GenAI4ELab/papercli-papers huggingface dataset:
FedXDS: Leveraging Model Attribution Methods to counteract Data Heterogeneity in Federated Learning
Authors: Maximilian Andreas Hoefler; Karsten Mueller; Wojciech Samek
Venue: ICCV 2025 (main)

Abstract: Explainable AI (XAI) methods have demonstrated significant success in recent years at identifying relevant features in input data that drive deep learning model decisions, enhancing interpretability for users. However, the potential of XAI beyond providing model transparency has remained largely unexplored in adjacent machine learning domains. In this paper, we show for the first time how XAI can be utilized in the context of federated learning. Specifically, while federated learning enables collaborative model training without raw data sharing, it suffers from performance degradation when client data distributions exhibit statistical heterogeneity. We introduce FedXDS (Federated Learning via XAI-guided Data Sharing), the first approach to utilize feature attribution techniques to identify precisely which data elements should be selectively shared between clients to mitigate heterogeneity. By employing propagation-based attribution, our method identifies task-relevant features through a single backward pass, enabling selective data sharing that aligns client contributions. To protect sensitive information, we incorporate metric privacy techniques that provide formal privacy guarantees while preserving utility. Experimental results demonstrate that our approach consistently achieves higher accuracy and faster convergence compared to existing methods across varying client numbers and heterogeneity settings. We provide theoretical privacy guarantees and empirically demonstrate robustness against both membership inference and feature inversion attacks. Code is available at https://github.com/MaxH1996/FedXDS.
Retinex-MEF: Retinex-based Glare Effects Aware Unsupervised Multi-Exposure Image Fusion
Authors: Haowen Bai; Jiangshe Zhang; Zixiang Zhao; Lilun Deng; Yukun Cui; Shuang Xu
Venue: ICCV 2025 (main)

Abstract: Multi-exposure image fusion (MEF) synthesizes multiple, differently exposed images of the same scene into a single, well-exposed composite. Retinex theory, which separates image illumination from scene reflectance, provides a natural framework to ensure consistent scene representation and effective information fusion across varied exposure levels. However, the conventional pixel-wise multiplication of illumination and reflectance inadequately models the glare effect induced by overexposure. To address this limitation, we introduce an unsupervised and controllable method termed Retinex-MEF. Specifically, our method decomposes multi-exposure images into separate illumination components with a shared reflectance component, and effectively models the glare induced by overexposure. The shared reflectance is learned via a bidirectional loss, which enables our approach to effectively mitigate the glare effect. Furthermore, we introduce a controllable exposure fusion criterion, enabling global exposure adjustments while preserving contrast, thus overcoming the constraints of a fixed exposure level. Extensive experiments on diverse datasets, including underexposure-overexposure fusion, exposure controlled fusion, and homogeneous extreme exposure fusion, demonstrate the effective decomposition and flexible fusion capability of our model. The code is available at https://github.com/HaowenBai/Retinex-MEF
Structure Matters: Revisiting Boundary Refinement in Video Object Segmentation
Authors: Guanyi Qin; Ziyue Wang; Daiyun Shen; Haofeng Liu; Hantao Zhou; Junde Wu; Runze Hu; Yueming Jin
Venue: ICCV 2025 (main)

