Maestro Dataset

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3 papers in the last four weeks, level with the four weeks before. 0.0% of all new papers.

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Latest papers 18

Sep 23, 2026cs.CL

An Explainable DistilBERT-BiLSTM-Attention Framework for Binary and Multi-Class Hate Speech Detection

Hate speech on social media poses serious risks to social harmony, mental well-being, and public safety, making its timely and accurate detection essential for content moderation systems. Most existing studies focus on binary classification, evaluated their frameworks on a single dataset, and provide limited insight into how decisions are made, which limits their real-world applicability. In addition, limited work is done on the explainability of their predictive inference. To address these challenges, this study proposes a multilevel and explainable hate speech detection framework. The proposed model integrates DistilBERT (Distilled Bidirectional Encoder Representations from Transformers) embeddings with a Bi-LSTM (Bidirectional Long Short-Term Memory) model, and an attention mechanism to capture both contextual meaning and sequential dependencies in text. To enhance trust and transparency, LIME (Local Interpretable Model-agnostic Explanations) is employed to explain model predictions by highlighting influential textual features. The framework is evaluated on two benchmark datasets using both binary and multi-class classification to examine robustness and generalization. In addition, an ablation study is presented to highlight the significance of various components of proposed framework. For binary classification, the proposed model achieves F1-scores of 96.78% on the Davidson dataset and 99.53% on the SMHS dataset. In the multi-class setting, it attains F1-scores of 97.00% and 94.99% on the Davidson and SMHS datasets, respectively, outperforming existing baseline approaches. The results demonstrate that multilevel evaluation improves the reliability that the proposed framework effectively balances performance and efficiency. This makes the framework suitable for practical hate speech moderation systems that require accurate, generalizable, and explainable decisions.

Rameesha Zia, Muhammad Shahid Iqbal Malik

School of Computing Sciences, Pak-Austria Fachhochschule, Institute of Applied Sciences and Technology, Haripur 22620, Pakistan

Hate Speech Detection Hate Speech Bert-Based Models Content Moderation Maestro Dataset Bidirectional Long Short-Term Memory+1

Sep 21, 2026cs.RO

Learning to Drive on Mars: Visual Multimodal Traversability Estimation for Off-World Navigation

Autonomous navigation on Mars requires vehicles to distinguish between traversable terrains across diverse and visually challenging environments. However, progress in learning-based navigation for off-world environments has been limited by the lack of large-scale datasets. Since landing in Jezero Crater, the Mars 2020 Perseverance rover has traversed terrain ranging from sandy dunes, rocky patches, and flat bedrocks. As a result, this paper presents a dataset spanning 500 sols and 45km of trajectories driven by both human operators and the onboard planner, ENav. Our dataset contains grayscale stereo image pairs, poses, accelerometer readings, rocker-bogie angles, and estimates of tilt and wheel slip. Building on this dataset, we introduce an uncertainty-aware traversability-estimation framework that learns terrain representations from multimodal driving experience. We compare our proposed method against existing approaches on the Mars 2020 dataset and show that our method achieves an AUROC of 0.874 and an F1 score of 0.758, outperforming the strongest baseline by 0.058 and 0.156, respectively, while also achieving the highest average precision and recall. Finally, we show that the visual representations can be integrated into path planners, such as ENav, on a physical rover test bed. Videos, code, and the M2020 dataset will be available at https://darren-chiu.github.io/learning-to-drive-on-mars.

Darren Chiu, Cole Wilson, Andrei Tumbar +2

University of Southern California, Los Angeles, CA 90089, USA. · Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA. · Washington State University, Pullman, WA 99164, USA.

