pwc-archive/papers-with-abstracts huggingface dataset:
NTIRE 2025 Challenge on Cross-Domain Few-Shot Object Detection: Methods and Results
Authors: Yuqian Fu, Xingyu Qiu, Bin Ren, Yanwei Fu, Radu Timofte, Nicu Sebe, Ming-Hsuan Yang, Luc van Gool, Kaijin Zhang, Qingpeng Nong, Xiugang Dong, Hong Gao, Xiangsheng Zhou, Jiancheng Pan, Yanxing Liu, Xiao He, Jiahao Li, Yuze Sun, Xiaomeng Huang, Zhenyu Zhang, Ran Ma, YuHan Liu, Zijian Zhuang, Shuai Yi, Yixiong Zou, Lingyi Hong, Mingxi Chen, Runze Li, Xingdong Sheng, Wenqiang Zhang, Weisen Chen, Yongxin Yan, Xinguo Chen, Yuanjie Shao, Zhengrong Zuo, Nong Sang, Hao Wu, Haoran Sun, Shuming Hu, Yan Zhang, Zhiguang Shi, Yu Zhang, Chao Chen, Tao Wang, Da Feng, Linhai Zhuo, Ziming Lin, Yali Huang, Jie Me, Yiming Yang, Mi Guo, Mingyuan Jiu, Mingliang Xu, Maomao Xiong, Qunshu Zhang, Xinyu Cao, Yuqing Yang, Dianmo Sheng, Xuanpu Zhao, Zhiyu Li, Xuyang Ding, Wenqian Li
Date: 2025-04-14 00:00:00
Tasks: Cross-Domain Few-Shot, Cross-Domain Few-Shot Object Detection, Few-Shot Object Detection, Object, object-detection, Object Detection, valid

Cross-Domain Few-Shot Object Detection (CD-FSOD) poses significant challenges to existing object detection and few-shot detection models when applied across domains. In conjunction with NTIRE 2025, we organized the 1st CD-FSOD Challenge, aiming to advance the performance of current object detectors on entirely novel target domains with only limited labeled data. The challenge attracted 152 registered participants, received submissions from 42 teams, and concluded with 13 teams making valid final submissions. Participants approached the task from diverse perspectives, proposing novel models that achieved new state-of-the-art (SOTA) results under both open-source and closed-source settings. In this report, we present an overview of the 1st NTIRE 2025 CD-FSOD Challenge, highlighting the proposed solutions and summarizing the results submitted by the participants.
PatrolVision: Automated License Plate Recognition in the wild
Authors: Anmol Singhal Navya Singhal
Date: 2025-04-15 00:00:00
Tasks: Autonomous Driving, GPU, License Plate Recognition, vehicle detection

Adoption of AI driven techniques in public services remains low due to challenges related to accuracy and speed of information at population scale. Computer vision techniques for traffic monitoring have not gained much popularity despite their relative strength in areas such as autonomous driving. Despite large number of academic methods for Automatic License Plate Recognition (ALPR) systems, very few provide an end to end solution for patrolling in the city. This paper presents a novel prototype for a low power GPU based patrolling system to be deployed in an urban environment on surveillance vehicles for automated vehicle detection, recognition and tracking. In this work, we propose a complete ALPR system for Singapore license plates having both single and double line creating our own YOLO based network. We focus on unconstrained capture scenarios as would be the case in real world application, where the license plate (LP) might be considerably distorted due to oblique views. In this work, we first detect the license plate from the full image using RFB-Net and rectify multiple distorted license plates in a single image. After that, the detected license plate image is fed to our network for character recognition. We evaluate the performance of our proposed system on a newly built dataset covering more than 16,000 images. The system was able to correctly detect license plates with 86\% precision and recognize characters of a license plate in 67\% of the test set, and 89\% accuracy with one incorrect character (partial match). We also test latency of our system and achieve 64FPS on Tesla P4 GPU
Bringing together invertible UNets with invertible attention modules for memory-efficient diffusion models
Authors: Karan Jain, Mohammad Nayeem Teli
Date: 2025-04-15 00:00:00
Tasks: Denoising, GPU, Image Generation

