pwc-archive/papers-with-abstracts huggingface dataset:
Depth-Sensitive Soft Suppression with RGB-D Inter-Modal Stylization Flow for Domain Generalization Semantic Segmentation
Authors: Binbin Wei, Yuhang Zhang, Shishun Tian, Muxin Liao, Wei Li, Wenbin Zou
Date: 2025-05-11 00:00:00
Tasks: Domain Adaptation, Domain Generalization, Semantic Segmentation, Sensitivity, Unsupervised Domain Adaptation

Unsupervised Domain Adaptation (UDA) aims to align source and target domain distributions to close the domain gap, but still struggles with obtaining the target data. Fortunately, Domain Generalization (DG) excels without the need for any target data. Recent works expose that depth maps contribute to improved generalized performance in the UDA tasks, but they ignore the noise and holes in depth maps due to device and environmental factors, failing to sufficiently and effectively learn domain-invariant representation. Although high-sensitivity region suppression has shown promising results in learning domain-invariant features, existing methods cannot be directly applicable to depth maps due to their unique characteristics. Hence, we propose a novel framework, namely Depth-Sensitive Soft Suppression with RGB-D inter-modal stylization flow (DSSS), focusing on learning domain-invariant features from depth maps for the DG semantic segmentation. Specifically, we propose the RGB-D inter-modal stylization flow to generate stylized depth maps for sensitivity detection, cleverly utilizing RGB information as the stylization source. Then, a class-wise soft spatial sensitivity suppression is designed to identify and emphasize non-sensitive depth features that contain more domain-invariant information. Furthermore, an RGB-D soft alignment loss is proposed to ensure that the stylized depth maps only align part of the RGB features while still retaining the unique depth information. To our best knowledge, our DSSS framework is the first work to integrate RGB and Depth information in the multi-class DG semantic segmentation task. Extensive experiments over multiple backbone networks show that our framework achieves remarkable performance improvement.
Differentiable NMS via Sinkhorn Matching for End-to-End Fabric Defect Detection
Authors: Zhengyang Lu, Bingjie Lu, Weifan Wang, Feng Wang
Date: 2025-05-11 00:00:00
Tasks: Defect Detection, object-detection, Object Detection

Fabric defect detection confronts two fundamental challenges. First, conventional non-maximum suppression disrupts gradient flow, which hinders genuine end-to-end learning. Second, acquiring pixel-level annotations at industrial scale is prohibitively costly. Addressing these limitations, we propose a differentiable NMS framework for fabric defect detection that achieves superior localization precision through end-to-end optimization. We reformulate NMS as a differentiable bipartite matching problem solved through the Sinkhorn-Knopp algorithm, maintaining uninterrupted gradient flow throughout the network. This approach specifically targets the irregular morphologies and ambiguous boundaries of fabric defects by integrating proposal quality, feature similarity, and spatial relationships. Our entropy-constrained mask refinement mechanism further enhances localization precision through principled uncertainty modeling. Extensive experiments on the Tianchi fabric defect dataset demonstrate significant performance improvements over existing methods while maintaining real-time speeds suitable for industrial deployment. The framework exhibits remarkable adaptability across different architectures and generalizes effectively to general object detection tasks.
MarkMatch: Same-Hand Stuffing Detection
Authors: Fei Zhao, Runlin Zhang, Chengcui Zhang, Nitesh Saxena
Date: 2025-05-11 00:00:00
Tasks: Binary Classification, Contrastive Learning

We present MarkMatch, a retrieval system for detecting whether two paper ballot marks were filled by the same hand. Unlike the previous SOTA method BubbleSig, which used binary classification on isolated mark pairs, MarkMatch ranks stylistic similarity between a query mark and a mark in the database using contrastive learning. Our model is trained with a dense batch similarity matrix and a dual loss objective. Each sample is contrasted against many negatives within each batch, enabling the model to learn subtle handwriting difference and improve generalization under handwriting variation and visual noise, while diagonal supervision reinforces high confidence on true matches. The model achieves an F1 score of 0.943, surpassing BubbleSig's best performance. MarkMatch also integrates Segment Anything Model for flexible mark extraction via box- or point-based prompts. The system offers election auditors a practical tool for visual, non-biometric investigation of suspicious ballots.
A Vision-Language Foundation Model for Leaf Disease Identification
Authors: Khang Nguyen Quoc, Lan Le Thi Thu, Luyl-Da Quach
Date: 2025-05-11 00:00:00
Tasks: Contrastive Learning, image-classification, Image Classification, Image-text Retrieval, Text Retrieval

