pwc-archive/papers-with-abstracts huggingface dataset:
Zebrafish Counting Using Event Stream Data
Authors: Qianghua Chen, Huiyu Wang, Li Ming, Ying Zhao
Date: 2025-04-18 00:00:00
Tasks: Camera Calibration

Zebrafish share a high degree of homology with human genes and are commonly used as model organism in biomedical research. For medical laboratories, counting zebrafish is a daily task. Due to the tiny size of zebrafish, manual visual counting is challenging. Existing counting methods are either not applicable to small fishes or have too many limitations. The paper proposed a zebrafish counting algorithm based on the event stream data. Firstly, an event camera is applied for data acquisition. Secondly, camera calibration and image fusion were preformed successively. Then, the trajectory information was used to improve the counting accuracy. Finally, the counting results were averaged over an empirical of period and rounded up to get the final results. To evaluate the accuracy of the algorithm, 20 zebrafish were put in a four-liter breeding tank. Among 100 counting trials, the average accuracy reached 97.95%. As compared with traditional algorithms, the proposed one offers a simpler implementation and achieves higher accuracy.
Analysing the Robustness of Vision-Language-Models to Common Corruptions
Authors: Muhammad Usama, Syeda Aishah Asim, Syed Bilal Ali, Syed Talal Wasim, Umair Bin Mansoor
Date: 2025-04-18 00:00:00
Tasks: TextVQA

Vision-language models (VLMs) have demonstrated impressive capabilities in understanding and reasoning about visual and textual content. However, their robustness to common image corruptions remains under-explored. In this work, we present the first comprehensive analysis of VLM robustness across 19 corruption types from the ImageNet-C benchmark, spanning four categories: noise, blur, weather, and digital distortions. We introduce two new benchmarks, TextVQA-C and GQA-C, to systematically evaluate how corruptions affect scene text understanding and object-based reasoning, respectively. Our analysis reveals that transformer-based VLMs exhibit distinct vulnerability patterns across tasks: text recognition deteriorates most severely under blur and snow corruptions, while object reasoning shows higher sensitivity to corruptions such as frost and impulse noise. We connect these observations to the frequency-domain characteristics of different corruptions, revealing how transformers' inherent bias toward low-frequency processing explains their differential robustness patterns. Our findings provide valuable insights for developing more corruption-robust vision-language models for real-world applications.
EyecareGPT: Boosting Comprehensive Ophthalmology Understanding with Tailored Dataset, Benchmark and Model
Authors: Sijing Li, Tianwei Lin, Lingshuai Lin, Wenqiao Zhang, Jiang Liu, Xiaoda Yang, Juncheng Li, Yucheng He, Xiaohui Song, Jun Xiao, Yueting Zhuang, Beng Chin Ooi
Date: 2025-04-18 00:00:00
Tasks: Diagnostic

Medical Large Vision-Language Models (Med-LVLMs) demonstrate significant potential in healthcare, but their reliance on general medical data and coarse-grained global visual understanding limits them in intelligent ophthalmic diagnosis. Currently, intelligent ophthalmic diagnosis faces three major challenges: (i) Data. The lack of deeply annotated, high-quality, multi-modal ophthalmic visual instruction data; (ii) Benchmark. The absence of a comprehensive and systematic benchmark for evaluating diagnostic performance; (iii) Model. The difficulty of adapting holistic visual architectures to fine-grained, region-specific ophthalmic lesion identification. In this paper, we propose the Eyecare Kit, which systematically tackles the aforementioned three key challenges with the tailored dataset, benchmark and model: First, we construct a multi-agent data engine with real-life ophthalmology data to produce Eyecare-100K, a high-quality ophthalmic visual instruction dataset. Subsequently, we design Eyecare-Bench, a benchmark that comprehensively evaluates the overall performance of LVLMs on intelligent ophthalmic diagnosis tasks across multiple dimensions. Finally, we develop the EyecareGPT, optimized for fine-grained ophthalmic visual understanding thoroughly, which incorporates an adaptive resolution mechanism and a layer-wise dense connector. Extensive experimental results indicate that the EyecareGPT achieves state-of-the-art performance in a range of ophthalmic tasks, underscoring its significant potential for the advancement of open research in intelligent ophthalmic diagnosis. Our project is available at https://github.com/DCDmllm/EyecareGPT.
Enhancing Pothole Detection and Characterization: Integrated Segmentation and Depth Estimation in Road Anomaly Systems
Authors: Uthman Baroudi, Alala BaHamid, Yasser Elalfy, Ziad Al Alami
Date: 2025-04-18 00:00:00
Tasks: Anomaly Detection, Depth Estimation, Transfer Learning