Diffusion models have recently gained state of the art performance on many image generation tasks. However, most models require significant computational resources to achieve this. This becomes apparent in the application of medical image synthesis due to the 3D nature of medical datasets like CT-scans, MRIs, electron microscope, etc. In this paper we propose a novel architecture for a single GPU memory-efficient training for diffusion models for high dimensional medical datasets. The proposed model is built by using an invertible UNet architecture with invertible attention modules. This leads to the following two contributions: 1. denoising diffusion models and thus enabling memory usage to be independent of the dimensionality of the dataset, and 2. reducing the energy usage during training. While this new model can be applied to a multitude of image generation tasks, we showcase its memory-efficiency on the 3D BraTS2020 dataset leading to up to 15\% decrease in peak memory consumption during training with comparable results to SOTA while maintaining the image quality.
PuzzleBench: A Fully Dynamic Evaluation Framework for Large Multimodal Models on Puzzle Solving
Authors: Zeyu Zhang, Zijian Chen, ZiCheng Zhang, Yuze Sun, Yuan Tian, Ziheng Jia, Chunyi Li, Xiaohong Liu, Xiongkuo Min, Guangtao Zhai
Date: 2025-04-15 00:00:00
Tasks: Logical Reasoning, Visual Question Answering (VQA)

Large Multimodal Models (LMMs) have demonstrated impressive capabilities across a wide range of multimodal tasks, achieving ever-increasing performance on various evaluation benchmarks. However, existing benchmarks are typically static and often overlap with pre-training datasets, leading to fixed complexity constraints and substantial data contamination issues. Meanwhile, manually annotated datasets are labor-intensive, time-consuming, and subject to human bias and inconsistency, leading to reliability and reproducibility issues. To address these problems, we propose a fully dynamic multimodal evaluation framework, named Open-ended Visual Puzzle Generation (OVPG), which aims to generate fresh, diverse, and verifiable evaluation data automatically in puzzle-solving tasks. Specifically, the OVPG pipeline consists of a raw material sampling module, a visual content generation module, and a puzzle rule design module, which ensures that each evaluation instance is primitive, highly randomized, and uniquely solvable, enabling continual adaptation to the evolving capabilities of LMMs. Built upon OVPG, we construct PuzzleBench, a dynamic and scalable benchmark comprising 11,840 VQA samples. It features six carefully designed puzzle tasks targeting three core LMM competencies, visual recognition, logical reasoning, and context understanding. PuzzleBench differs from static benchmarks that quickly become outdated. It enables ongoing dataset refreshing through OVPG and a rich set of open-ended puzzle designs, allowing seamless adaptation to the evolving capabilities of LMMs.
CDUPatch: Color-Driven Universal Adversarial Patch Attack for Dual-Modal Visible-Infrared Detectors
Authors: Jiahuan Long, Wen Yao, Tingsong Jiang, Chao Ma
Date: 2025-04-15 00:00:00
Tasks: Data Augmentation, object-detection, Object Detection

Adversarial patches are widely used to evaluate the robustness of object detection systems in real-world scenarios. These patches were initially designed to deceive single-modal detectors (e.g., visible or infrared) and have recently been extended to target visible-infrared dual-modal detectors. However, existing dual-modal adversarial patch attacks have limited attack effectiveness across diverse physical scenarios. To address this, we propose CDUPatch, a universal cross-modal patch attack against visible-infrared object detectors across scales, views, and scenarios. Specifically, we observe that color variations lead to different levels of thermal absorption, resulting in temperature differences in infrared imaging. Leveraging this property, we propose an RGB-to-infrared adapter that maps RGB patches to infrared patches, enabling unified optimization of cross-modal patches. By learning an optimal color distribution on the adversarial patch, we can manipulate its thermal response and generate an adversarial infrared texture. Additionally, we introduce a multi-scale clipping strategy and construct a new visible-infrared dataset, MSDrone, which contains aerial vehicle images in varying scales and perspectives. These data augmentation strategies enhance the robustness of our patch in real-world conditions. Experiments on four benchmark datasets (e.g., DroneVehicle, LLVIP, VisDrone, MSDrone) show that our method outperforms existing patch attacks in the digital domain. Extensive physical tests further confirm strong transferability across scales, views, and scenarios.
TMCIR: Token Merge Benefits Composed Image Retrieval
Authors: Chaoyang Wang, Zeyu Zhang, Long Teng, Zijun Li, Shichao Kan
Date: 2025-04-15 00:00:00
Tasks: Contrastive Learning, cross-modal alignment, Image Retrieval, Image to text, Retrieval