Leaf disease identification plays a pivotal role in smart agriculture. However, many existing studies still struggle to integrate image and textual modalities to compensate for each other's limitations. Furthermore, many of these approaches rely on pretraining with constrained datasets such as ImageNet, which lack domain-specific information. We propose SCOLD (Soft-target COntrastive learning for Leaf Disease identification), a context-aware vision-language foundation model tailored to address these challenges for agricultural tasks. SCOLD is developed using a diverse corpus of plant leaf images and corresponding symptom descriptions, comprising over 186,000 image-caption pairs aligned with 97 unique concepts. Through task-agnostic pretraining, SCOLD leverages contextual soft targets to mitigate overconfidence in contrastive learning by smoothing labels, thereby improving model generalization and robustness on fine-grained classification tasks. Experimental results demonstrate that SCOLD outperforms existing vision-language models such as OpenAI-CLIP-L, BioCLIP, and SigLIP2 across several benchmarks, including zero-shot and few-shot classification, image-text retrieval, and image classification, while maintaining a competitive parameter footprint. Ablation studies further highlight SCOLD's effectiveness in contrast to its counterparts. The proposed approach significantly advances the agricultural vision-language foundation model, offering strong performance with minimal or no supervised fine-tuning. This work lays a solid groundwork for future research on models trained with long-form and simplified contexts, tasks involving class ambiguity, and multi-modal systems for intelligent plant disease diagnostics. The code for this study is available at https://huggingface.co/enalis/scold
MELLM: Exploring LLM-Powered Micro-Expression Understanding Enhanced by Subtle Motion Perception
Authors: Zhengye Zhang, Sirui Zhao, Shifeng Liu, Shukang Yin, Xinglong Mao, Tong Xu, Enhong Chen
Date: 2025-05-11 00:00:00
Tasks: Emotion Classification, Large Language Model, Micro Expression Recognition, Micro-Expression Recognition, Optical Flow Estimation, Text Generation

Micro-expressions (MEs) are crucial psychological responses with significant potential for affective computing. However, current automatic micro-expression recognition (MER) research primarily focuses on discrete emotion classification, neglecting a convincing analysis of the subtle dynamic movements and inherent emotional cues. The rapid progress in multimodal large language models (MLLMs), known for their strong multimodal comprehension and language generation abilities, offers new possibilities. MLLMs have shown success in various vision-language tasks, indicating their potential to understand MEs comprehensively, including both fine-grained motion patterns and underlying emotional semantics. Nevertheless, challenges remain due to the subtle intensity and short duration of MEs, as existing MLLMs are not designed to capture such delicate frame-level facial dynamics. In this paper, we propose a novel Micro-Expression Large Language Model (MELLM), which incorporates a subtle facial motion perception strategy with the strong inference capabilities of MLLMs, representing the first exploration of MLLMs in the domain of ME analysis. Specifically, to explicitly guide the MLLM toward motion-sensitive regions, we construct an interpretable motion-enhanced color map by fusing onset-apex optical flow dynamics with the corresponding grayscale onset frame as the model input. Additionally, specialized fine-tuning strategies are incorporated to further enhance the model's visual perception of MEs. Furthermore, we construct an instruction-description dataset based on Facial Action Coding System (FACS) annotations and emotion labels to train our MELLM. Comprehensive evaluations across multiple benchmark datasets demonstrate that our model exhibits superior robustness and generalization capabilities in ME understanding (MEU). Code is available at https://github.com/zyzhangUstc/MELLM.
CMD: Controllable Multiview Diffusion for 3D Editing and Progressive Generation
Authors: Peng Li, Suizhi Ma, Jialiang Chen, YuAn Liu, Chongyi Zhang, Wei Xue, Wenhan Luo, Alla Sheffer, Wenping Wang, Yike Guo
Date: 2025-05-11 00:00:00
Tasks: 3D Generation

