed-forward 3D reconstruction methods, such as visual geometry transformers, have substantially advanced the traditional per-scene optimization paradigm by enabling effective multi-view reconstruction in a single forward pass. However, most existing methods struggle to achieve a balance between reconstruction quality and computational efficiency, which limits their scalability and efficiency. Although some efficient visual geometry transformers have recently emerged, they typically use the same sparsity ratio across layers and frames and lack mechanisms to adaptively learn representative tokens to capture global relationships, leading to suboptimal performance. In this work, we propose TurboVGGT, a novel approach that employs an efficient visual geometry transformer with adaptive alternating attention for fast multi-view 3D reconstruction. Specifically, TurboVGGT employs an end-to-end trainable framework with adaptive sparse global attention guided by adaptive sparsity selection to capture global relationships across frames and frame attention to aggregate local details within each frame. In the adaptive sparse global attention, TurboVGGT adaptively learns representative tokens with varying sparsity levels for global geometry modeling, considering that token importance varies across frames, attention layers operate tokens at different levels of abstraction, and global dependencies rely on structurally informative regions. Extensive experiments on multiple 3D reconstruction benchmarks demonstrate that TurboVGGT achieves fast multi-view reconstruction while maintaining competitive reconstruction quality compared with state-of-the-art methods. Project page: https://turbovggt.github.io/.

David Huang, Guile Wu, Chengjie Huang +2

Huawei Noah’s Ark Lab · University of Toronto · Foundation Model Department, Huawei

3D ViTs Multi-View 3D Reconstruction Efficient ViTs Sparse Attention Feed-Forward 3D Reconstruction

May 13, 2026cs.CV

SToRe3D: Sparse Token Relevance in ViTs for Efficient Multi-View 3D Object Detection

Vision Transformers (ViTs) enable strong multi-view 3D detection but are limited by high inference latency from dense token and query processing across multiple views and large 3D regions. Existing sparsity methods, designed mainly for 2D vision, prune or merge image tokens but do not extend to full-model sparsity or address 3D object queries. We introduce SToRe3D, a relevance-aligned sparsity framework that jointly selects 2D image tokens and 3D object queries while storing filtered features for reactivation. Mutual 2D-3D relevance heads allocate compute to driving-critical content and preserve other embeddings. Evaluated on nuScenes and our new nuScenes-Relevance benchmark, SToRe3D achieves up to 3x faster inference with marginal accuracy loss, establishing real-time large-scale ViT-based 3D detection while maintaining accuracy on planning-critical agents.

Sandro Papais, Lezhou Feng, Charles Cossette +1

University of Toronto · Zoox Inc.

Multi-View 3D Object Detection Token Pruning Efficient ViTs 3D Object Detection Real-Time Object Detection

May 13, 2026cs.CV

Evolving Layer-Specific Scalar Functions for Hardware-Aware Transformer Adaptation

Vision Transformers (ViTs) achieve state-of-the-art performance on challenging vision tasks, but their deployment on edge devices is severely hindered by the computational complexity and global reduction bottleneck imposed by layer normalization. Recent methods attempt to bypass this by replacing normalization layers with hardware-friendly scalar approximations. However, these homogeneous replacements do not optimally fit to all layers' behaviour and rely on expensive model retraining. In this work, we propose a highly efficient, hardware-aware framework that utilizes genetic programming (GP) to evolve heterogeneous, layer-specific scalar functions directly from pre-trained weights. Coupled with a novel post-training re-alignment strategy, our approach eliminates the need to retrain models from scratch entirely. Our evolved expressions accurately approximate the target normalization behaviours, capturing

91.6\%

of the variance (

R^2

) compared to only

70.2\%

for homogeneous baselines, allowing our modified architecture to recover

84.25\%

Top-1 ImageNet-1K accuracy in only 20 epochs. By preserving this performance while eliminating the global reduction bottleneck, our approach establishes a highly favourable trade-off between arithmetic complexity and off-chip memory traffic, removing a primary barrier to the efficient deployment of ViTs on edge accelerators.