Traversability Autonomous Navigation Autonomous Surface Vehicles Terrain Navigation Maestro Dataset

Aug 31, 2026cs.CV

OCR-Based Field Extraction for Archaeological Pottery Metadata: The CENTURIA Dataset

Pottery is a primary source for reconstructing the chronological and economic dimensions of past societies. Archaeologists often document ceramic finds through technical drawings and handwritten metadata. This metadata is critical for dating, provenance attribution, and cross-site comparison, but remains inaccessible to computational analysis, requiring manual transcription of every record. We investigate whether state-of-the-art document analysis models can address this task, and introduce CENTURIA, a dataset of 507 pottery records from the Roman site of Carnuntum, providing transcriptions, bounding boxes, and structured field-level labels across seven metadata categories. Benchmarking five OCR models reveals a substantial domain gap: zero-shot transcription error reaches 15-32% SpACER-M, far exceeding rates on printed archival documents, with domain-specific fields recovered in fewer than 3% of cases. LoRA fine-tuning on just 57 samples, reflecting a realistic archival annotation budget, closes this gap, reducing transcription error to below 1.5% and recovering overall field-level accuracy above 87%. Our results show that a small expert-validated fine-tuning set suffices to convert handwritten pottery documentation into structured, searchable metadata ready for archaeological databases.

Gissu Valentina Naghavi, Dominik Hagmann, Martin Kampel +1

Computer Vision Lab, TU Wien, Vienna, Austria · Austrian Archaeological Institute, Austrian Academy of Sciences, Vienna, Austria

Historical Manuscripts Archive Metadata Maestro Dataset

Aug 30, 2026cs.CV

MariSat: A Maritime Dataset for Instance Segmentation of Objects in Satellite and Aerial Images

Automated maritime surveillance from satellite and aerial imagery requires large, precisely annotated datasets, which remain scarce for the instance-segmentation task, particularly for small vessels in cluttered port environments. We present MariSat, a new benchmark dataset of 1260 aerial and satellite images covering diverse port and coastal scenes, annotated at the pixel level for eight maritime object classes (sailboat, yacht, jet-ski, fishing boat, cruise ship, military vessel, tugboat and cargo ship). The dataset was produced through a semi-automatic annotation pipeline combining the textpromptable segmentation model SAM 3 with a cascade of geometric and colorimetric post-processing filters, followed by a manual correction and quality-control pass performed with the CVAT annotation platform. We describe the image-collection methodology, the annotation and correction process, and the resulting data organization. We also report class-wise statistics for the training, validation, and test splits. MariSat has already been used to fine-tune and benchmark segmentation and detection models (SAM 3 and YOLO11) for real-time maritime monitoring. We report detailed quantitative and per-class results for both tasks. The MariSat dataset is publicly available on GitHub : https://github.com/amirabbes/P2M-Maritime-Segmentation

Amir Abbes, Ines Harrabi, Lucas Justin Yirepoa Kinda +3

University of Carthage, SUP’COM, LR11TIC05, MEDIATRON, 2083, Ariana, Tunisia · University of Gabes, Hatem Bettaher IResCoMath Laboratory, Gabes, Tunisia · University of Rouen Normandie, ESIGELEC, IRSEEM, 76000 Rouen, France

Ship Detection Vision Datasets Aerial Imagery Maestro Dataset Objects

Aug 11, 2026cs.CV

Mixture-of-Experts-based Entropy Model for Learned Image Compression

Learned image compression has seen significant progress in recent years with the development of end-to-end learned models that achieve better compression efficiency than state-of-the-art conventional methods. Recently, Mixture of Experts (MoE) approaches have seen promising results in NLP and computer vision tasks. In this paper, we introduce the MoE approach to learned image compression. We propose a MoE-based Entropy model (MoEE) for learned image compression, allowing the model to selectively activate only the subset of parameters required for the input image. Our model achieves a BD-Rate improvement over VVC of -16.85% on the Kodak dataset.

