GenAI4ELab/papercli-papers huggingface dataset:
Unified Category-Level Object Detection and Pose Estimation from RGB Images using 3D Prototypes
Authors: Tom Fischer; Xiaojie Zhang; Eddy Ilg
Venue: ICCV 2025 (main)

Abstract: Recognizing objects in images is a fundamental problem in computer vision. Although detecting objects in 2D images is common, many applications require determining their pose in 3D space. Traditional category-level methods rely on RGB-D inputs, which may not always be available, or employ two-stage approaches that use separate models and representations for detection and pose estimation. For the first time, we introduce a unified model that integrates detection and pose estimation into a single framework for RGB images by leveraging neural mesh models with learned features and multi-model RANSAC. Our approach achieves state-of-the-art results for RGB category-level pose estimation on REAL275, improving on the current state-of-the-art by 22.9% averaged across all scale-agnostic metrics. Finally, we demonstrate that our unified method exhibits greater robustness compared to single-stage baselines.
GT-Mean Loss: A Simple Yet Effective Solution for Brightness Mismatch in Low-Light Image Enhancement
Authors: Jingxi Liao; Shijie Hao; Richang Hong; Meng Wang
Venue: ICCV 2025 (main)

Abstract: Low-light image enhancement (LLIE) aims to improve the visual quality of images captured under poor lighting conditions. In supervised LLIE tasks, there exists a significant yet often overlooked inconsistency between the overall brightness of an enhanced image and its ground truth counterpart, referred to as brightness mismatch in this study. Brightness mismatch negatively impact supervised LLIE models by misleading model training. However, this issue is largely neglected in current research. In this context, we propose the  GT-mean loss, a simple yet effective loss function directly modeling the mean values of images from a probabilistic perspective.The GT-mean loss is flexible, as it extends existing supervised LLIE loss functions into the GT-mean form with minimal additional computational costs. Extensive experiments demonstrate that the incorporation of the GT-mean loss results in consistent performance improvements across various methods and datasets.
OpenVision: A Fully-Open, Cost-Effective Family of Advanced Vision Encoders for Multimodal Learning
Authors: Xianhang Li; Yanqing Liu; Haoqin Tu; Cihang Xie
Venue: ICCV 2025 (main)

Abstract: OpenAI's CLIP models, released in early 2021, have long been the only viable choice for the research community in building multimodal foundation models. This dominance has only recently been challenged by a few alternatives like SigLIP. However, to the best of our knowledge, all these solutions are still not fully open, e.g., their training data remains proprietary and/or their training frameworks are unreleased. In this paper, we address this challenge by introducing a family of fully open vision encoders that are as competitive as, or even surpass, OpenAI's CLIP in building multimodal foundation models like LLaVA. Moreover, due to their fully open nature, we offer these vision encoders in a wide range of sizes, from as few as 5.9 million parameters to 632.1 million parameters. We further demonstrate that these variable-sized vision encoders provide significant flexibility: larger models deliver enhanced multimodal performance, while smaller models enable efficient and portable multimodal foundation models suitable for edge device deployment. The training data, code and trained models will be released soon.
Unified Multimodal Understanding via Byte-Pair Visual Encoding
Authors: Wanpeng Zhang; Yicheng Feng; Hao Luo; Yijiang Li; Zihao Yue; Sipeng Zheng; Zongqing Lu
Venue: ICCV 2025 (main)

Abstract: Multimodal large language models (MLLMs) have made significant progress in vision-language understanding, yet effectively aligning different modalities remains a fundamental challenge. We present a framework that unifies multimodal understanding by applying byte-pair encoding to visual tokens. Unlike conventional approaches that rely on modality-specific encoders, our method directly incorporates structural information into visual tokens, mirroring successful tokenization strategies in text-only language models. We introduce a priority-guided encoding scheme that considers both frequency and spatial consistency, coupled with a multi-stage training procedure based on curriculum-driven data composition. These enhancements enable the transformer model to better capture cross-modal relationships and reason with visual information. Comprehensive experiments demonstrate improved performance across diverse vision-language tasks. By bridging the gap between visual and textual representations, our approach contributes to the advancement of more capable and efficient multimodal foundation models.
MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices
Authors: Hailong Yan; Ao Li; Xiangtao Zhang; Zhe Liu; Zenglin Shi; Ce Zhu; Le Zhang
Venue: ICCV 2025 (main)