Abstract: Given an object mask, Semi-supervised Video Object Segmentation (SVOS) technique aims to track and segment the object across video frames, serving as a fundamental task in computer vision. Although recent memory-based methods demonstrate potential, they often struggle with scenes involving occlusion, particularly in handling object interactions and high feature similarity. To address these issues and meet the real-time processing requirements of downstream applications, in this paper, we propose a novel bOundary Amendment video object Segmentation method with Inherent Structure refinement, hereby named OASIS. Specifically, a lightweight structure refinement module is proposed to enhance segmentation accuracy. With the fusion of rough edge priors captured by the Canny filter and stored object features, the module can generate an object-level structure map and refine the representations by highlighting boundary features. Evidential learning for uncertainty estimation is introduced to further address challenges in occluded regions. The proposed method, OASIS, maintains an efficient design, yet extensive experiments on challenging benchmarks demonstrate its superior performance and competitive inference speed compared to other state-of-the-art methods, i.e., achieving the F values of 91.6 (vs. 89.7 on DAVIS-17 validation set) and G values of 86.6 (vs. 86.2 on YouTubeVOS 2019 validation set) while maintaining a competitive speed of 48 FPS on DAVIS.
Bilateral Collaboration with Large Vision-Language Models for Open Vocabulary Human-Object Interaction Detection
Authors: Yupeng Hu; Changxing Ding; Chang Sun; Shaoli Huang; Xiangmin Xu
Venue: ICCV 2025 (main)

Abstract: Open vocabulary Human-Object Interaction (HOI) detection is a challenging task that detects all <human, verb, object> triplets of interest in an image, even those that are not pre-defined in the training set. Existing approaches typically rely on output features generated by large Vision-Language Models (VLMs) to enhance the generalization ability of interaction representations. However, the visual features produced by VLMs are holistic and coarse-grained, which contradicts the nature of detection tasks. To address this issue, we propose a novel Bilateral Collaboration framework for open vocabulary HOI detection (BC-HOI). This framework includes an Attention Bias Guidance (ABG) component, which guides the VLM to produce fine-grained instance-level interaction features according to the attention bias provided by the HOI detector. It also includes a Large Language Model (LLM)-based Supervision Guidance (LSG) component, which provides fine-grained token-level supervision for the HOI detector by the LLM component of the VLM. LSG enhances the ability of ABG to generate high-quality attention bias. We conduct extensive experiments on two popular benchmarks: HICO-DET and V-COCO, consistently achieving superior performance in the open vocabulary and closed settings. The code will be released in Github.
GaSLight: Gaussian Splats for Spatially-Varying Lighting in HDR
Authors: Christophe Bolduc; Yannick Hold-Geoffroy; Jean-François Lalonde
Venue: ICCV 2025 (main)

Abstract: We present GaSLight, a method that generates spatially-varying lighting from regular images. Our method proposes using HDR Gaussian Splats as light source representation, marking the first time regular images can serve as light sources in a 3D renderer. Our two-stage process first enhances the dynamic range of images plausibly and accurately by leveraging the priors embedded in diffusion models. Next, we employ Gaussian Splats to model 3D lighting, achieving spatially variant lighting. Our approach yields state-of-the-art results on HDR estimations and their applications in illuminating virtual objects and scenes. To facilitate the benchmarking of images as light sources, we introduce a novel dataset of calibrated and unsaturated HDR captures to evaluate images as light sources. We assess our method using a combination of our dataset and an existing dataset from the literature. The code to reproduce our method is available at https://lvsn.github.io/gaslight/.
Hate in Plain Sight: On the Risks of Moderating AI-Generated Hateful Illusions
Authors: Yiting Qu; Ziqing Yang; Yihan Ma; Michael Backes; Savvas Zannettou; Yang Zhang
Venue: ICCV 2025 (main)

Abstract: Recent advances in text-to-image diffusion models have enabled the creation of a new form of digital art: optical illusions---visual tricks that create different perceptions of reality. However, adversaries may misuse such techniques to generate hateful illusions, which embed specific hate messages into harmless scenes and disseminate them across web communities. In this work, we take the first step toward investigating the risks of scalable hateful illusion generation and the potential for bypassing current content moderation models. Specifically, we generate 1,860 optical illusions using Stable Diffusion and ControlNet, conditioned on 62 hate messages. Of these, 1,571 are hateful illusions that successfully embed hate messages, either overtly or subtly, forming the Hateful Illusion dataset. Using this dataset, we evaluate the performance of six moderation classifiers and nine vision language models (VLMs) in identifying hateful illusions. Experimental results reveal significant vulnerabilities in existing moderation models: the detection accuracy falls below 0.245 for moderation classifiers and below 0.102 for VLMs. We further identify a critical limitation in their vision encoders, which mainly focus on surface-level image details while overlooking the secondary layer of information, i.e., hidden messages. To address this risk, we explore preliminary mitigation measures and identify the most effective approaches from the perspectives of image transformations and training-level strategies.
Steering Guidance for Personalized Text-to-Image Diffusion Models
Authors: Sunghyun Park; Seokeon Choi; Hyoungwoo Park; Sungrack Yun
Venue: ICCV 2025 (main)