Jonas Brenig, Radu Timofte

Computer Vision Lab, CAIDAS & IFI, University of Würzburg, Germany

Learned Image Compression Data Compression Methods Experts Maestro Dataset

Aug 11, 2026cs.CV

A HamNoSys-Guided Dataset and Baselines for Fine-Grained Isolated Handshape Recognition in Sign Language

Purpose: Fine-grained handshape recognition supports computational sign-language transcription, recognition, and translation, but broad, phonetically defined visual inventories with signer-aware evaluation remain limited. This work introduces a benchmark grounded in the language-independent Hamburg Notation System (HamNoSys). Methods: A balanced dataset of 144,000 RGB images was collected from 15 participants for 160 handshape classes defined by the official HamNoSys 4 Handshapes Chart. ResNet-18 and ViT-B/16 were evaluated as appearance-based models, while a graph convolutional network and XGBoost were evaluated from hand landmarks. Both a class-stratified subject-dependent split and a 15-fold leave-one-subject-out (LOSO) protocol were used. The same model families were additionally assessed on LSWH100 and ASL Fingerspelling Dataset A for external context. Results: The subject-dependent benchmarks established reproducible reference performance across all four model families, whereas LOSO evaluation exposed a substantial reduction when recognition was required to generalise to unseen participants. On ASL Fingerspelling Dataset A, mean LOSO top-1 accuracy ranged from 82.20% to 87.40%. Conclusion: The documented acquisition, curation, and complementary evaluation protocols pro-vide a reproducible resource for fine-grained isolated-handshape research and for developing more accessible sign-language technologies.

Ushnish Sarkar, Suvajit Patra, Bhaswar Chattopadhyay +2

Computer and Informatics Group, Variable Energy Cyclotron Centre, Kolkata, 700064, India. · Homi Bhabha National Institute, Mumbai, 400094, India. · Ramakrishna Mission Vivekananda Educational and Research Institute, Belur, 711202, India.

Sign Language Translation Hand Gesture Recognition Maestro Dataset Convolutional Neural Networks

Jul 25, 2026cs.CL

BERT-based Models vs. Large Language Models for Low-Resource Named Entity Recognition: A Comparative Study on Marathi

Named Entity Recognition (NER) for low-resource languages such as Marathi remains a challenging task due to limited annotated resources and linguistic complexity. Although recent Large Language Models (LLMs) have demonstrated strong performance across a wide range of natural language processing tasks, their effectiveness for language-specific NER in low-resource settings remains uncertain. In this study, we fine-tune MahaBERT-v2 on different variants of the MahaNER dataset and systematically compare the performance of these models with an existing MahaNER baseline and prominent general-purpose LLMs, including Gemini, LLaMA-3.3-70B, and Gemma models. All models are evaluated on a Marathi NER test dataset using standard metrics of precision, recall, and F1-score. The experimental results show that the fine-tuned MahaBERT-based models consistently outperform both the baseline and all evaluated LLMs, with the fine-tuned models achieving F1-scores ranging from 0.88 to 0.91, surpassing the existing MahaNER model (0.8843) and significantly exceeding the performance of LLM-based approaches, whose F1-scores range from 0.57 to 0.69. These findings demonstrate that task-specific, language-focused models trained on domain-relevant data remain more effective than general-purpose LLMs for Marathi NER, highlighting the continued importance of specialized architectures for low-resource language processing.

Hariom Ingle, Ronit Ghode, Ishwari Gondkar +2

Department of Information Technology, PICT, Pune, India · L3Cube Labs, Pune, India · Indian Institute of Technology Madras, Chennai, India

Named-Entity Recognition Bert-Based Models Hindi Maestro Dataset

Jun 30, 2026cs.CV

Towards a foundational model for recognising diastematic Gregorian notation

Optical recognition of Gregorian notation has recently been attempted with end-to-end methods, with four datasets introduced. However, each of these datasets is in a different encoding. We design a common encoding based on the S-GABC proposal, convert all four datasets to this common encoding, and train a shared end-to-end foundational model for diastematic Gregorian notation that establishes a new state of the art across all four datasets.