Kieran Carrigg, Sigur de Vries, Amirhossein Sadough +1

Department of Machine Learning and Neural Computing Donders Institute for Brain, Cognition, and Behaviour Thomas van Aquinostraat 4, 6525 GD Nijmegen, The Netherlands

Efficient Neural Network Inference Efficient ViTs Layer Normalization Edge ML

May 12, 2026cs.CV

Do Vision Transformers Need All-to-All Attention? Global Communication Through Elastic Learned Cores

Vision Transformers (ViTs) learn rich visual-semantic representations through all-to-all self-attention among patch tokens. However, this design implicitly assumes that direct pairwise patch interactions are necessary for effective representation learning. In this work, we challenge this assumption and show that representations supporting both global recognition and dense prediction can be learned without direct patch-to-patch interaction. We propose VECA (Visual Elastic-Core Attention), a vision transformer with core-periphery structured attention mediated by a small set of learned cores. Patch tokens exchange global information exclusively through these cores, while the full set of dense patches are preserved and iteratively updated across layers. This reduces attention complexity from

O(N^2)

to

O(N)

, linear in the number of patches

N

for a fixed core budget

C

. Unlike prior latent-token cross-attention architectures, VECA facilitates sparse global communication without compressing the spatial representation itself. Nested training along the core axis further enables a single model to elastically trade off computation and accuracy at inference time without retraining. Across image classification and dense prediction tasks, VECA remains competitive with full-attention backbones and outperforms the evaluated linear-complexity alternatives on most benchmarks. Moreover, without explicit supervision, these cores develop semantically organized structures that support object-label transfer across video frames. These results show that effective visual representations can be learned without direct all-to-all patch interaction.

Alan Z. Song, Yinjie Chen, Mu Nan +3

Carnegie Mellon University · University of Hong Kong

Visual Representation Learning Vision Transformer Transformer Attention Efficient ViTs Sparse Attention Dense Prediction

May 12, 2026cs.CV

Spectral Vision Transformer for Efficient Tokenization with Limited Data

We propose a novel spectral vision transformer architecture for efficient tokenization in limited data, with an emphasis on medical imaging. We outline convenient theoretical properties arising from the choice of basis including spatial invariance and optimal signal-to-noise ratio. We show reduced complexity arising from the spectral projection compared to spatial vision transformers. We show equitable or superior performance with a reduced number of parameters as compared to a variety of models including compact and standard vision transformers, convolutional neural networks with attention, shifted window transformers, multi-layer perceptrons, and logistic regression. We include simulated, public, and clinical data in our analysis and release our code at: \verb+github.com/agr78/spectralViT+.

Alexandra G. Roberts, Maneesh John, Jinwei Zhang +10

Medical Imaging Image Tokenization Vision Transformer Visual Tokenization Efficient ViTs Image Classification+1

May 12, 2026cs.CV

DORA: Dynamic Online Reinforcement Agent for Token Merging in Vision Transformers

Vision Transformers (ViTs) incur significant computational overhead due to the quadratic complexity of self-attention relative to the token sequence length. While existing token reduction methods mitigate this issue, they predominantly rely on fixed heuristic metrics, predefined ratios, or static offline masks, which lack the adaptability to capture input-dependent redundancy during inference. In this paper, we propose DORA (Dynamic Online Reinforcement Agent), the first reinforcement learning (RL)-driven online inference framework for dynamic token merging in ViTs. We formulate the merging process as a sequential Markov Decision Process (MDP), where a lightweight RL agent determines the merging strategy for each Transformer block based on the current feature state and layer-specific context. To balance computational efficiency and feature fidelity, the agent is optimized via a dense reward function incorporating a non-linear distillation-based penalty. We implement an asymmetric Actor-Critic architecture that utilizes a high-capacity Critic for stable offline training while retaining a minimal Actor head for low-computation online inference. Evaluations across multiple ViT scales (Tiny to Large) demonstrate that DORA improves the accuracy-efficiency Pareto front compared to current baselines. Under strict negligible accuracy-drop constraints (<= 0.05%), DORA achieves up to a 12.66% token merging rate, and delivers up to a 569.7% relative improvement over the most efficient baseline. On ImageNet-1K, under aligned accuracy constraints, DORA achieves up to a 76% relative improvement in computational savings compared to state-of-the-art methods. Furthermore, on out-of-distribution (OOD) benchmarks such as ImageNet-A and ImageNet-C, DORA attains a relative efficiency advantage of over 430%.

