VLM Compression

VLM: Vision-Language Model

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8 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

UniCompress: From Multi-LoRA Experts to One Compact Model for Edge Intelligence
Nov 4, 2026Mingkang Liu, Peipei Jiao, Weiming Meng +3VLM Compression VLM Quantization
Juhaokan Technology Co., Ltd. Qingdao, China
Retrofitting Recurrent Vision-Language Models from Pretrained Backbones
Oct 15, 2026Madhav Iyengar, Andreas Geiger, Hilde KühneVLM Compression Vision-Language Models
University of Tübingen, Tübingen AI Center · KE:SAI
Hierarchical Compression of Vision-Language Model Benchmarks
Sep 29, 2026Hyunjong Ok, Seunggu Kang, Jaeho LeeVLM Evaluation VLM Compression
Pohang University of Science and Technology · Upstage AI
RiverONE: Generating Knowledge-Intensive VLM by Simulated Quantum Machines
Jun 29, 2026Xindian Ma, Xinyu Long, Yefei Zhang +11Vision-Language Models VLM Compression
Thewake RiverYtz Lab
Finetuning Vision-Language-Action Models Requires Fewer Layers Than You Think
Jun 18, 2026Gia-Binh Nguyen, Trong-Bao Ho, Thien-Loc Ha +18Model Compression VLM Compression
Center for AI Research, VinUniversity · VinRobotics · University of Arkansas +10
LSVD: Loss-Aware Low-Rank Approximation for Efficient Low-Precision Vision-Language Models
May 30, 2026Haiyu Wang, Yutong Wang, Leshu Li +2Vision-Language Models Efficient VLM Inference
Tandon School of Engineering, New York University · Courant Institute of Mathematical Sciences, New York University · Brookhaven National Laboratory
Channel Attention-Guided Cross-Modal Knowledge Distillation for Referring Image Segmentation
Apr 18, 2026Chen YangCross-Modal Learning Cross-Modal Knowledge Distillation
School of Information Science and Technology, TaiShan University, Taian, China
DARK: Diagonal-Anchored Repulsive Knowledge Distillation for Vision-Language Models under Extreme Compression
Mar 5, 2026Numan Saeed, Asif Hanif, Fadillah Adamsyah Maani +2VLM Compression VLM Distillation
Computer Vision, Mohamed Bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE

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UniCompress: From Multi-LoRA Experts to One Compact Model for Edge Intelligence

Mingkang Liu, Peipei Jiao, Weiming Meng, Tianyuan Liu+2

Juhaokan Technology Co., Ltd. Qingdao, China

VLM Compression VLM Quantization Efficient VLM Inference Vision-Language Models Low-Bit Quantization Embodied AI

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Embodied and edge systems increasingly rely on vision-language models (VLMs) to support heterogeneous skills, yet deploying a separate task adapter for each skill while retaining a high-precision backbone creates substantial memory and management costs. We present UniCompress, a deployment-oriented framework that converts multi-task expertise into one compact, adapter-free model. Starting from a consolidated BF16 Qwen3-VL-4B teacher, UniCompress constructs an asymmetric INT2 student using GPTQ initialization and teacher-guided quantization recovery. The approach jointly quantizes and recovers the projection weights in the vision encoder and language backbone, enabling aggressive compression of the complete vision-language model while preserving its multi-task behavior. We evaluate UniCompress on four heterogeneous embodied tasks: posture recognition, household-safety understanding, multimodal dialogue routing, and mobile-GUI action prediction. UniCompress reduces the approximate weight storage from 8.9 GB in BF16 to 2.1 GB in INT2, corresponding to a reduction of approximately 76%. It improves the INT2 macro task score by 34.3 percentage points over GPTQ-INT2 while remaining within 0.9 points of the consolidated BF16 teacher. These results demonstrate the potential of teacher-guided joint vision-language INT2 recovery for deploying a single multi-skill VLM on resource-constrained embodied platforms.