Daniel Kurek, Jan Hajič

Institute of Formal and Applied Linguistics, Charles University, Prague, Czechia

Passage Encoding Maestro Dataset Proposal

Jun 26, 2026cs.LG

A Comparison of Fusion Techniques for Multi-Modal Human Activity Recognition on the HARMES Dataset

Recent advances in Human Activity Recognition (HAR) from wearable sensors have shown that multi-modal deep learning models consistently outperform their uni-modal counterparts. Modalities can include IMUs, RGB cameras, audio signals, and others. One important aspect of multi-modal deep learning is the sensor fusion approach we apply. Over recent years, multiple fusion paradigms have been proposed for multi-modal HAR. However, to the best of our knowledge, no head-to-head comparison of these paradigms exists on a common multi-modal HAR benchmark dataset. To address this research gap, we systematically compare seven state-of-the-art sensor fusion methods on the recently released HARMES dataset, which comprises 61 hours of fully labeled IMU, audio, and ambient humidity data. The chosen dataset focuses on 15 household and personal hygiene activities of daily living (ADLs). By applying the seven different fusion techniques to a state-of-the-art multi-modal model architecture, we show that Gated Multi-modal Fusion achieves the highest macro F1-score (0.82), surpassing the concatenation-based late fusion HARMES paper baseline of 0.76 by +6pp under leave-one-participant-out evaluation. All code used in our experiments is made publicly available on GitHub.

Ahmed Mohamady, Robin Burchard, Kristof Van Laerhoven

University of Siegen, Germany

Multimodal Fusion Human Activity Recognition Multimodal Deep Learning Maestro Dataset

Jun 23, 2026cs.CV

TrOCR for Medieval HTR: A Systematic Ablation Study with Cross-Dataset Validation

Fine-tuning transformer-based handwritten text recognition (HTR) models on medieval manuscripts is challenging because these models are pre-trained on modern text and must adapt to a very different visual domain. This paper studies how three controllable fine-tuning choices (contrast normalization, data augmentation, and layer freezing) affect recognition accuracy when adapting TrOCR to small historical datasets. We run controlled experiments on a 13th-century Italian manuscript (I-CT 91 "Cortonese") and replicate the same experimental grid on the public READ-16 benchmark as robustness evidence. On Cortonese, our best configuration achieves 8.03% character error rate (CER). Statistical comparisons across 13 configurations show that freezing up to three encoder layers or six decoder layers does not significantly harm accuracy, while deeper freezing becomes progressively detrimental. Removing contrast normalization (CLAHE) yields 7.84% CER, comparable to a domain-specialized baseline, suggesting strong optimization can reduce reliance on image preprocessing. Cross-dataset validation on READ-16 shows that decoder freezing thresholds transfer more robustly than encoder thresholds, and combined freezing strategies require dataset-specific re-validation. Finally, we use Grad-CAM gradient attributions and decoder cross-attention maps to diagnose error patterns and failure modes revealed by the ablations. Source code is available at https://github.com/LaudareProject/TrOCR-analysis

Sachin Sharma, Michele Flammini, Federico Simonetta

GSSI – Gran Sasso Science Institute L’Aquila, Italy

Handwritten Text Recognition Historical Manuscripts Maestro Dataset Frozen Backbone Character Error Rate Ablation+2

Jun 19, 2026cs.LG

RocketPFN: Accurate Time Series Classification via In-Context Learning

We introduce RocketPFN, a training-free pipeline for time series classification that combines random convolutional feature extraction (Rocket) with in-context classification via a pretrained tabular foundation model (TabPFN v2.5). On 92 UCR datasets (30-resample protocol), RocketPFN matches HC2, the strongest published method on the archive, in mean accuracy (both 0.900, Wilcoxon p=0.50), with no training on the target data and a median inference time of 30 seconds per fold. It also significantly outperforms every individual classifier in the HC2 ensemble. On UEA (20 datasets) the difference is likewise not statistically significant. A separate comparison concerns TSC foundation models: when paired with the same downstream classifier, MOMENT, Mantis, and MantisV2 are all significantly outperformed by RocketPFN using fewer extracted features and no learned parameters (p<0.001 in each case). This holds even when the encoders were pretrained on corpora that include the UCR training samples. We propose this two-stage pipeline as a reference point for evaluating zero-shot TSC foundation models.