Abstract: Recent advancements in deep neural networks have driven significant progress in image enhancement (IE). However, deploying deep learning models on resource-constrained platforms, such as mobile devices, remains challenging due to high computation and memory demands. To address these challenges and facilitate real-time IE on mobile, we introduce an extremely lightweight Convolutional Neural Network (CNN) framework with around 4K parameters. Our approach integrates re-parameterization with an Incremental Weight Optimization strategy to ensure efficiency. Additionally, we enhance performance with a Feature Self-Transform module and a Hierarchical Dual-Path Attention mechanism, optimized with a Local Variance-Weighted loss. With this efficient framework, we are the first to achieve real-time IE inference at up to 1,100 frames per second (FPS) while delivering competitive image quality, achieving the best trade-off between speed and performance across multiple IE tasks. The code will be available at https://github.com/AVC2-UESTC/MobileIE.git.
Learning Pixel-adaptive Multi-layer Perceptrons for Real-time Image Enhancement
Authors: Junyu Lou; Xiaorui Zhao; Kexuan Shi; Shuhang Gu
Venue: ICCV 2025 (main)

Abstract: Deep learning-based bilateral grid processing has emerged as a promising solution for image enhancement, inherently encoding spatial and intensity information while enabling efficient full-resolution processing through slicing operations. However, existing approaches are limited to linear affine transformations, hindering their ability to model complex color relationships. Meanwhile, while multi-layer perceptrons (MLPs) excel at non-linear mappings, traditional MLP-based methods employ globally shared parameters, which is hard to deal with localized variations. To overcome these dual challenges, we propose a Bilateral Grid-based Pixel-Adaptive Multi-layer Perceptron (BPAM) framework. Our approach synergizes the spatial modeling of bilateral grids with the non-linear capabilities of MLPs. Specifically, we generate bilateral grids containing MLP parameters, where each pixel dynamically retrieves its unique transformation parameters and obtain a distinct MLP for color mapping based on spatial coordinates and intensity values. In addition, we propose a novel grid decomposition strategy that categorizes MLP parameters into distinct types stored in separate subgrids. Multi-channel guidance maps are used to extract category-specific parameters from corresponding subgrids, ensuring effective utilization of color information during slicing while guiding precise parameter generation. Extensive experiments on public datasets demonstrate that our method outperforms state-of-the-art methods in performance while maintaining real-time processing capabilities.
ACE-G: Improving Generalization of Scene Coordinate Regression Through Query Pre-Training
Authors: Leonard Bruns; Axel Barroso-Laguna; Tommaso Cavallari; Aron Monszpart; Sowmya Munukutla; Victor Adrian Prisacariu; Eric Brachmann
Venue: ICCV 2025 (main)

Abstract: Scene coordinate regression (SCR) has established itself as a promising learning-based approach to visual relocalization. After mere minutes of scene-specific training, SCR models estimate camera poses of query images with high accuracy. Still, SCR methods fall short of the generalization capabilities of more classical feature-matching approaches. When imaging conditions of query images, such as lighting or viewpoint, are too different from the training views, SCR models fail. Failing to generalize is an inherent limitation of previous SCR frameworks, since their training objective is to encode the training views in the weights of the coordinate regressor itself. The regressor essentially overfits to the training views, by design. We propose to separate the coordinate regressor and the map representation into a generic transformer and a scene-specific map code. This separation allows us to pre-train the transformer on tens of thousands of scenes. More importantly, it allows us to train the transformer to generalize from mapping images to unseen query images during pre-training. We demonstrate on multiple challenging relocalization datasets that our method, ACE-G, leads to significantly increased robustness while keeping the computational footprint attractive.
Everything is a Video: Unifying Modalities through Next-Frame Prediction
Authors: G. Thomas Hudson; Dean Slack; Thomas Winterbottom; Jamie Sterling; Chenghao Xiao; Junjie Shentu; Noura Al Moubayed
Venue: ICCV 2025 (main)