Kaixuan He, Song Chen, Yi Kang

University of Science and Technology of China, Hefei, China · Institute of Artificial Intelligence, Hefei Comprehensive National Science Center

Efficient ViTs Visual Token Merging Visual Token Compression

May 12, 2026cs.NE

Breaking Global Self-Attention Bottlenecks in Transformer-based Spiking Neural Networks with Local Structure-Aware Self-Attention

Transformer-based Spiking Neural Networks (SNNs) integrate SNNs with global self-attention and have demonstrated impressive performance. However, existing Transformer-based SNNs suffer from two fundamental limitations. First, they typically employ max pooling layers to reduce the size of feature maps, but the max pooling captures only the strongest response and fails to comprehensively preserve representative regional features. Second, the global self-attention involves all global feature interactions, resulting in computational redundancy and quadratic computational complexity, thus conflicting with the sparse and energy-efficient characteristics of SNNs. To address these challenges, we develop Local Structure-Aware Spiking Transformer (LSFormer), a novel Transformer-based Spiking Neural Network that incorporates Spiking Response Pooling (SPooling) and Local Structure-Aware Spiking Self-Attention (LS-SSA). For the first time, our LSFormer leverages a local dilated window mechanism to capture both local details and long-range dependencies. Experimental results demonstrate that our LSFormer achieves state-of-the-art performance compared to existing advanced Transformer-based SNNs. Notably, on the more challenging static dataset Tiny-ImageNet and neuromorphic dataset N-CALTECH101, LSFormer substantially outperforms state-of-the-art baselines by 4.3% and 8.6% in top-1 classification accuracy, respectively. These results highlight the potential of LSFormer to advance energy-efficient spiking models toward practical deployment in large-scale vision applications.

Lingdong Li, Hangming Zhang, Qiang Yu

School of Future Technology, Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin 300354, China · School of Artificial Intelligence, Tianjin Key Laboratory of Cognitive Computing and Application, College of Intelligence and Computing, Tianjin University, Tianjin 300354, China

Spiking Transformers Efficient ViTs Spiking Neural Networks Local Attention Attention Mechanisms Image Classification

May 12, 2026cs.CV

Lite3R: A Model-Agnostic Framework for Efficient Feed-Forward 3D Reconstruction

Transformer-based 3D reconstruction has emerged as a powerful paradigm for recovering geometry and appearance from multi-view observations, offering strong performance across challenging visual conditions. As these models scale to larger backbones and higher-resolution inputs, improving their efficiency becomes increasingly important for practical deployment. However, modern 3D transformer pipelines face two coupled challenges: dense multi-view attention creates substantial token-mixing overhead, and low-precision execution can destabilize geometry-sensitive representations and degrade depth, pose, and 3D consistency. To address the first challenge, we propose Lite3R, a model-agnostic teacher-student framework that replaces dense attention with Sparse Linear Attention to preserve important geometric interactions while reducing attention cost. To address the second challenge, we introduce a parameter-efficient FP8-aware quantization-aware training (FP8-aware QAT) strategy with partial attention distillation, which freezes the vast majority of pretrained backbone parameters and trains only lightweight linear-branch projection layers, enabling stable low-precision deployment while retaining pretrained geometric priors. We further evaluate Lite3R on two representative backbones, VGGT and DA3-Large, over BlendedMVS and DTU64, showing that it substantially reduces latency (1.7-2.0x) and memory usage (1.9-2.4x) while preserving competitive reconstruction quality overall. These results demonstrate that Lite3R provides an effective algorithm-system co-design approach for practical transformer-based 3D reconstruction. Code: https://github.com/AIGeeksGroup/Lite3R. Website: https://aigeeksgroup.github.io/Lite3R.