Franco Martino O'Rourke, Ana Trisovic, Dimitris Bertsimas

Time-Series Classification Time Series Maestro Dataset In-Context Learning

Jun 7, 2026cs.RO

Personalized and Robust Proactive Robot Assistance with Uncertainty-Guided LLM Reasoning

Proactive robot assistance in household environments requires accurate prediction of human activities and object usage under dynamic and noisy conditions. Existing approaches often rely on complex spatio-temporal models, which can be computationally expensive and sensitive to environmental variability. In this paper, we propose GLOBE, a lightweight framework that combines n-gram Markov models for capturing temporal behavioral patterns with uncertainty-guided large language model (LLM) reasoning. The framework performs sequential prediction efficiently while selectively invoking LLM reasoning only when the model confidence is low. To evaluate performance under realistic conditions, we introduce HOMER-Noise, a noisy extension of the HOMER+ dataset that simulates structured disturbances such as object movements caused by humans, pets, and toddlers. Experimental results show that GLOBE achieves competitive performance with state-of-the-art methods while improving robustness and computational efficiency across both clean and noisy settings. The framework is further validated through a proof-of-concept integration with a Stretch 3 mobile manipulator, demonstrating its potential application in real-world human-robot interaction scenarios.

Alvaro Gonzalez, M. H. Hasan Shovo, Ali Ayub

Concordia University, Montreal, Quebec H3G 1M8, Canada

Human-Robot Interaction Large Language Model Uncertainty LLM Reasoning Strategies Maestro Dataset Personalization

May 27, 2026cs.CL

Evaluating the Realism of LLM-powered Social Agents: A Case Study of Reactions to Spanish Online News

LLM-powered social agents are increasingly used to simulate online social behavior, yet their realism remains difficult to validate. Existing work has largely relied on general-purpose benchmarks, while less attention has been paid to short, reactive discourse such as audience replies to online news. In this paper, we evaluate whether LLM-generated reactions to Spanish online news reproduce measurable properties of real audience discourse. Using the Hatemedia dataset, we pair 5,631 news items with 58,555 real audience reactions, and generate a matched synthetic dataset using five LLMs under a shared experimental setting. We compare real and synthetic reactions across three dimensions: hate speech, sentiment, and semantic alignment, considering both off-the-shelf and fine-tuned generation. Results show that off-the-shelf models are poor proxies for real audience reactions: they strongly underproduce hate speech, introduce model-specific sentiment biases, and remain distributionally distant from human replies. Fine-tuning improves fidelity unevenly. Qwen3 provides the most balanced approximation, while Mistral7B achieves the strongest sentiment and semantic alignment but overshoots hate prevalence. Plausible synthetic replies do not necessarily reproduce the distributional properties of public discourse.

Alejandro Buitrago López, Alberto Ortega Pastor, Javier Pastor-Galindo +1

Faculty of Computer Science, University of Murcia, Murcia (Spain)

Scaling Improve Social Simulation Spanish Maestro Dataset Reaction

May 5, 2026cs.LG

HUGO-CS: A Hybrid-Labeled, Uncertainty-Aware, General-Purpose, Observational Dataset for Cold Spray

Cold spraying is an increasingly common approach for repairing and manufacturing components due to its solid-state manufacturing capabilities. However, process optimization remains difficult due to many interdependent parameters and the lack of large-scale, machine-readable data to support modeling. While the scientific literature contains many relevant experiments, results are inconsistently reported (often in tables and figures) and use non-uniform units, limiting utilization at scale. To address these limitations, this work presents HUGO-CS, a literature-derived dataset of 4,383 cold-spray experiments with 144 features from 1,124 sources, exceeding the previous largest dataset (137 samples) by 30x. With completely manual extraction requiring an average of 91 minutes per document, this work designs and leverages a Hybrid-labeled, Uncertainty-aware, General-purpose, Observational extraction framework, called HUGO, to support this extraction. HUGO combines automated LLM-based labeling with targeted manual label refinement to handle this experimental result extraction process from scientific literature. To balance labeling efficiency with extraction accuracy, HUGO introduces a Hierarchical Risk Mitigation (HRM) to route LLM outputs with a high risk of potential errors for manual review, while retaining low-risk records as auto-labeled. Lastly, HUGO post-processing consolidates categorical descriptors, maps reported feedstock chemistries into structured continuous compositions, and normalizes units across sources. Of the 4,383 reported experiments, 1,765 are hand-labeled, providing a high-quality labeled subset for benchmarking, error analysis, and higher-fidelity data points. All code to replicate this work, along with the complete HUGO-CS dataset, are released under a CC-BY license at https://github.com/sprice134/HUGO.