Abstract: Multimodal learning, which involves integrating information from various modalities such as text, images, audio, and video, is pivotal for numerous complex tasks like visual question answering, cross-modal retrieval, and caption generation. Traditional approaches rely on modality-specific encoders and late fusion techniques, which can hinder flexibility when adapting to new tasks or modalities. To address these limitations, we introduce a novel framework that extends the concept of task reformulation beyond natural language processing (NLP) to multimodal learning. We propose to reformulate diverse multimodal tasks into a unified next-frame prediction problem, allowing a single model to handle different modalities without modality-specific components. This method treats all inputs and outputs as sequential frames in a video, enabling seamless integration of modalities and effective knowledge transfer across tasks. Our approach is evaluated on a range of tasks, including text-to-text, image-to-text, video-to-text, and audio-to-text, demonstrating the model's ability to generalize across modalities with minimal adaptation. We show that task reformulation can significantly simplify multimodal model design across various tasks, laying the groundwork for more generalized multimodal foundation models.
ViT-EnsembleAttack: Augmenting Ensemble Models for Stronger Adversarial Transferability in Vision Transformers
Authors: Hanwen Cao; Haobo Lu; Xiaosen Wang; Kun He
Venue: ICCV 2025 (main)

Abstract: Ensemble-based attacks have been proven to be effective in enhancing adversarial transferability by aggregating the output of models with various architectures. However, existing research primarily focuses on refining ensemble weights or optimizing the ensemble path, overlooking the exploration of ensemble models to enhance the transferability of adversarial attacks. To address this gap, we propose applying adversarial augmentation to the surrogate models, aiming to boost overall generalization of ensemble models and reduce the risk of adversarial overfitting. Meanwhile, observing that ensemble Vision Transformers (ViTs) gain less attention, we propose ViT-EnsembleAttack based on the idea of model adversarial augmentation, the first ensemble-based attack method tailored for ViTs to the best of our knowledge. Our approach generates augmented models for each surrogate ViT using three strategies: Multi-head dropping, Attention score scaling, and MLP feature mixing, with the associated parameters optimized by Bayesian optimization. These adversarially augmented models are ensembled to generate adversarial examples. Furthermore, we introduce Automatic Reweighting and Step Size Enlargement modules to boost transferability. Extensive experiments demonstrate that ViT-EnsembleAttack significantly enhances the adversarial transferability of ensemble-based attacks on ViTs, outperforming existing methods by a substantial margin. Code is available at https://github.com/Trustworthy-AI-Group/TransferAttack.
LayerTracer: Cognitive-Aligned Layered SVG Synthesis via Diffusion Transformer
Authors: Yiren Song; Danze Chen; Mike Zheng Shou
Venue: ICCV 2025 (main)

Abstract: Generating cognitive-aligned layered SVGs remains challenging due to existing methods' tendencies toward either oversimplified single-layer outputs or optimization-induced shape redundancies. We propose LayerTracer, a DiT based framework that bridges this gap by learning designers' layered SVG creation processes from a novel dataset of sequential design operations. Our approach operates in two phases: First, a text-conditioned DiT generates multi-phase rasterized construction blueprints that simulate human design workflows. Second, layer-wise vectorization with path deduplication produces clean, editable SVGs. For image vectorization, we introduce a conditional diffusion mechanism that encodes reference images into latent tokens, guiding hierarchical reconstruction while preserving structural integrity. Extensive experiments show that LayerTracer surpasses optimization-based and neural baselines in generation quality and editability.
TopoTTA: Topology-Enhanced Test-Time Adaptation for Tubular Structure Segmentation
Authors: Jiale Zhou; Wenhan Wang; Shikun Li; Xiaolei Qu; Xin Guo; Yizhong Liu; Wenzhong Tang; Xun Lin; Yefeng Zheng
Venue: ICCV 2025 (main)