Haoyu Zhang, Zeyu Zhang, Zedong Zhou +2

1Peking University · 2La Trobe University

Quantization-Aware Training 3D Reconstruction Efficient ViTs Feed-Forward 3D Reconstruction

May 11, 2026cs.LG

RelFlexformer: Efficient Attention 3D-Transformers for Integrable Relative Positional Encodings

We present a new class of efficient attention mechanisms applying universal 3D Relative Positional Encoding (RPE) methods given by arbitrary integrable modulation functions

f

. They lead to the new class of 3D-Transformer models, called \textit{RelFlexformers}, flexibly integrating those RPEs, and characterized by the

O(L \log L)

time complexity of the attention computation for the

L

-length input sequences. RelFlexformers builds on the theory of the Non-Uniform Fourier Transform (NU-FFT), naturally generalizing several existing efficient RPE-attention methods from structured settings with tokens homogeneously embedded in unweighted grids into general non-structured heterogeneous scenarios, where tokens' positions are arbitrarily distributed in the corresponding 3D spaces. As such, RelFlexformers can be applied in particular to model point clouds. Our extensive empirical evaluation on a large portfolio of 3D datasets confirms quality improvements provided by the NU-FFT-driven attention modulation techniques in the RelFlexformers.

Byeongchan Kim, Arijit Sehanobish, Avinava Dubey +2

1Seoul National University · 2Independent · 3Google Research +2

3D ViTs Self-Attention Efficient ViTs Positional Encoding

May 11, 2026cs.CV

Investigating Single-Block Recurrence in Vision Transformers for Image Recognition

Vision Transformers (ViTs) implement depth by stacking independently parameterized blocks, but it remains unclear how much of this parameterization is necessary and how much can be replaced by recurrent reuse. We study this question with bViT, a single-block recurrent ViT that repeatedly applies the same transformer block while preserving the iterative computation of a deep model. On ImageNet-1K, bViT-B reaches 0.779 validation accuracy compared with 0.789 for ViT-B under the same training recipe and computational budget, while using 8.6M rather than 86.6M parameters. This correspondence becomes stronger with model width, while narrow recurrent models exhibit a substantial performance gap. Beyond classification, the single-block formulation provides a controlled testbed for studying how transformer computation evolves with depth, since the same heads, neurons, and weight matrices can be tracked across recurrent steps. Analyses of attention, activation patterns, and step-conditioned spectral pruning reveal temporally organized behavior and step-dependent utilization of the shared parameters. bViT also transfers competitively to downstream tasks while enabling highly parameter-efficient adaptation. Our work shows that much of the performance associated with independently parameterized ViT depth can be recovered through recurrent reuse of a single sufficiently wide transformer block.

Michal Byra, Pawel Olszowiec, Grzegorz Stefanski +2

Samsung AI Center, Warsaw, Poland · Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland

Vision Transformer Recurrent Transformers Efficient ViTs Image Classification

May 11, 2026cs.CV

MicroViTv2: Beyond the FLOPS for Edge Energy-Friendly Vision Transformers

The Vision Transformer (ViT) achieves remarkable accuracy across visual tasks but remains computationally expensive for edge deployment. This paper presents MicroViTv2, a lightweight Vision Transformer optimized for real-device efficiency. Built upon the original MicroViT, the proposed model is designed based on reparameterized design, specifically Reparameterized Patch Embedding (RepEmbed) and Reparameterized Depth-Wise convolution mixer (RepDW) for faster inference, and introduces the Single Depth-Wise Transposed Attention (SDTA) to capture long-range dependencies with minimal redundancy. Despite slightly higher FLOPs, MicroViTv2 improves accuracy up to 0.5% compared to its predecessor and surpassing MobileViTv2, EdgeNeXt, and EfficientViT while maintaining fast inference and high energy efficiency on Jetson AGX Orin. Experiments on ImageNet-1K and COCO demonstrate that hardware-aware design and structural re-parameterization are key to achieving high accuracy and low energy consumption, validating the need to evaluate efficiency beyond FLOPs. Code is available at https://github.com/novendrastywn/MicroViT.