Composed Image Retrieval (CIR) retrieves target images using a multi-modal query that combines a reference image with text describing desired modifications. The primary challenge is effectively fusing this visual and textual information. Current cross-modal feature fusion approaches for CIR exhibit an inherent bias in intention interpretation. These methods tend to disproportionately emphasize either the reference image features (visual-dominant fusion) or the textual modification intent (text-dominant fusion through image-to-text conversion). Such an imbalanced representation often fails to accurately capture and reflect the actual search intent of the user in the retrieval results. To address this challenge, we propose TMCIR, a novel framework that advances composed image retrieval through two key innovations: 1) Intent-Aware Cross-Modal Alignment. We first fine-tune CLIP encoders contrastively using intent-reflecting pseudo-target images, synthesized from reference images and textual descriptions via a diffusion model. This step enhances the encoder ability of text to capture nuanced intents in textual descriptions. 2) Adaptive Token Fusion. We further fine-tune all encoders contrastively by comparing adaptive token-fusion features with the target image. This mechanism dynamically balances visual and textual representations within the contrastive learning pipeline, optimizing the composed feature for retrieval. Extensive experiments on Fashion-IQ and CIRR datasets demonstrate that TMCIR significantly outperforms state-of-the-art methods, particularly in capturing nuanced user intent.
MediSee: Reasoning-based Pixel-level Perception in Medical Images
Authors: Qinyue Tong, Ziqian Lu, Jun Liu, Yangming Zheng, Zheming Lu
Date: 2025-04-15 00:00:00
Tasks: Logical Reasoning, Reasoning Segmentation, Segmentation

Despite remarkable advancements in pixel-level medical image perception, existing methods are either limited to specific tasks or heavily rely on accurate bounding boxes or text labels as input prompts. However, the medical knowledge required for input is a huge obstacle for general public, which greatly reduces the universality of these methods. Compared with these domain-specialized auxiliary information, general users tend to rely on oral queries that require logical reasoning. In this paper, we introduce a novel medical vision task: Medical Reasoning Segmentation and Detection (MedSD), which aims to comprehend implicit queries about medical images and generate the corresponding segmentation mask and bounding box for the target object. To accomplish this task, we first introduce a Multi-perspective, Logic-driven Medical Reasoning Segmentation and Detection (MLMR-SD) dataset, which encompasses a substantial collection of medical entity targets along with their corresponding reasoning. Furthermore, we propose MediSee, an effective baseline model designed for medical reasoning segmentation and detection. The experimental results indicate that the proposed method can effectively address MedSD with implicit colloquial queries and outperform traditional medical referring segmentation methods.
GATE3D: Generalized Attention-based Task-synergized Estimation in 3D*
Authors: Eunsoo Im, Changhyun Jee, Jung Kwon Lee
Date: 2025-04-15 00:00:00
Tasks: 2D Object Detection, 3D Object Detection, Autonomous Driving, Monocular 3D Object Detection, object-detection, Object Detection

The emerging trend in computer vision emphasizes developing universal models capable of simultaneously addressing multiple diverse tasks. Such universality typically requires joint training across multi-domain datasets to ensure effective generalization. However, monocular 3D object detection presents unique challenges in multi-domain training due to the scarcity of datasets annotated with accurate 3D ground-truth labels, especially beyond typical road-based autonomous driving contexts. To address this challenge, we introduce a novel weakly supervised framework leveraging pseudo-labels. Current pretrained models often struggle to accurately detect pedestrians in non-road environments due to inherent dataset biases. Unlike generalized image-based 2D object detection models, achieving similar generalization in monocular 3D detection remains largely unexplored. In this paper, we propose GATE3D, a novel framework designed specifically for generalized monocular 3D object detection via weak supervision. GATE3D effectively bridges domain gaps by employing consistency losses between 2D and 3D predictions. Remarkably, our model achieves competitive performance on the KITTI benchmark as well as on an indoor-office dataset collected by us to evaluate the generalization capabilities of our framework. Our results demonstrate that GATE3D significantly accelerates learning from limited annotated data through effective pre-training strategies, highlighting substantial potential for broader impacts in robotics, augmented reality, and virtual reality applications. Project page: https://ies0411.github.io/GATE3D/
QAVA: Query-Agnostic Visual Attack to Large Vision-Language Models
Authors: Yudong Zhang, Ruobing Xie, Jiansheng Chen, Xingwu Sun, Zhanhui Kang, Yu Wang
Date: 2025-04-15 00:00:00
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