Stephen Price, Kyle Miller, Marco Musto +5

Computer Science Department, Worcester Polytechnic Institute, Worcester, MA, USA · Citrine Informatics, Redwood City, CA, USA · Materials and Manufacturing Department, Worcester Polytechnic Institute, Worcester, MA, USA +1

Maestro Dataset Extraction Freeze

Apr 24, 2026astro-ph.IM

Thermal background reduction for mid-infrared imaging by low-rank background and sparse point-source modelling

Mid-infrared astronomy from the ground faces critical challenges in accurately detecting and quantifying sources due to the dominant spatially and time-variable background noise. Moreover, chopping and nodding, the traditional methods for dealing with these background issues, will not be technically feasible on the next generation of extremely large telescopes. This limitation requires the development of novel computational methods for a robust background reduction. We present and evaluate a novel method named LOw-RAnk Background ELimination (LORABEL) to improve the sensitivity of mid-infrared astronomical observations, without the need for classical telescope nodding, source masking, or other overheads in observing time. We applied a low-rank background-reduction strategy to (1) data taken on the ground with the VISIR with synthetically injected sources, and (2) airborne data from SOFIA. We compared the performance of our new method to classical chopping and nodding techniques, and analysed the effect on source photometry and detection precision for different observational scenarios. In regimes with a low signal-to-noise ratio (S/N

<5

) in the ground-based VISIR data, LORABEL reduces variation in the photometric error with respect to chopping differences alone and even the classical chop-nod sequence, at the cost of introducing a bias. Secondly, we demonstrate that LORABEL increases detection precision in comparison to traditional background-reduction methods. For the SOFIA dataset, we achieve a

20-100

fold decrease in mean background flux with respect to the traditional chop-nod method while preserving most of the source flux. Our findings suggest that LORABEL is applicable to a wider range of instrumental observation, that is, both ground-based and airborne, and it is a suitable tool in the context of faint-source detection.

R. A. R. Moens, A. G. M. Pietrow, B. Brandl +1

Delft Center for Systems and Control, Delft University of Technology, Mekelweg 2, 2628 CD Delft, Netherlands · Leibniz-Institut für Astrophysik Potsdam (AIP), An der Sternwarte 16, 14482 Potsdam, Germany. · Centre for Mathematical Plasma Astrophysics, KU Leuven, Celestijnenlaan 200B, B-3001 Leuven, Belgium. +4

Infrared Canada-France-Hawaii Telescope Data Low-Rank Structure Maestro Dataset

Apr 23, 2026cs.CL

Listen and Chant Before You Read: The Ladder of Beauty in LM Pre-Training

We show that pre-training a Transformer on music before language significantly accelerates language acquisition. Using piano performances (MAESTRO dataset), a developmental pipeline -- music

\to

poetry

\to

prose -- yields a

17.5\%

perplexity improvement over random initialization (

p < 0.001

, 5 seeds), with music and poetry improving orthogonal model components (internal computation and embeddings, respectively). Convergence tests confirm that this is not a transient head start: at

d\!=\!64

, multi-seed validation (5 seeds) shows a persistent 5.5% gap at plateau (

p = 0.017

), with the pipeline converging faster and to a lower loss in every run. Real music matches the transfer ceiling of synthetic patterns with one-third the data, and scaling experiments reveal that optimal pre-training data volume shifts with model capacity (

-3\% \to +3\% \to +6\%

advantage of larger datasets from

d\!=\!16

to

d\!=\!64

). Across the scales we study (

d\!\in\!\{16,32,64\}

, up to

{\sim}400

K parameters), these results suggest a capacity-dependent data curation principle and indicate that structured human creative outputs can provide an efficient pre-training substrate for small language models; stronger conclusions at modern pre-training scale will require substantially larger experiments.