Road anomaly detection plays a crucial role in road maintenance and in enhancing the safety of both drivers and vehicles. Recent machine learning approaches for road anomaly detection have overcome the tedious and time-consuming process of manual analysis and anomaly counting; however, they often fall short in providing a complete characterization of road potholes. In this paper, we leverage transfer learning by adopting a pre-trained YOLOv8-seg model for the automatic characterization of potholes using digital images captured from a dashboard-mounted camera. Our work includes the creation of a novel dataset, comprising both images and their corresponding depth maps, collected from diverse road environments in Al-Khobar city and the KFUPM campus in Saudi Arabia. Our approach performs pothole detection and segmentation to precisely localize potholes and calculate their area. Subsequently, the segmented image is merged with its depth map to extract detailed depth information about the potholes. This integration of segmentation and depth data offers a more comprehensive characterization compared to previous deep learning-based road anomaly detection systems. Overall, this method not only has the potential to significantly enhance autonomous vehicle navigation by improving the detection and characterization of road hazards but also assists road maintenance authorities in responding more effectively to road damage.
Efficient Parameter Adaptation for Multi-Modal Medical Image Segmentation and Prognosis
Authors: Numan Saeed, Shahad Hardan, Muhammad Ridzuan, Nada Saadi, Karthik Nandakumar, Mohammad Yaqub
Date: 2025-04-18 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Prognosis, Semantic Segmentation, Tumor Segmentation

Cancer detection and prognosis relies heavily on medical imaging, particularly CT and PET scans. Deep Neural Networks (DNNs) have shown promise in tumor segmentation by fusing information from these modalities. However, a critical bottleneck exists: the dependency on CT-PET data concurrently for training and inference, posing a challenge due to the limited availability of PET scans. Hence, there is a clear need for a flexible and efficient framework that can be trained with the widely available CT scans and can be still adapted for PET scans when they become available. In this work, we propose a parameter-efficient multi-modal adaptation (PEMMA) framework for lightweight upgrading of a transformer-based segmentation model trained only on CT scans such that it can be efficiently adapted for use with PET scans when they become available. This framework is further extended to perform prognosis task maintaining the same efficient cross-modal fine-tuning approach. The proposed approach is tested with two well-known segementation backbones, namely UNETR and Swin UNETR. Our approach offers two main advantages. Firstly, we leverage the inherent modularity of the transformer architecture and perform low-rank adaptation (LoRA) as well as decomposed low-rank adaptation (DoRA) of the attention weights to achieve parameter-efficient adaptation. Secondly, by minimizing cross-modal entanglement, PEMMA allows updates using only one modality without causing catastrophic forgetting in the other. Our method achieves comparable performance to early fusion, but with only 8% of the trainable parameters, and demonstrates a significant +28% Dice score improvement on PET scans when trained with a single modality. Furthermore, in prognosis, our method improves the concordance index by +10% when adapting a CT-pretrained model to include PET scans, and by +23% when adapting for both PET and EHR data.
Inverse Inference on Cooperative Control of Networked Dynamical Systems
Authors: Yushan Li, Jianping He, Dimos V. Dimarogonas
Date: 2025-04-18 00:00:00