Novendra Setyawan, Chi-Chia Sun, Mao-Hsiu Hsu +2

Department of Electro-Optics, National Formosa University, Taiwan · Department of Electrical Engineering, University of Muhammadiyah Malang, Indonesia · Department of Electrical Engineering, National Taipei University, Taiwan +1

Energy-Efficient ML Edge Inference Efficient ViTs Image Classification

May 8, 2026cs.CV

PrePARE: Pre-AA Token Pruning for Frozen Multi-View Geometry Transformers

Multi-view geometry transformers are feed-forward 3D foundation models that jointly predict depth maps, point maps, and camera poses for N images in a single forward pass. Most of them build on an alternating-attention (AA) stack whose cross-view attention runs over the patch tokens of all N views in every block, so its cost grows quadratically in N: on 300-frame ScanNetv2 clips, VGGT peaks at 72.3 GiB, and MapAnything does not fit on a 93 GiB card. Existing methods reduce tokens or attention inside the AA stack, so the full patch grid still enters it, and the pre-AA interface, where the encoder hands its tokens to the first AA block, has been overlooked. We propose PrePARE, which prunes patch tokens once at the pre-AA interface and restores the dense grid once after the last AA block. Every block of the frozen AA stack then runs on the reduced tokens, while the prediction heads receive the full grid. A Token Scorer, supervised by how the unpruned AA stack uses each token, and a Feature-guided Restoration module are the only trained parts. The Token Routing module is rule-based. On ScanNetv2 at N = 300, PrePARE reduces the peak memory of VGGT by 80%, from 72.3 to 14.4 GiB, below every in-AA method we ran, and runs 5.4 times faster, while its per-scene Chamfer distance is lower than the unpruned model's on 34 of 50 scenes (sign test p = 0.015). On pi^3 and Depth Anything 3 it saves 15% and 20% of peak memory, where the in-AA methods save at most 9%, and at 1000 frames it is the only method we ran that fits Depth Anything 3 and MapAnything on one GPU.

Haotang Li, Zhenyu Qi, Shaohan Henry Wang +5

University of Arizona · East Carolina University · Augusta University +1

Multi-View Geometry Efficient ViTs Feed-Forward 3D Reconstruction Visual Token Pruning

May 8, 2026cs.CV

An Efficient Token Compression Framework for Visual Object Tracking

Refining visual representations by eliminating their internal feature-level redundancy is crucial for simultaneously optimizing the performance and computational cost of models in visual tracking. To enhance their performance, many contemporary Transformer-based trackers leverage a larger number of historical template frames to capture richer spatio-temporal cues. However, this strategy leads to a massive number of input visual tokens. This creates two critical issues: it imposes a quadratic computational burden and can also degrade the tracker's overall performance. To bridge this gap, we propose a compress-then-interact tracking framework, ETCTrack, that learns to efficiently compress template tokens from historical template frames into a robust target representation, moving beyond handcrafted rules. Our method first employs the Adaptive Token Compressor to dynamically construct compact yet highly discriminative template tokens by filtering out redundant visual tokens. These refined template tokens are then processed by our Hierarchical Interaction Encoder to achieve a deep, adaptive interaction with the search features. Refined search features ensure subsequent precise target localization. Experiments on seven benchmarks demonstrate that our method outperforms current state-of-the-art trackers. ETCTrack-B224 reduces the number of template tokens by 60%, leading to a 21.4% reduction in MACs with only a 0.4% drop in accuracy. The source code are available at https://github.com/PJD-WJ/ETCTrack.

