pwc-archive/papers-with-abstracts huggingface dataset:
Integrating Vision and Location with Transformers: A Multimodal Deep Learning Framework for Medical Wound Analysis
Authors: Ramin Mousa, Hadis Taherinia, Khabiba Abdiyeva, Amir Ali Bengari, Mohammadmahdi Vahediahmar
Date: 2025-04-14 00:00:00
Tasks: Diagnostic, feature selection, Multimodal Deep Learning

Effective recognition of acute and difficult-to-heal wounds is a necessary step in wound diagnosis. An efficient classification model can help wound specialists classify wound types with less financial and time costs and also help in deciding on the optimal treatment method. Traditional machine learning models suffer from feature selection and are usually cumbersome models for accurate recognition. Recently, deep learning (DL) has emerged as a powerful tool in wound diagnosis. Although DL seems promising for wound type recognition, there is still a large scope for improving the efficiency and accuracy of the model. In this study, a DL-based multimodal classifier was developed using wound images and their corresponding locations to classify them into multiple classes, including diabetic, pressure, surgical, and venous ulcers. A body map was also created to provide location data, which can help wound specialists label wound locations more effectively. The model uses a Vision Transformer to extract hierarchical features from input images, a Discrete Wavelet Transform (DWT) layer to capture low and high frequency components, and a Transformer to extract spatial features. The number of neurons and weight vector optimization were performed using three swarm-based optimization techniques (Monster Gorilla Toner (MGTO), Improved Gray Wolf Optimization (IGWO), and Fox Optimization Algorithm). The evaluation results show that weight vector optimization using optimization algorithms can increase diagnostic accuracy and make it a very effective approach for wound detection. In the classification using the original body map, the proposed model was able to achieve an accuracy of 0.8123 using image data and an accuracy of 0.8007 using a combination of image data and wound location. Also, the accuracy of the model in combination with the optimization models varied from 0.7801 to 0.8342.
Can We Edit LLMs for Long-Tail Biomedical Knowledge?
Authors: Xinhao Yi, Jake Lever, Kevin Bryson, Zaiqiao Meng
Date: 2025-04-14 00:00:00
Tasks: knowledge editing

Knowledge editing has emerged as an effective approach for updating large language models (LLMs) by modifying their internal knowledge. However, their application to the biomedical domain faces unique challenges due to the long-tailed distribution of biomedical knowledge, where rare and infrequent information is prevalent. In this paper, we conduct the first comprehensive study to investigate the effectiveness of knowledge editing methods for editing long-tail biomedical knowledge. Our results indicate that, while existing editing methods can enhance LLMs' performance on long-tail biomedical knowledge, their performance on long-tail knowledge remains inferior to that on high-frequency popular knowledge, even after editing. Our further analysis reveals that long-tail biomedical knowledge contains a significant amount of one-to-many knowledge, where one subject and relation link to multiple objects. This high prevalence of one-to-many knowledge limits the effectiveness of knowledge editing in improving LLMs' understanding of long-tail biomedical knowledge, highlighting the need for tailored strategies to bridge this performance gap.
HUMOTO: A 4D Dataset of Mocap Human Object Interactions
Authors: Jiaxin Lu, Chun-Hao Paul Huang, Uttaran Bhattacharya, QiXing Huang, Yi Zhou
Date: 2025-04-14 00:00:00
Tasks: Human-Object Interaction Detection, Motion Generation, Object

We present Human Motions with Objects (HUMOTO), a high-fidelity dataset of human-object interactions for motion generation, computer vision, and robotics applications. Featuring 736 sequences (7,875 seconds at 30 fps), HUMOTO captures interactions with 63 precisely modeled objects and 72 articulated parts. Our innovations include a scene-driven LLM scripting pipeline creating complete, purposeful tasks with natural progression, and a mocap-and-camera recording setup to effectively handle occlusions. Spanning diverse activities from cooking to outdoor picnics, HUMOTO preserves both physical accuracy and logical task flow. Professional artists rigorously clean and verify each sequence, minimizing foot sliding and object penetrations. We also provide benchmarks compared to other datasets. HUMOTO's comprehensive full-body motion and simultaneous multi-object interactions address key data-capturing challenges and provide opportunities to advance realistic human-object interaction modeling across research domains with practical applications in animation, robotics, and embodied AI systems. Project: https://jiaxin-lu.github.io/humoto/ .
Multimodal Representation Learning Techniques for Comprehensive Facial State Analysis
Authors: Kaiwen Zheng, Xuri Ge, Junchen Fu, Jun Peng, Joemon M. Jose
Date: 2025-04-14 00:00:00
Tasks: Emotion Recognition, Representation Learning