In typical multimodal tasks, such as Visual Question Answering (VQA), adversarial attacks targeting a specific image and question can lead large vision-language models (LVLMs) to provide incorrect answers. However, it is common for a single image to be associated with multiple questions, and LVLMs may still answer other questions correctly even for an adversarial image attacked by a specific question. To address this, we introduce the query-agnostic visual attack (QAVA), which aims to create robust adversarial examples that generate incorrect responses to unspecified and unknown questions. Compared to traditional adversarial attacks focused on specific images and questions, QAVA significantly enhances the effectiveness and efficiency of attacks on images when the question is unknown, achieving performance comparable to attacks on known target questions. Our research broadens the scope of visual adversarial attacks on LVLMs in practical settings, uncovering previously overlooked vulnerabilities, particularly in the context of visual adversarial threats. The code is available at https://github.com/btzyd/qava.
DMAGaze: Gaze Estimation Based on Feature Disentanglement and Multi-Scale Attention
Authors: Haohan Chen, Hongjia Liu, Shiyong Lan, Wenwu Wang, Yixin Qiao, Yao Li, Guonan Deng
Date: 2025-04-15 00:00:00
Tasks: Disentanglement, Gaze Estimation, Head Pose Estimation, Pose Estimation

Gaze estimation, which predicts gaze direction, commonly faces the challenge of interference from complex gaze-irrelevant information in face images. In this work, we propose DMAGaze, a novel gaze estimation framework that exploits information from facial images in three aspects: gaze-relevant global features (disentangled from facial image), local eye features (extracted from cropped eye patch), and head pose estimation features, to improve overall performance. Firstly, we design a new continuous mask-based Disentangler to accurately disentangle gaze-relevant and gaze-irrelevant information in facial images by achieving the dual-branch disentanglement goal through separately reconstructing the eye and non-eye regions. Furthermore, we introduce a new cascaded attention module named Multi-Scale Global Local Attention Module (MS-GLAM). Through a customized cascaded attention structure, it effectively focuses on global and local information at multiple scales, further enhancing the information from the Disentangler. Finally, the global gaze-relevant features disentangled by the upper face branch, combined with head pose and local eye features, are passed through the detection head for high-precision gaze estimation. Our proposed DMAGaze has been extensively validated on two mainstream public datasets, achieving state-of-the-art performance.
TerraMind: Large-Scale Generative Multimodality for Earth Observation
Authors: Johannes Jakubik, Felix Yang, Benedikt Blumenstiel, Erik Scheurer, Rocco Sedona, Stefano Maurogiovanni, Jente Bosmans, Nikolaos Dionelis, Valerio Marsocci, Niklas Kopp, Rahul Ramachandran, Paolo Fraccaro, Thomas Brunschwiler, Gabriele Cavallaro, Juan Bernabe-Moreno, Nicolas Longépé
Date: 2025-04-15 00:00:00
Tasks: Earth Observation

We present TerraMind, the first any-to-any generative, multimodal foundation model for Earth observation (EO). Unlike other multimodal models, TerraMind is pretrained on dual-scale representations combining both token-level and pixel-level data across modalities. On a token level, TerraMind encodes high-level contextual information to learn cross-modal relationships, while on a pixel level, TerraMind leverages fine-grained representations to capture critical spatial nuances. We pretrained TerraMind on nine geospatial modalities of a global, large-scale dataset. In this paper, we demonstrate that (i) TerraMind's dual-scale early fusion approach unlocks a range of zero-shot and few-shot applications for Earth observation, (ii) TerraMind introduces "Thinking-in-Modalities" (TiM) -- the capability of generating additional artificial data during finetuning and inference to improve the model output -- and (iii) TerraMind achieves beyond state-of-the-art performance in community-standard benchmarks for EO like PANGAEA. The pretraining dataset, the model weights, and our code is open-sourced under a permissive license.
Single-Input Multi-Output Model Merging: Leveraging Foundation Models for Dense Multi-Task Learning
Authors: Juan Garcia Giraldo, Nikolaos Dimitriadis, Ke Wang, Pascal Frossard
Date: 2025-04-15 00:00:00
Tasks: Multi-Task Learning, Scene Understanding, Task Arithmetic