Weijing Wu, Qihua Liang, Bineng Zhong +3

Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin, 541004, China · Guangxi Key Lab of Multi-Source Information Mining and Security, Guangxi Normal University, Guilin, 541004, China · University Engineering Research Center of Educational Intelligent Technology, Guangxi Normal University, Guilin, 541004, China +1

Visual Object Tracking Efficient ViTs Visual Token Pruning Visual Token Compression

May 8, 2026cs.CV

Amortized-Precision Quantization for Early-Exit Vision Transformers

Vision Transformers (ViTs) achieve strong performance across vision tasks, yet their deployment with low-precision early exiting remains fragile. Existing quantization methods assume static full-depth execution, making them unstable when exit decisions are perturbed by quantization noise, which can amplify errors along dynamic inference paths. In this paper, we introduce Amortized-Precision Quantization (APQ), a utilization-aware formulation that accounts for layer-wise stochastic exposure to quantization noise and reveals depth-precision trade-offs. Building on APQ, we propose Mutual Adaptive Quantization with Early Exiting (MAQEE), a bi-level framework that jointly optimizes exit thresholds and bit-widths under explicit risk control to improve inference stability. MAQEE establishes a superior Pareto frontier in the accuracy-efficiency trade-off, reducing BOPs by up to 95% while maintaining accuracy and outperforming strong baselines by up to 20% across classification, detection, and segmentation tasks.

Rui Fang, Hsi-Wen Chen, Ming-Syan Chen

National Taiwan University

Mixed-Precision Quantization Dynamic Neural Networks Vision Transformer Efficient ViTs Neural Network Quantization Early-Exit Neural Networks

May 8, 2026cs.CV

SAFformer:Improving Spiking Transformer via Active Predictive Filtering

Spiking Neural Networks (SNNs) offer notable advantages in biological plausibility and energy efficiency, making them promising candidates for building low-power Transformers. However, existing Spiking Transformers largely adhere to a passive reactive paradigm, which struggles to focus on task-relevant information and incurs substantial computational overhead when processing redundant visual data. To overcome this fundamental yet underexplored limitation, we propose SAFformer, a novel Spiking Transformer architecture based on an active predictive filtering paradigm. Inspired by the brain's predictive coding mechanism, SAFformer actively suppresses predictable signals and focuses on salient visual features. Extensive experiments show that SAFformer establishes new state-of-the-art performance on CIFAR-10/100 and CIFAR10-DVS. Remarkably, on ImageNet-1K, it achieves 80.44% Top-1 accuracy with only 26.58M parameters and an energy consumption of 5.88 mJ, demonstrating an exceptional balance between accuracy and efficiency.

Zequan Xie, Weiming Zeng, Yunhua Chen +3

School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China · Faculty of Science, Hong Kong Baptist University, China · School of Electronic Information, Wuhan University, China

Spiking Transformers Efficient ViTs Spiking Neural Networks Image Classification

May 7, 2026cs.CV

LookWhen? Fast Video Recognition by Learning When, Where, and What to Compute

Transformers dominate video recognition. They split videos into tokens, and processing them has expensive superlinear computational cost. Yet videos are filled with redundancy, so we can question the need for this expense. We introduce LookWhen, a selector-extractor framework that factorizes video recognition into learning when, where, and what to compute. Our shallow selector gets a scaled-down video and quickly scores all tokens across space-time, while our deep extractor gets the top-K selected tokens to approximate full-video representations without actually processing all the tokens. A key challenge is defining effective supervision for selection and extraction. For selection pre-training, we introduce a score on representations that ranks tokens by uniqueness using a simple nearest-neighbor distance. For extraction pre-training, we distill both a video teacher and an image teacher, for which we normalize its frame-wise representations to learn what changes within videos. Through these strategies, our selector-extractor learns general and efficient representations for feature extraction or fine-tuning to a task. Through experiments on Kinetics-400, SSv2, Epic-Kitchens, Diving48, Jester, and Charades, we show that LookWhen achieves a better accuracy-computation trade-off than efficient models and upgraded baselines of similar size. LookWhen Pareto-dominates in accuracy-FLOPs on 9 of 12 cases (6 tasks x 2 settings) and roughly matches on 3. In accuracy-throughput, measuring time in practice, LookWhen is more efficient still at 6.7x faster than InternVideo2-B at equal accuracy.