Multimodal foundation models have significantly improved feature representation by integrating information from multiple modalities, making them highly suitable for a broader set of applications. However, the exploration of multimodal facial representation for understanding perception has been limited. Understanding and analyzing facial states, such as Action Units (AUs) and emotions, require a comprehensive and robust framework that bridges visual and linguistic modalities. In this paper, we present a comprehensive pipeline for multimodal facial state analysis. First, we compile a new Multimodal Face Dataset (MFA) by generating detailed multilevel language descriptions of face, incorporating Action Unit (AU) and emotion descriptions, by leveraging GPT-4o. Second, we introduce a novel Multilevel Multimodal Face Foundation model (MF^2) tailored for Action Unit (AU) and emotion recognition. Our model incorporates comprehensive visual feature modeling at both local and global levels of face image, enhancing its ability to represent detailed facial appearances. This design aligns visual representations with structured AU and emotion descriptions, ensuring effective cross-modal integration. Third, we develop a Decoupled Fine-Tuning Network (DFN) that efficiently adapts MF^2 across various tasks and datasets. This approach not only reduces computational overhead but also broadens the applicability of the foundation model to diverse scenarios. Experimentation show superior performance for AU and emotion detection tasks.
Benchmarking 3D Human Pose Estimation Models Under Occlusions
Authors: Filipa Lino, Carlos Santiago, Manuel Marques
Date: 2025-04-14 00:00:00
Tasks: 3D Human Pose Estimation, Benchmarking, Pose Estimation, Sensitivity

This paper addresses critical challenges in 3D Human Pose Estimation (HPE) by analyzing the robustness and sensitivity of existing models to occlusions, camera position, and action variability. Using a novel synthetic dataset, BlendMimic3D, which includes diverse scenarios with multi-camera setups and several occlusion types, we conduct specific tests on several state-of-the-art models. Our study focuses on the discrepancy in keypoint formats between common datasets such as Human3.6M, and 2D datasets such as COCO, commonly used for 2D detection models and frequently input of 3D HPE models. Our work explores the impact of occlusions on model performance and the generality of models trained exclusively under standard conditions. The findings suggest significant sensitivity to occlusions and camera settings, revealing a need for models that better adapt to real-world variability and occlusion scenarios. This research contributed to ongoing efforts to improve the fidelity and applicability of 3D HPE systems in complex environments.
Heimdall: test-time scaling on the generative verification
Authors: Wenlei Shi, Xing Jin
Date: 2025-04-14 00:00:00
Tasks: Math

An AI system can create and maintain knowledge only to the extent that it can verify that knowledge itself. Recent work on long Chain-of-Thought reasoning has demonstrated great potential of LLMs on solving competitive problems, but their verification ability remains to be weak and not sufficiently investigated. In this paper, we propose Heimdall, the long CoT verification LLM that can accurately judge the correctness of solutions. With pure reinforcement learning, we boost the verification accuracy from 62.5% to 94.5% on competitive math problems. By scaling with repeated sampling, the accuracy further increases to 97.5%. Through human evaluation, Heimdall demonstrates impressive generalization capabilities, successfully detecting most issues in challenging math proofs, the type of which is not included during training. Furthermore, we propose Pessimistic Verification to extend the functionality of Heimdall to scaling up the problem solving. It calls Heimdall to judge the solutions from a solver model and based on the pessimistic principle, selects the most likely correct solution with the least uncertainty. Taking DeepSeek-R1-Distill-Qwen-32B as the solver model, Pessimistic Verification improves the solution accuracy on AIME2025 from 54.2% to 70.0% with 16x compute budget and to 83.3% with more compute budget. With the stronger solver Gemini 2.5 Pro, the score reaches 93.0%. Finally, we prototype an automatic knowledge discovery system, a ternary system where one poses questions, another provides solutions, and the third verifies the solutions. Using the data synthesis work NuminaMath for the first two components, Heimdall effectively identifies problematic records within the dataset and reveals that nearly half of the data is flawed, which interestingly aligns with the recent ablation studies from NuminaMath.
Towards Low-Latency Event-based Obstacle Avoidance on a FPGA-Drone
Authors: Pietro Bonazzi, Christian Vogt, Michael Jost, Lyes Khacef, Federico Paredes-Vallés, Michele Magno
Date: 2025-04-14 00:00:00
Tasks: Collision Avoidance, Event-based vision