Yoshinori Nomura

Mirage Mountain Technologies Inc.

Large Language Model Pretraining Pretraining Text-To-Music Language Acquisition Maestro Dataset Ladder+1

Feb 19, 2026stat.ME

Sparse Bayesian Modeling of EEG Channel Interactions Improves P300 Brain-Computer Interface Performance

Electroencephalography (EEG)-based P300 brain-computer interfaces (BCIs) enable communication without physical movement by detecting stimulus-evoked neural responses. Accurate and efficient decoding remains challenging due to high dimensionality, temporal dependence, and complex interactions across EEG channels. Existing approaches often treat channels independently or rely on black-box models, limiting interpretability and personalization. We propose a sparse Bayesian time-varying regression framework that explicitly models pairwise EEG channel interactions while performing temporal feature selection, where a relaxed-thresholded Gaussian process prior induces structured sparsity in both channel-specific and interaction effects, enabling interpretable identification of task-relevant channels and channel pairs. Applied to a public P300 speller dataset of 55 participants, our method achieves a 96.4% median character-level accuracy using all sequence repetitions and attains the best overall decoding performance among competing statistical and deep learning approaches. Incorporating channel interactions yields subgroup-specific gains in character-level accuracy, particularly among participants abstained from alcohol (median 7%, up to 14%). Importantly, our approach improves median BCI-Utility by more than 10% at its optimal operating point, achieving peak throughput after only six sequence repetitions. These results demonstrate that explicitly modeling structured EEG channel interactions within a principled Bayesian framework enhances accuracy and user-centric throughput, and supports personalization in P300 BCI systems.

Guoxuan Ma, Yuan Zhong, Moyan Li +2

Department of Biostatistics, University of Michigan, Ann Arbor MI 48109

Brain-Computer Interface Electroencephalography Bayesian Networks Maestro Dataset Task-Relevant Information

Dec 17, 2025cs.CV

GRAN-TED: Generating Robust, Aligned, and Nuanced Text Embedding for Diffusion Models

The text encoder is a critical component of text-to-image and text-to-video diffusion models, fundamentally determining the semantic fidelity of the generated content. However, its development has been hindered by two major challenges: the lack of an efficient evaluation framework that reliably predicts downstream generation performance, and the difficulty of effectively adapting pretrained language models for visual synthesis. To address these issues, we introduce GRAN-TED, a paradigm to Generate Robust, Aligned, and Nuanced Text Embeddings for Diffusion models. Our contribution is twofold. First, we propose TED-6K, a novel text-only benchmark that enables efficient and robust assessment of an encoder's representational quality without requiring costly end-to-end model training. We demonstrate that performance on TED-6K, standardized via a lightweight, unified adapter, strongly correlates with an encoder's effectiveness in downstream generation tasks. Notably, under our experimental setup, compared with training a diffusion model from scratch, evaluating with TED-6K is about \textbf{750

\times

faster}. Second, guided by this validated framework, we develop a superior text encoder using a novel two-stage training paradigm. This process involves an initial fine-tuning stage on a Multimodal Large Language Model for better visual representation, followed by a layer-wise weighting method to extract more nuanced and potent text features. Our experiments show that the resulting GRAN-TED encoder not only achieves state-of-the-art performance on TED-6K but also leads to demonstrable performance gains in text-to-image and text-to-video generation. Our TED-6K dataset and evaluation code are available at the following link: https://anonymous.4open.science/r/GRAN-TED-4FCC/.

Bozhou Li, Sihan Yang, Yushuo Guan +6

Peking University · Kling Team, Kuaishou Technology · Xi’an Jiaotong University +1

Text-To-Video Diffusion Models Text-To-Image Diffusion Models Encoders Text Analysis and Detection Maestro Dataset