Model merging is a flexible and computationally tractable approach to merge single-task checkpoints into a multi-task model. Prior work has solely focused on constrained multi-task settings where there is a one-to-one mapping between a sample and a task, overlooking the paradigm where multiple tasks may operate on the same sample, e.g., scene understanding. In this paper, we focus on the multi-task setting with single-input-multiple-outputs (SIMO) and show that it qualitatively differs from the single-input-single-output model merging settings studied in the literature due to the existence of task-specific decoders and diverse loss objectives. We identify that existing model merging methods lead to significant performance degradation, primarily due to representation misalignment between the merged encoder and task-specific decoders. We propose two simple and efficient fixes for the SIMO setting to re-align the feature representation after merging. Compared to joint fine-tuning, our approach is computationally effective and flexible, and sheds light into identifying task relationships in an offline manner. Experiments on NYUv2, Cityscapes, and a subset of the Taskonomy dataset demonstrate: (1) task arithmetic suffices to enable multi-task capabilities; however, the representations generated by the merged encoder has to be re-aligned with the task-specific heads; (2) the proposed architecture rivals traditional multi-task learning in performance but requires fewer samples and training steps by leveraging the existence of task-specific models.
CFIS-YOLO: A Lightweight Multi-Scale Fusion Network for Edge-Deployable Wood Defect Detection
Authors: Jincheng Kang, Yi Cen, Yigang Cen, Ke Wang, YuHan Liu
Date: 2025-04-15 00:00:00
Tasks: Computational Efficiency, Defect Detection, object-detection, Object Detection, Object Localization

Wood defect detection is critical for ensuring quality control in the wood processing industry. However, current industrial applications face two major challenges: traditional methods are costly, subjective, and labor-intensive, while mainstream deep learning models often struggle to balance detection accuracy and computational efficiency for edge deployment. To address these issues, this study proposes CFIS-YOLO, a lightweight object detection model optimized for edge devices. The model introduces an enhanced C2f structure, a dynamic feature recombination module, and a novel loss function that incorporates auxiliary bounding boxes and angular constraints. These innovations improve multi-scale feature fusion and small object localization while significantly reducing computational overhead. Evaluated on a public wood defect dataset, CFIS-YOLO achieves a mean Average Precision (mAP@0.5) of 77.5\%, outperforming the baseline YOLOv10s by 4 percentage points. On SOPHON BM1684X edge devices, CFIS-YOLO delivers 135 FPS, reduces power consumption to 17.3\% of the original implementation, and incurs only a 0.5 percentage point drop in mAP. These results demonstrate that CFIS-YOLO is a practical and effective solution for real-world wood defect detection in resource-constrained environments.
Multi-level Cellular Automata for FLIM networks
Authors: Felipe Crispim Salvagnini, Jancarlo F. Gomes, Cid A. N. Santos, Silvio Jamil F. Guimarães, Alexandre X. Falcão
Date: 2025-04-15 00:00:00
Tasks: Salient Object Detection

The necessity of abundant annotated data and complex network architectures presents a significant challenge in deep-learning Salient Object Detection (deep SOD) and across the broader deep-learning landscape. This challenge is particularly acute in medical applications in developing countries with limited computational resources. Combining modern and classical techniques offers a path to maintaining competitive performance while enabling practical applications. Feature Learning from Image Markers (FLIM) methodology empowers experts to design convolutional encoders through user-drawn markers, with filters learned directly from these annotations. Recent findings demonstrate that coupling a FLIM encoder with an adaptive decoder creates a flyweight network suitable for SOD, requiring significantly fewer parameters than lightweight models and eliminating the need for backpropagation. Cellular Automata (CA) methods have proven successful in data-scarce scenarios but require proper initialization -- typically through user input, priors, or randomness. We propose a practical intersection of these approaches: using FLIM networks to initialize CA states with expert knowledge without requiring user interaction for each image. By decoding features from each level of a FLIM network, we can initialize multiple CAs simultaneously, creating a multi-level framework. Our method leverages the hierarchical knowledge encoded across different network layers, merging multiple saliency maps into a high-quality final output that functions as a CA ensemble. Benchmarks across two challenging medical datasets demonstrate the competitiveness of our multi-level CA approach compared to established models in the deep SOD literature.