This work quantitatively evaluates the performance of event-based vision systems (EVS) against conventional RGB-based models for action prediction in collision avoidance on an FPGA accelerator. Our experiments demonstrate that the EVS model achieves a significantly higher effective frame rate (1 kHz) and lower temporal (-20 ms) and spatial prediction errors (-20 mm) compared to the RGB-based model, particularly when tested on out-of-distribution data. The EVS model also exhibits superior robustness in selecting optimal evasion maneuvers. In particular, in distinguishing between movement and stationary states, it achieves a 59 percentage point advantage in precision (78% vs. 19%) and a substantially higher F1 score (0.73 vs. 0.06), highlighting the susceptibility of the RGB model to overfitting. Further analysis in different combinations of spatial classes confirms the consistent performance of the EVS model in both test data sets. Finally, we evaluated the system end-to-end and achieved a latency of approximately 2.14 ms, with event aggregation (1 ms) and inference on the processing unit (0.94 ms) accounting for the largest components. These results underscore the advantages of event-based vision for real-time collision avoidance and demonstrate its potential for deployment in resource-constrained environments.
DiffMOD: Progressive Diffusion Point Denoising for Moving Object Detection in Remote Sensing
Authors: Jinyue Zhang, Xiangrong Zhang, Zhongjian Huang, Tianyang Zhang, Yifei Jiang, Licheng Jiao
Date: 2025-04-14 00:00:00
Tasks: Denoising, Density Estimation, Moving Object Detection, Object, object-detection, Object Detection

Moving object detection (MOD) in remote sensing is significantly challenged by low resolution, extremely small object sizes, and complex noise interference. Current deep learning-based MOD methods rely on probability density estimation, which restricts flexible information interaction between objects and across temporal frames. To flexibly capture high-order inter-object and temporal relationships, we propose a point-based MOD in remote sensing. Inspired by diffusion models, the network optimization is formulated as a progressive denoising process that iteratively recovers moving object centers from sparse noisy points. Specifically, we sample scattered features from the backbone outputs as atomic units for subsequent processing, while global feature embeddings are aggregated to compensate for the limited coverage of sparse point features. By modeling spatial relative positions and semantic affinities, Spatial Relation Aggregation Attention is designed to enable high-order interactions among point-level features for enhanced object representation. To enhance temporal consistency, the Temporal Propagation and Global Fusion module is designed, which leverages an implicit memory reasoning mechanism for robust cross-frame feature integration. To align with the progressive denoising process, we propose a progressive MinK optimal transport assignment strategy that establishes specialized learning objectives at each denoising level. Additionally, we introduce a missing loss function to counteract the clustering tendency of denoised points around salient objects. Experiments on the RsData remote sensing MOD dataset show that our MOD method based on scattered point denoising can more effectively explore potential relationships between sparse moving objects and improve the detection capability and temporal consistency.
RealWebAssist: A Benchmark for Long-Horizon Web Assistance with Real-World Users
Authors: Suyu Ye, Haojun Shi, Darren Shih, Hyokun Yun, Tanya Roosta, Tianmin Shu
Date: 2025-04-14 00:00:00
Tasks: Instruction Following