Abstract: Tubular structure segmentation (TSS) is important for various applications, such as hemodynamic analysis and route navigation. Despite significant progress in TSS, domain shifts remain a major challenge, leading to performance degradation in unseen target domains. Unlike other segmentation tasks, TSS is more sensitive to domain shifts, as changes in topological structures can compromise segmentation integrity, and variations in local features distinguishing foreground from background (e.g., texture and contrast) may further disrupt topological continuity. To address these challenges, we propose Topology-enhanced Test-Time Adaptation (TopoTTA), the first test-time adaptation framework designed specifically for TSS. TopoTTA consists of two stages: Stage 1 adapts models to cross-domain topological discrepancies using the proposed Topological Meta Difference Convolutions (TopoMDCs), which enhance topological representation without altering pre-trained parameters; Stage 2 improves topological continuity by a novel Topology Hard sample Generation (TopoHG) strategy and prediction alignment on hard samples with pseudo-labels in the generated pseudo-break regions. Extensive experiments across four scenarios and ten datasets demonstrate TopoTTA's effectiveness in handling topological distribution shifts, achieving an average improvement of 31.81% in clDice. TopoTTA also serves as a plug-and-play TTA solution for CNN-based TSS models.
Counting Stacked Objects
Authors: Corentin Dumery; Noa Etté; Aoxiang Fan; Ren Li; Jingyi Xu; Hieu Le; Pascal Fua
Venue: ICCV 2025 (main)

Abstract: Visual object counting is a fundamental computer vision task underpinning numerous real-world applications, from cell counting in biomedicine to traffic and wildlife monitoring. However, existing methods struggle to handle the challenge of stacked 3D objects in which most objects are hidden by those above them. To address this important yet underexplored problem, we propose a novel 3D counting approach that decomposes the task into two complementary subproblems - estimating the 3D geometry of the object stack and the occupancy ratio from multi-view images. By combining geometric reconstruction and deep learning-based depth analysis, our method can accurately count identical objects within containers, even when they are irregularly stacked. We validate our 3D Counting pipeline on large-scale synthetic and diverse real-world datasets with manually verified total counts.
VOccl3D: A Video Benchmark Dataset for 3D Human Pose and Shape Estimation under real Occlusions
Authors: Yash Garg; Saketh Bachu; Arindam Dutta; Rohit Lal; Sarosij Bose; Calvin-Khang Ta; M. Salman Asif; Amit Roy-Chowdhury
Venue: ICCV 2025 (main)

Abstract: Human pose and shape (HPS) estimation methods have been extensively studied, with many demonstrating high zero-shot performance on in-the-wild images and videos. However, these methods often struggle in challenging scenarios involving complex human poses or significant occlusions. Although some studies address 3D human pose estimation under occlusion, they typically evaluate performance on datasets that lack realistic or substantial occlusions, e.g., most existing datasets introduce occlusions with random patches over the human or clipart-style overlays, which may not reflect real-world challenges. To bridge this gap in realistic occlusion datasets, we introduce a novel benchmark dataset, VOccl3D, a Video-based human Occlusion dataset with 3D body pose and shape annotations. Inspired by works such as AGORA and BEDLAM, we constructed this dataset using advanced computer graphics rendering techniques, incorporating diverse real-world occlusion scenarios, clothing textures, and human motions. Additionally, we fine-tuned recent HPS methods, CLIFF and BEDLAM-CLIFF, on our dataset, demonstrating significant qualitative and quantitative improvements across multiple public datasets, as well as on the test split of our dataset, while comparing its performance with other state-of-the-art methods. Furthermore, we leveraged our dataset to enhance human detection performance under occlusion by fine-tuning an existing object detector, YOLO11, thus leading to a robust end-to-end HPS estimation system under occlusions. Overall, this dataset serves as a valuable resource for future research aimed at benchmarking methods designed to handle occlusions, offering a more realistic alternative to existing occlusion datasets.
PoseAnchor: Robust Root Position Estimation for 3D Human Pose Estimation
Authors: Jun-Hee Kim; Jumin Han; Seong-Whan Lee
Venue: ICCV 2025 (main)