Recently, 3D generation methods have shown their powerful ability to automate 3D model creation. However, most 3D generation methods only rely on an input image or a text prompt to generate a 3D model, which lacks the control of each component of the generated 3D model. Any modifications of the input image lead to an entire regeneration of the 3D models. In this paper, we introduce a new method called CMD that generates a 3D model from an input image while enabling flexible local editing of each component of the 3D model. In CMD, we formulate the 3D generation as a conditional multiview diffusion model, which takes the existing or known parts as conditions and generates the edited or added components. This conditional multiview diffusion model not only allows the generation of 3D models part by part but also enables local editing of 3D models according to the local revision of the input image without changing other 3D parts. Extensive experiments are conducted to demonstrate that CMD decomposes a complex 3D generation task into multiple components, improving the generation quality. Meanwhile, CMD enables efficient and flexible local editing of a 3D model by just editing one rendered image.
Hallucination-Aware Multimodal Benchmark for Gastrointestinal Image Analysis with Large Vision-Language Models
Authors: Bidur Khanal, Sandesh Pokhrel, Sanjay Bhandari, Ramesh Rana, Nikesh Shrestha, Ram Bahadur Gurung, Cristian Linte, Angus Watson, Yash Raj Shrestha, Binod Bhattarai
Date: 2025-05-11 00:00:00
Tasks: Descriptive, Diagnostic, Hallucination

Vision-Language Models (VLMs) are becoming increasingly popular in the medical domain, bridging the gap between medical images and clinical language. Existing VLMs demonstrate an impressive ability to comprehend medical images and text queries to generate detailed, descriptive diagnostic medical reports. However, hallucination--the tendency to generate descriptions that are inconsistent with the visual content--remains a significant issue in VLMs, with particularly severe implications in the medical field. To facilitate VLM research on gastrointestinal (GI) image analysis and study hallucination, we curate a multimodal image-text GI dataset: Gut-VLM. This dataset is created using a two-stage pipeline: first, descriptive medical reports of Kvasir-v2 images are generated using ChatGPT, which introduces some hallucinated or incorrect texts. In the second stage, medical experts systematically review these reports, and identify and correct potential inaccuracies to ensure high-quality, clinically reliable annotations. Unlike traditional datasets that contain only descriptive texts, our dataset also features tags identifying hallucinated sentences and their corresponding corrections. A common approach to reducing hallucination in VLM is to finetune the model on a small-scale, problem-specific dataset. However, we take a different strategy using our dataset. Instead of finetuning the VLM solely for generating textual reports, we finetune it to detect and correct hallucinations, an approach we call hallucination-aware finetuning. Our results show that this approach is better than simply finetuning for descriptive report generation. Additionally, we conduct an extensive evaluation of state-of-the-art VLMs across several metrics, establishing a benchmark. GitHub Repo: https://github.com/bhattarailab/Hallucination-Aware-VLM.
Replay-Based Continual Learning with Dual-Layered Distillation and a Streamlined U-Net for Efficient Text-to-Image Generation
Authors: Md. Naimur Asif Borno, Md Sakib Hossain Shovon, Asmaa Soliman Al-Moisheer, Mohammad Ali Moni
Date: 2025-05-11 00:00:00
Tasks: Continual Learning, Image Generation, Text to Image Generation, Text-to-Image Generation

Recent advancements in text-to-image diffusion models are hindered by high computational demands, limiting accessibility and scalability. This paper introduces KDC-Diff, a novel stable diffusion framework that enhances efficiency while maintaining image quality. KDC-Diff features a streamlined U-Net architecture with nearly half the parameters of the original U-Net (482M), significantly reducing model complexity. We propose a dual-layered distillation strategy to ensure high-fidelity generation, transferring semantic and structural insights from a teacher to a compact student model while minimizing quality degradation. Additionally, replay-based continual learning is integrated to mitigate catastrophic forgetting, allowing the model to retain prior knowledge while adapting to new data. Despite operating under extremely low computational resources, KDC-Diff achieves state-of-the-art performance on the Oxford Flowers and Butterflies & Moths 100 Species datasets, demonstrating competitive metrics such as FID, CLIP, and LPIPS. Moreover, it significantly reduces inference time compared to existing models. These results establish KDC-Diff as a highly efficient and adaptable solution for text-to-image generation, particularly in computationally constrained environments.
Technical Report for ICRA 2025 GOOSE 2D Semantic Segmentation Challenge: Leveraging Color Shift Correction, RoPE-Swin Backbone, and Quantile-based Label Denoising Strategy for Robust Outdoor Scene Understanding
Authors: Chih-Chung Hsu, I-Hsuan Wu, Wen-Hai Tseng, Ching-Heng Cheng, Ming-Hsuan Wu, Jin-Hui Jiang, Yu-Jou Hsiao
Date: 2025-05-11 00:00:00
Tasks: 2D Semantic Segmentation, Denoising, Scene Understanding, Segmentation, Semantic Segmentation