Recent years have witnessed the rapid advancement of understanding the control mechanism of networked dynamical systems (NDSs), which are governed by components such as nodal dynamics and topology. This paper reveals that the critical components in continuous-time state feedback cooperative control of NDSs can be inferred merely from discrete observations. In particular, we advocate a bi-level inference framework to estimate the global closed-loop system and extract the components, respectively. The novelty lies in bridging the gap from discrete observations to the continuous-time model and effectively decoupling the concerned components. Specifically, in the first level, we design a causality-based estimator for the discrete-time closed-loop system matrix, which can achieve asymptotically unbiased performance when the NDS is stable. In the second level, we introduce a matrix logarithm based method to recover the continuous-time counterpart matrix, providing new sampling period guarantees and establishing the recovery error bound. By utilizing graph properties of the NDS, we develop least square based procedures to decouple the concerned components with up to a scalar ambiguity. Furthermore, we employ inverse optimal control techniques to reconstruct the objective function driving the control process, deriving necessary conditions for the solutions. Numerical simulations demonstrate the effectiveness of the proposed method.
DenSe-AdViT: A novel Vision Transformer for Dense SAR Object Detection
Authors: Yang Zhang, Jingyi Cao, Yanan You, Yuanyuan Qiao
Date: 2025-04-18 00:00:00
Tasks: object-detection, Object Detection

Vision Transformer (ViT) has achieved remarkable results in object detection for synthetic aperture radar (SAR) images, owing to its exceptional ability to extract global features. However, it struggles with the extraction of multi-scale local features, leading to limited performance in detecting small targets, especially when they are densely arranged. Therefore, we propose Density-Sensitive Vision Transformer with Adaptive Tokens (DenSe-AdViT) for dense SAR target detection. We design a Density-Aware Module (DAM) as a preliminary component that generates a density tensor based on target distribution. It is guided by a meticulously crafted objective metric, enabling precise and effective capture of the spatial distribution and density of objects. To integrate the multi-scale information enhanced by convolutional neural networks (CNNs) with the global features derived from the Transformer, Density-Enhanced Fusion Module (DEFM) is proposed. It effectively refines attention toward target-survival regions with the assist of density mask and the multiple sources features. Notably, our DenSe-AdViT achieves 79.8% mAP on the RSDD dataset and 92.5% on the SIVED dataset, both of which feature a large number of densely distributed vehicle targets.
SupResDiffGAN a new approach for the Super-Resolution task
Authors: Dawid Kopeć, Wojciech Kozłowski, Maciej Wizerkaniuk, Dawid Krutul, Jan Kocoń, Maciej Zięba
Date: 2025-04-18 00:00:00
Tasks: Image Generation, Super-Resolution

In this work, we present SupResDiffGAN, a novel hybrid architecture that combines the strengths of Generative Adversarial Networks (GANs) and diffusion models for super-resolution tasks. By leveraging latent space representations and reducing the number of diffusion steps, SupResDiffGAN achieves significantly faster inference times than other diffusion-based super-resolution models while maintaining competitive perceptual quality. To prevent discriminator overfitting, we propose adaptive noise corruption, ensuring a stable balance between the generator and the discriminator during training. Extensive experiments on benchmark datasets show that our approach outperforms traditional diffusion models such as SR3 and I$^2$SB in efficiency and image quality. This work bridges the performance gap between diffusion- and GAN-based methods, laying the foundation for real-time applications of diffusion models in high-resolution image generation.
Visual Intention Grounding for Egocentric Assistants
Authors: Pengzhan Sun, Junbin Xiao, Tze Ho Elden Tse, Yicong Li, Arjun Akula, Angela Yao
Date: 2025-04-18 00:00:00
Tasks: Object, Visual Grounding