Abstract: Standard 3D human pose estimation (HPE) benchmarks employ root-centering, which normalizes poses relative to the pelvis but discards absolute root position information. While effective for evaluation, this approach limits real-world applications such as motion tracking, AR/VR, and human-computer interaction, where absolute root position is essential. Moreover, incorporating root position into these models often leads to performance degradation. To address these limitations, we introduce PoseAnchor, a unified framework that seamlessly integrates root position estimation while improving overall pose accuracy. PoseAnchor leverages Iterative Hard Thresholding Robust Least Squares Regression (ITRR), a novel robust regression approach introduced to 3D HPE for the first time. ITRR effectively mitigates the impact of noisy 2D detections, enabling more accurate root position estimation. With ITRR, PoseAnchor enables zero-shot root localization, allowing existing models to estimate absolute root positions without retraining or architectural modifications. ITRR identifies a support set of reliable joints based on their spatial relationships to achieve robust root estimation, effectively filtering out unreliable joints. Beyond zero-shot localization, PoseAnchor incorporates ITRR into a Data-Driven Training framework that selectively utilizes the support set to optimize pose learning. By dynamically filtering high-confidence joint data, PoseAnchor mitigates noise while improving robustness.
Sparfels: Fast Reconstruction from Sparse Unposed Imagery
Authors: Shubhendu Jena; Amine Ouasfi; Mae Younes; Adnane Boukhayma
Venue: ICCV 2025 (main)

Abstract: We present a method for Sparse view reconstruction with surface element splatting that runs within 2 minutes on a consumer grade GPU. While few methods address sparse radiance field learning from noisy or unposed sparse cameras, shape recovery remains relatively underexplored in this setting. Several radiance and shape learning test-time optimization methods address the sparse posed setting by learning data priors or using combinations of external monocular geometry priors. Differently, we propose an efficient and simple pipeline harnessing a single recent 3D foundation model. We leverage its various task heads, notably point maps and camera initializations to instantiate a bundle adjusting 2D Gaussian Splatting (2DGS) model, and image correspondences to guide camera optimization midst 2DGS training. Key to our contribution is a novel formulation of splatted color variance along rays, which can be computed efficiently. Reducing this moment in training leads to more accurate shape reconstructions. We demonstrate stat-of-the-art performances in the sparse uncalibrated setting in reconstruction and novel view Benchmarks based on established multi-view datasets.
Unsupervised Visual Chain-of-Thought Reasoning via Preference Optimization
Authors: Kesen Zhao; Beier Zhu; Qianru Sun; Hanwang Zhang
Venue: ICCV 2025 (main)

Abstract: Chain-of-thought (CoT) reasoning greatly improves the interpretability and problem-solving abilities of multimodal large language models (MLLMs). However, existing approaches are focused on text CoT, limiting their ability to leverage visual cues. Visual CoT remains underexplored, and the only work is based on supervised fine-tuning (SFT) that relies on extensive labeled bounding-box data and is hard to generalize to unseen cases. In this paper, we introduce Unsupervised Visual CoT (UV-CoT), a novel framework for image-level CoT reasoning via preference optimization. UV-CoT performs preference comparisons between model-generated bounding boxes (one is preferred and the other is dis-preferred), eliminating the need for bounding-box annotations. We get such preference data by introducing an automatic data generation pipeline. Given an image, our target MLLM (e.g., LLaVA-1.5-7B) generates seed bounding boxes using a template prompt and then answers the question using each bounded region as input. An evaluator MLLM (e.g., OmniLLM-12B) ranks the responses, and these rankings serve as supervision to train the target MLLM with UV-CoT by minimizing negative log-likelihood losses. By emulating human perception--identifying key regions and reasoning based on them--UV-CoT can improve visual comprehension, particularly in spatial reasoning tasks where textual descriptions alone fall short. Our experiments on six datasets demonstrate the superiority of UV-CoT, compared to the state-of-the-art textual and visual CoT methods. Our zero-shot testing on three unseen datasets shows the strong generalization of UV-CoT. The implementation code is available in the Appendix.
On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations
Authors: Amir Mehrpanah; Matteo Gamba; Kevin Smith; Hossein Azizpour
Venue: ICCV 2025 (main)

Abstract: ReLU networks, while prevalent for visual data, have sharp transitions, sometimes relying on individual pixels for predictions, making vanilla gradient-based explanations noisy and difficult to interpret. Existing methods, such as GradCAM, smooth these explanations by producing surrogate models at the cost of faithfulness. We introduce a unifying spectral framework to systematically analyze and quantify smoothness, faithfulness, and their trade-off in explanations.Using this framework, we quantify and regularize the contribution of ReLU networks to high-frequency information, providing a principled approach to identifying this trade-off. Our analysis characterizes how surrogate-based smoothing distorts explanations, leading to an "explanation gap" that we formally define and measure for different post-hoc methods.Finally, we validate our theoretical findings across different design choices, datasets, and ablations.