To achieve successful assistance with long-horizon web-based tasks, AI agents must be able to sequentially follow real-world user instructions over a long period. Unlike existing web-based agent benchmarks, sequential instruction following in the real world poses significant challenges beyond performing a single, clearly defined task. For instance, real-world human instructions can be ambiguous, require different levels of AI assistance, and may evolve over time, reflecting changes in the user's mental state. To address this gap, we introduce RealWebAssist, a novel benchmark designed to evaluate sequential instruction-following in realistic scenarios involving long-horizon interactions with the web, visual GUI grounding, and understanding ambiguous real-world user instructions. RealWebAssist includes a dataset of sequential instructions collected from real-world human users. Each user instructs a web-based assistant to perform a series of tasks on multiple websites. A successful agent must reason about the true intent behind each instruction, keep track of the mental state of the user, understand user-specific routines, and ground the intended tasks to actions on the correct GUI elements. Our experimental results show that state-of-the-art models struggle to understand and ground user instructions, posing critical challenges in following real-world user instructions for long-horizon web assistance.
$α$-Flow: A Unified Framework for Continuous-State Discrete Flow Matching Models
Authors: Chaoran Cheng, Jiahan Li, Jiajun Fan, Ge Liu
Date: 2025-04-14 00:00:00
Tasks: Language Modeling, Language Modelling

Recent efforts have extended the flow-matching framework to discrete generative modeling. One strand of models directly works with the continuous probabilities instead of discrete tokens, which we colloquially refer to as Continuous-State Discrete Flow Matching (CS-DFM). Existing CS-DFM models differ significantly in their representations and geometric assumptions. This work presents a unified framework for CS-DFM models, under which the existing variants can be understood as operating on different $\alpha$-representations of probabilities. Building upon the theory of information geometry, we introduce $\alpha$-Flow, a family of CS-DFM models that adheres to the canonical $\alpha$-geometry of the statistical manifold, and demonstrate its optimality in minimizing the generalized kinetic energy. Theoretically, we show that the flow matching loss for $\alpha$-flow establishes a unified variational bound for the discrete negative log-likelihood. We comprehensively evaluate different instantiations of $\alpha$-flow on various discrete generation domains to demonstrate their effectiveness in discrete generative modeling, including intermediate values whose geometries have never been explored before. $\alpha$-flow significantly outperforms its discrete-state counterpart in image and protein sequence generation and better captures the entropy in language modeling.
Patch and Shuffle: A Preprocessing Technique for Texture Classification in Autonomous Cementitious Fabrication
Authors: Jeremiah Giordani
Date: 2025-04-14 00:00:00
Tasks: Classification, Texture Classification

Autonomous fabrication systems are transforming construction and manufacturing, yet they remain vulnerable to print errors. Texture classification is a key component of computer vision systems that enable real-time monitoring and adjustment during cementitious fabrication. Traditional classification methods often rely on global image features, which can bias the model toward semantic content rather than low-level textures. In this paper, we introduce a novel preprocessing technique called "patch and shuffle," which segments input images into smaller patches, shuffles them, and reconstructs a jumbled image before classification. This transformation removes semantic context, forcing the classifier to rely on local texture features. We evaluate this approach on a dataset of extruded cement images, using a ResNet-18-based architecture. Our experiments compare the patch and shuffle method to a standard pipeline, holding all other factors constant. Results show a significant improvement in accuracy: the patch and shuffle model achieved 90.64% test accuracy versus 72.46% for the baseline. These findings suggest that disrupting global structure enhances performance in texture-based classification tasks. This method has implications for broader vision tasks where low-level features matter more than high-level semantics. The technique may improve classification in applications ranging from fabrication monitoring to medical imaging.
FLOSS: Free Lunch in Open-vocabulary Semantic Segmentation
Authors: Yasser Benigmim, Mohammad Fahes, Tuan-Hung Vu, Andrei Bursuc, Raoul de Charette
Date: 2025-04-14 00:00:00
Tasks: Open Vocabulary Semantic Segmentation, Open-Vocabulary Semantic Segmentation, Semantic Segmentation