This report presents our semantic segmentation framework developed by team ACVLAB for the ICRA 2025 GOOSE 2D Semantic Segmentation Challenge, which focuses on parsing outdoor scenes into nine semantic categories under real-world conditions. Our method integrates a Swin Transformer backbone enhanced with Rotary Position Embedding (RoPE) for improved spatial generalization, alongside a Color Shift Estimation-and-Correction module designed to compensate for illumination inconsistencies in natural environments. To further improve training stability, we adopt a quantile-based denoising strategy that downweights the top 2.5\% of highest-error pixels, treating them as noise and suppressing their influence during optimization. Evaluated on the official GOOSE test set, our approach achieved a mean Intersection over Union (mIoU) of 0.848, demonstrating the effectiveness of combining color correction, positional encoding, and error-aware denoising in robust semantic segmentation.
BridgeIV: Bridging Customized Image and Video Generation through Test-Time Autoregressive Identity Propagation
Authors: Panwen Hu, Jiehui Huang, Qiang Sun, Xiaodan Liang
Date: 2025-05-11 00:00:00
Tasks: Video Generation

Both zero-shot and tuning-based customized text-to-image (CT2I) generation have made significant progress for storytelling content creation. In contrast, research on customized text-to-video (CT2V) generation remains relatively limited. Existing zero-shot CT2V methods suffer from poor generalization, while another line of work directly combining tuning-based T2I models with temporal motion modules often leads to the loss of structural and texture information. To bridge this gap, we propose an autoregressive structure and texture propagation module (STPM), which extracts key structural and texture features from the reference subject and injects them autoregressively into each video frame to enhance consistency. Additionally, we introduce a test-time reward optimization (TTRO) method to further refine fine-grained details. Quantitative and qualitative experiments validate the effectiveness of STPM and TTRO, demonstrating improvements of 7.8 and 13.1 in CLIP-I and DINO consistency metrics over the baseline, respectively.
Federated Learning with LoRA Optimized DeiT and Multiscale Patch Embedding for Secure Eye Disease Recognition
Authors: Md. Naimur Asif Borno, Md Sakib Hossain Shovon, MD Hanif Sikder, Iffat Firozy Rimi, Tahani Jaser Alahmadi, Mohammad Ali Moni
Date: 2025-05-11 00:00:00
Tasks: Federated Learning

Recent progress in image-based medical disease detection encounters challenges such as limited annotated data sets, inadequate spatial feature analysis, data security issues, and inefficient training frameworks. This study introduces a data-efficient image transformer (DeIT)-based approach that overcomes these challenges by utilizing multiscale patch embedding for better feature extraction and stratified weighted random sampling to address class imbalance. The model also incorporates a LoRA-enhanced transformer encoder, a distillation framework, and federated learning for decentralized training, improving both efficiency and data security. Consequently, it achieves state-of-the-art performance, with the highest AUC, F1 score, precision, minimal loss, and Top-5 accuracy. Additionally, Grad-CAM++ visualizations improve interpretability by highlighting critical pathological regions, enhancing the model's clinical relevance. These results highlight the potential of this approach to advance AI-powered medical imaging and disease detection.
VALISENS: A Validated Innovative Multi-Sensor System for Cooperative Automated Driving
Authors: Lei Wan, Prabesh Gupta, Andreas Eich, Marcel Kettelgerdes, Hannan Ejaz Keen, Michael Klöppel-Gersdorf, Alexey Vinel
Date: 2025-05-11 00:00:00
Tasks: Motion Forecasting, object-detection, Object Detection, Sensor Fusion