Visual grounding associates textual descriptions with objects in an image. Conventional methods target third-person image inputs and named object queries. In applications such as AI assistants, the perspective shifts -- inputs are egocentric, and objects may be referred to implicitly through needs and intentions. To bridge this gap, we introduce EgoIntention, the first dataset for egocentric visual intention grounding. EgoIntention challenges multimodal LLMs to 1) understand and ignore unintended contextual objects and 2) reason about uncommon object functionalities. Benchmark results show that current models misidentify context objects and lack affordance understanding in egocentric views. We also propose Reason-to-Ground (RoG) instruction tuning; it enables hybrid training with normal descriptions and egocentric intentions with a chained intention reasoning and object grounding mechanism. RoG significantly outperforms naive finetuning and hybrid training on EgoIntention, while maintaining or slightly improving naive description grounding. This advancement enables unified visual grounding for egocentric and exocentric visual inputs while handling explicit object queries and implicit human intentions.
Compile Scene Graphs with Reinforcement Learning
Authors: Zuyao Chen, Jinlin Wu, Zhen Lei, Marc Pollefeys, Chang Wen Chen
Date: 2025-04-18 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Next-token prediction is the fundamental principle for training large language models (LLMs), and reinforcement learning (RL) further enhances their reasoning performance. As an effective way to model language, image, video, and other modalities, the use of LLMs for end-to-end extraction of structured visual representations, such as scene graphs, remains underexplored. It requires the model to accurately produce a set of objects and relationship triplets, rather than generating text token by token. To achieve this, we introduce R1-SGG, a multimodal LLM (M-LLM) initially trained via supervised fine-tuning (SFT) on the scene graph dataset and subsequently refined using reinforcement learning to enhance its ability to generate scene graphs in an end-to-end manner. The SFT follows a conventional prompt-response paradigm, while RL requires the design of effective reward signals. We design a set of graph-centric rewards, including three recall-based variants -- Hard Recall, Hard Recall+Relax, and Soft Recall -- which evaluate semantic and spatial alignment between predictions and ground truth at the object and relation levels. A format consistency reward further ensures that outputs follow the expected structural schema. Extensive experiments on the VG150 and PSG benchmarks show that R1-SGG substantially reduces failure rates and achieves strong performance in Recall and mean Recall, surpassing traditional SGG models and existing multimodal language models. Our code is available at https://github.com/gpt4vision/R1-SGG
Cross-Hierarchical Bidirectional Consistency Learning for Fine-Grained Visual Classification
Authors: Pengxiang Gao, Yihao Liang, Yanzhi Song, Zhouwang Yang
Date: 2025-04-18 00:00:00
Tasks: Classification, Fine-Grained Image Classification, image-classification, Image Classification

Fine-Grained Visual Classification (FGVC) aims to categorize closely related subclasses, a task complicated by minimal inter-class differences and significant intra-class variance. Existing methods often rely on additional annotations for image classification, overlooking the valuable information embedded in Tree Hierarchies that depict hierarchical label relationships. To leverage this knowledge to improve classification accuracy and consistency, we propose a novel Cross-Hierarchical Bidirectional Consistency Learning (CHBC) framework. The CHBC framework extracts discriminative features across various hierarchies using a specially designed module to decompose and enhance attention masks and features. We employ bidirectional consistency loss to regulate the classification outcomes across different hierarchies, ensuring label prediction consistency and reducing misclassification. Experiments on three widely used FGVC datasets validate the effectiveness of the CHBC framework. Ablation studies further investigate the application strategies of feature enhancement and consistency constraints, underscoring the significant contributions of the proposed modules.
FocusTrack: A Self-Adaptive Local Sampling Algorithm for Efficient Anti-UAV Tracking
Authors: Ying Wang, Tingfa Xu, Jianan Li
Date: 2025-04-18 00:00:00
Tasks: Computational Efficiency