Ali Salamatian, Anthony Fuller, Pritam Sarkar +3

University of British Columbia · Carleton University · Vector Institute

Efficient ViTs Video Action Recognition Video Representation Learning Visual Token Pruning

May 7, 2026cs.CV

Spark3R: Asymmetric Token Reduction Makes Fast Feed-Forward 3D Reconstruction

Feed-forward 3D reconstruction models based on Vision Transformers can directly estimate scene geometry and camera poses from a small set of input images, but scaling them to video inputs with hundreds or thousands of frames remains challenging due to the quadratic cost of global attention layers. Recent token-merging methods accelerate these models by compressing the token sequence within the global attention layers, but they apply a uniform reduction to query tokens and key-value tokens, ignoring their functionally distinct roles in 3D reconstruction. In this work, we identify a key property of feed-forward 3D reconstruction models: query tokens encode view-specific geometric requests and are sensitive to compression, while key-value tokens represent shared scene context and tolerate aggressive compression. Guided by this insight, we propose Spark3R, a training-free acceleration framework that decouples the compression of query tokens and key-value tokens by assigning distinct reduction factors, with intra-group token merging applied to query tokens and lightweight token pruning to key-value tokens. Additionally, Spark3R adaptively adjusts the key-value reduction factor across layers, further improving the quality-efficiency trade-off. As a plug-and-play framework requiring no retraining, Spark3R integrates directly into multiple pretrained feed-forward 3D reconstruction models, including VGGT,

π^3

, Depth-Anything-3, and VGGT-

Ω

, and achieves up to

28\times

speedup on 1,000-frame inputs while maintaining competitive reconstruction quality.

Zecheng Tang, Jiaye Fu, Qiankun Gao +5

School of Electronic and Computer Engineering, Peking University, Shenzhen 518055, China

3D Reconstruction Efficient ViTs Feed-Forward 3D Reconstruction Visual Token Pruning Visual Token Compression

May 5, 2026cs.CV

RD-ViT: Recurrent-Depth Vision Transformer for Semantic Segmentation with Reduced Data Dependence Extending the Recurrent-Depth Transformer Architecture to Dense Prediction

Vision Transformers (ViTs) achieve state-of-the-art segmentation accuracy but require large training datasets because each layer has unique parameters that must be learned independently. We present RD-ViT, a Recurrent-Depth Vision Transformer that adapts the Recurrent-Depth Transformer (RDT) architecture to dense prediction tasks, supporting both 2D and 3D inputs. RD-ViT replaces the deep stack of unique transformer blocks with a single shared block looped T times, augmented with LTI-stable state injection for guaranteed convergence, Adaptive Computation Time (ACT) for spatial compute allocation, depth-wise LoRA adaptation, and optional Mixture-of-Experts (MoE) feed-forward networks for category-specific specialization. We evaluate on the ACDC cardiac MRI segmentation benchmark in both 2D slice-level and 3D volumetric settings with exclusively real experiments executed in Google Colab. In 2D, RD-ViT outperforms standard ViT at 10% training data (Dice 0.774 vs 0.762) and at full data (0.882 vs 0.872). In 3D, RD-ViT with MoE achieves Dice 0.812 with 3.0M parameters, reaching 99.4% of standard ViT performance (0.817) at 53% of the parameter count. MoE expert utilization analysis reveals that different experts spontaneously specialize for different cardiac structures (RV, MYO, LV) without explicit routing supervision. ACT halting maps show higher compute allocation at cardiac boundaries, and the mean ponder time decreases from 2.6 to 1.4 iterations during training, demonstrating learned computational efficiency. Depth extrapolation enables inference with more loops than training without degradation. All code, notebooks, and results are publicly released.

Renjie He

Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

Medical Image Analysis Recurrent Transformers Efficient ViTs 3D Medical Image Segmentation Semantic Segmentation Data-Efficient Learning