Perception is a core capability of automated vehicles and has been significantly advanced through modern sensor technologies and artificial intelligence. However, perception systems still face challenges in complex real-world scenarios. To improve robustness against various external factors, multi-sensor fusion techniques are essential, combining the strengths of different sensor modalities. With recent developments in Vehicle-to-Everything (V2X communication, sensor fusion can now extend beyond a single vehicle to a cooperative multi-agent system involving Connected Automated Vehicle (CAV) and intelligent infrastructure. This paper presents VALISENS, an innovative multi-sensor system distributed across multiple agents. It integrates onboard and roadside LiDARs, radars, thermal cameras, and RGB cameras to enhance situational awareness and support cooperative automated driving. The thermal camera adds critical redundancy for perceiving Vulnerable Road User (VRU), while fusion with roadside sensors mitigates visual occlusions and extends the perception range beyond the limits of individual vehicles. We introduce the corresponding perception module built on this sensor system, which includes object detection, tracking, motion forecasting, and high-level data fusion. The proposed system demonstrates the potential of cooperative perception in real-world test environments and lays the groundwork for future Cooperative Intelligent Transport Systems (C-ITS) applications.
High-Frequency Prior-Driven Adaptive Masking for Accelerating Image Super-Resolution
Authors: Wei Shang, Dongwei Ren, Wanying Zhang, Pengfei Zhu, QinGhua Hu, WangMeng Zuo
Date: 2025-05-11 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

The primary challenge in accelerating image super-resolution lies in reducing computation while maintaining performance and adaptability. Motivated by the observation that high-frequency regions (e.g., edges and textures) are most critical for reconstruction, we propose a training-free adaptive masking module for acceleration that dynamically focuses computation on these challenging areas. Specifically, our method first extracts high-frequency components via Gaussian blur subtraction and adaptively generates binary masks using K-means clustering to identify regions requiring intensive processing. Our method can be easily integrated with both CNNs and Transformers. For CNN-based architectures, we replace standard $3 \times 3$ convolutions with an unfold operation followed by $1 \times 1$ convolutions, enabling pixel-wise sparse computation guided by the mask. For Transformer-based models, we partition the mask into non-overlapping windows and selectively process tokens based on their average values. During inference, unnecessary pixels or windows are pruned, significantly reducing computation. Moreover, our method supports dilation-based mask adjustment to control the processing scope without retraining, and is robust to unseen degradations (e.g., noise, compression). Extensive experiments on benchmarks demonstrate that our method reduces FLOPs by 24--43% for state-of-the-art models (e.g., CARN, SwinIR) while achieving comparable or better quantitative metrics. The source code is available at https://github.com/shangwei5/AMSR
Boosting Cross-spectral Unsupervised Domain Adaptation for Thermal Semantic Segmentation
Authors: Seokjun Kwon, Jeongmin Shin, Namil Kim, Soonmin Hwang, Yukyung Choi
Date: 2025-05-11 00:00:00
Tasks: Autonomous Driving, Domain Adaptation, Image Segmentation, Scene Understanding, Segmentation, Semantic Segmentation, Thermal Image Segmentation, Unsupervised Domain Adaptation

In autonomous driving, thermal image semantic segmentation has emerged as a critical research area, owing to its ability to provide robust scene understanding under adverse visual conditions. In particular, unsupervised domain adaptation (UDA) for thermal image segmentation can be an efficient solution to address the lack of labeled thermal datasets. Nevertheless, since these methods do not effectively utilize the complementary information between RGB and thermal images, they significantly decrease performance during domain adaptation. In this paper, we present a comprehensive study on cross-spectral UDA for thermal image semantic segmentation. We first propose a novel masked mutual learning strategy that promotes complementary information exchange by selectively transferring results between each spectral model while masking out uncertain regions. Additionally, we introduce a novel prototypical self-supervised loss designed to enhance the performance of the thermal segmentation model in nighttime scenarios. This approach addresses the limitations of RGB pre-trained networks, which cannot effectively transfer knowledge under low illumination due to the inherent constraints of RGB sensors. In experiments, our method achieves higher performance over previous UDA methods and comparable performance to state-of-the-art supervised methods.