Recent Open-Vocabulary Semantic Segmentation (OVSS) models extend the CLIP model to segmentation while maintaining the use of multiple templates (e.g., a photo of <class>, a sketch of a <class>, etc.) for constructing class-wise averaged text embeddings, acting as a classifier. In this paper, we challenge this status quo and investigate the impact of templates for OVSS. Empirically, we observe that for each class, there exist single-template classifiers significantly outperforming the conventional averaged classifier. We refer to them as class-experts. Given access to unlabeled images and without any training involved, we estimate these experts by leveraging the class-wise prediction entropy of single-template classifiers, selecting as class-wise experts those which yield the lowest entropy. All experts, each specializing in a specific class, collaborate in a newly proposed fusion method to generate more accurate OVSS predictions. Our plug-and-play method, coined FLOSS, is orthogonal and complementary to existing OVSS methods, offering a ''free lunch'' to systematically improve OVSS without labels and additional training. Extensive experiments demonstrate that FLOSS consistently boosts state-of-the-art methods on various OVSS benchmarks. Moreover, the selected expert templates can generalize well from one dataset to others sharing the same semantic categories, yet exhibiting distribution shifts. Additionally, we obtain satisfactory improvements under a low-data regime, where only a few unlabeled images are available. Our code is available at https://github.com/yasserben/FLOSS .
The Scalability of Simplicity: Empirical Analysis of Vision-Language Learning with a Single Transformer
Authors: Weixian Lei, Jiacong Wang, Haochen Wang, Xiangtai Li, Jun Hao Liew, Jiashi Feng, Zilong Huang
Date: 2025-04-14 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Multimodal Large Language Model, Semantic Segmentation

This paper introduces SAIL, a single transformer unified multimodal large language model (MLLM) that integrates raw pixel encoding and language decoding within a singular architecture. Unlike existing modular MLLMs, which rely on a pre-trained vision transformer (ViT), SAIL eliminates the need for a separate vision encoder, presenting a more minimalist architecture design. Instead of introducing novel architectural components, SAIL adapts mix-attention mechanisms and multimodal positional encodings to better align with the distinct characteristics of visual and textual modalities. We systematically compare SAIL's properties-including scalability, cross-modal information flow patterns, and visual representation capabilities-with those of modular MLLMs. By scaling both training data and model size, SAIL achieves performance comparable to modular MLLMs. Notably, the removal of pretrained ViT components enhances SAIL's scalability and results in significantly different cross-modal information flow patterns. Moreover, SAIL demonstrates strong visual representation capabilities, achieving results on par with ViT-22B in vision tasks such as semantic segmentation. Code and models are available at https://github.com/bytedance/SAIL.
MorphTok: Morphologically Grounded Tokenization for Indian Languages
Authors: Maharaj Brahma, N J Karthika, Atul Singh, Devaraj Adiga, Smruti Bhate, Ganesh Ramakrishnan, Rohit Saluja, Maunendra Sankar Desarkar
Date: 2025-04-14 00:00:00
Tasks: Language Modeling, Language Modelling, Machine Translation, Segmentation, Translation

Tokenization is a crucial step in NLP, especially with the rise of large language models (LLMs), impacting downstream performance, computational cost, and efficiency. Existing LLMs rely on the classical Byte-pair Encoding (BPE) algorithm for subword tokenization that greedily merges frequent character bigrams. This often leads to segmentation that does not align with linguistically meaningful units. To address this, we propose morphology-aware segmentation as a pre-tokenization step prior to applying BPE. To facilitate morphology-aware segmentation, we create a novel dataset for Hindi and Marathi, incorporating sandhi splitting to enhance the subword tokenization. Experiments on downstream tasks show that morphologically grounded tokenization improves performance for machine translation and language modeling. Additionally, to handle the ambiguity in the Unicode characters for diacritics, particularly dependent vowels in syllable-based writing systems, we introduce Constrained BPE (CBPE), an extension to the traditional BPE algorithm that incorporates script-specific constraints. Specifically, CBPE handles dependent vowels. Our results show that CBPE achieves a 1.68\% reduction in fertility scores while maintaining comparable or improved downstream performance in machine translation, offering a computationally efficient alternative to standard BPE. Moreover, to evaluate segmentation across different tokenization algorithms, we introduce a new human evaluation metric, \textit{EvalTok}, enabling more human-grounded assessment.