Anti-UAV tracking poses significant challenges, including small target sizes, abrupt camera motion, and cluttered infrared backgrounds. Existing tracking paradigms can be broadly categorized into global- and local-based methods. Global-based trackers, such as SiamDT, achieve high accuracy by scanning the entire field of view but suffer from excessive computational overhead, limiting real-world deployment. In contrast, local-based methods, including OSTrack and ROMTrack, efficiently restrict the search region but struggle when targets undergo significant displacements due to abrupt camera motion. Through preliminary experiments, it is evident that a local tracker, when paired with adaptive search region adjustment, can significantly enhance tracking accuracy, narrowing the gap between local and global trackers. To address this challenge, we propose FocusTrack, a novel framework that dynamically refines the search region and strengthens feature representations, achieving an optimal balance between computational efficiency and tracking accuracy. Specifically, our Search Region Adjustment (SRA) strategy estimates the target presence probability and adaptively adjusts the field of view, ensuring the target remains within focus. Furthermore, to counteract feature degradation caused by varying search regions, the Attention-to-Mask (ATM) module is proposed. This module integrates hierarchical information, enriching the target representations with fine-grained details. Experimental results demonstrate that FocusTrack achieves state-of-the-art performance, obtaining 67.7% AUC on AntiUAV and 62.8% AUC on AntiUAV410, outperforming the baseline tracker by 8.5% and 9.1% AUC, respectively. In terms of efficiency, FocusTrack surpasses global-based trackers, requiring only 30G MACs and achieving 143 fps with FocusTrack (SRA) and 44 fps with the full version, both enabling real-time tracking.
ViG3D-UNet: Volumetric Vascular Connectivity-Aware Segmentation via 3D Vision Graph Representation
Authors: Bowen Liu, Chunlei Meng, Wei Lin, Hongda Zhang, Ziqing Zhou, Zhongxue Gan, Chun Ouyang
Date: 2025-04-18 00:00:00
Tasks: Graph Neural Network, Segmentation

Accurate vascular segmentation is essential for coronary visualization and the diagnosis of coronary heart disease. This task involves the extraction of sparse tree-like vascular branches from the volumetric space. However, existing methods have faced significant challenges due to discontinuous vascular segmentation and missing endpoints. To address this issue, a 3D vision graph neural network framework, named ViG3D-UNet, was introduced. This method integrates 3D graph representation and aggregation within a U-shaped architecture to facilitate continuous vascular segmentation. The ViG3D module captures volumetric vascular connectivity and topology, while the convolutional module extracts fine vascular details. These two branches are combined through channel attention to form the encoder feature. Subsequently, a paperclip-shaped offset decoder minimizes redundant computations in the sparse feature space and restores the feature map size to match the original input dimensions. To evaluate the effectiveness of the proposed approach for continuous vascular segmentation, evaluations were performed on two public datasets, ASOCA and ImageCAS. The segmentation results show that the ViG3D-UNet surpassed competing methods in maintaining vascular segmentation connectivity while achieving high segmentation accuracy. Our code will be available soon.
LMPOcc: 3D Semantic Occupancy Prediction Utilizing Long-Term Memory Prior from Historical Traversals
Authors: Shanshuai Yuan, Julong Wei, Muer Tie, Xiangyun Ren, Zhongxue Gan, Wenchao Ding
Date: 2025-04-18 00:00:00
Tasks: 3D Semantic Occupancy Prediction, Autonomous Driving, Autonomous Vehicles, Prediction

Vision-based 3D semantic occupancy prediction is critical for autonomous driving, enabling unified modeling of static infrastructure and dynamic agents. In practice, autonomous vehicles may repeatedly traverse identical geographic locations under varying environmental conditions, such as weather fluctuations and illumination changes. Existing methods in 3D occupancy prediction predominantly integrate adjacent temporal contexts. However, these works neglect to leverage perceptual information, which is acquired from historical traversals of identical geographic locations. In this paper, we propose Longterm Memory Prior Occupancy (LMPOcc), the first 3D occupancy prediction methodology that exploits long-term memory priors derived from historical traversal perceptual outputs. We introduce a plug-and-play architecture that integrates long-term memory priors to enhance local perception while simultaneously constructing global occupancy representations. To adaptively aggregate prior features and current features, we develop an efficient lightweight Current-Prior Fusion module. Moreover, we propose a model-agnostic prior format to ensure compatibility across diverse occupancy prediction baselines. LMPOcc achieves state-of-the-art performance validated on the Occ3D-nuScenes benchmark, especially on static semantic categories. Additionally, experimental results demonstrate LMPOcc's ability to construct global occupancy through multi-vehicle crowdsourcing.