Abstract: Personalizing text-to-image diffusion models is crucial for adapting the pre-trained models to specific target concepts, enabling diverse image generation. However, fine-tuning with few images introduces an inherent trade-off between aligning with the target distribution (e.g., subject fidelity) and preserving the broad knowledge of the original model (e.g., text editability). Existing sampling guidance methods, such as classifier-free guidance (CFG) and autoguidance (AG), fail to effectively guide the output toward well-balanced space: CFG restricts the adaptation to the target distribution, while AG compromises text alignment. To address these limitations, we propose personalization guidance, a simple yet effective method leveraging an unlearned weak model conditioned on a null text prompt. Moreover, our method dynamically controls the extent of unlearning in a weak model through weight interpolation between pre-trained and fine-tuned models during inference. Unlike existing guidance methods, which depend solely on guidance scales, our method explicitly steers the outputs toward a balanced latent space without additional computational overhead. Experimental results demonstrate that our proposed guidance can improve text alignment and target distribution fidelity, integrating seamlessly with various fine-tuning strategies.
Improving Rectified Flow with Boundary Conditions
Authors: Xixi Hu; Runlong Liao; Keyang Xu; Bo Liu; Yeqing Li; Eugene Ie; Hongliang Fei; Qiang Liu
Venue: ICCV 2025 (main)

Abstract: Rectified Flow offers a simple and effective approach to high-quality generative modeling by learning a velocity field. However,we identify a limitation in directly modeling the velocity with an unconstrained neural network: the learned velocity often fails to satisfy certain boundary conditions, leading to inaccurate velocity field estimations that deviate from the desired ODE. This issue is particularly critical during stochastic sampling at inference, as the score function's errors are amplified near the boundary. To mitigate this, we propose a Boundary-enforced Rectified Flow Model (Boundary RF Model), in which we enforce boundary conditions with a minimal code modification. Boundary RF Model improves performance over vanilla RF model, demonstrating 8.01% improvement in FID score on ImageNet using ODE sampling and 8.98% improvement using SDE sampling.
ATAS: Any-to-Any Self-Distillation for Enhanced Open-Vocabulary Dense Prediction
Authors: Juan Yeo; Soonwoo Cha; Jiwoo Song; Hyunbin Jin; Taesup Kim
Venue: ICCV 2025 (main)

Abstract: Vision-language models such as CLIP have recently propelled open-vocabulary dense prediction tasks by enabling recognition of a broad range of visual concepts. However, CLIP still struggles with fine-grained, region-level understanding, hindering its effectiveness on these dense prediction tasks. We identify two pivotal factors required to address this limitation: semantic coherence and fine-grained vision-language alignment. Current adaptation methods often improve fine-grained alignment at the expense of semantic coherence, and often rely on extra modules or supervised fine-tuning. To overcome these issues, we propose Any-to-Any Self-Distillation (ATAS), a novel approach that simultaneously enhances semantic coherence and fine-grained alignment by leveraging a model's own knowledge across all representation levels. Unlike prior methods, ATAS uses only unlabeled images and an internal self-distillation process to refine CLIP's representations, preserving local semantic consistency while sharpening local detail recognition. On open-vocabulary object detection and semantic segmentation benchmarks, ATAS achieves substantial performance gains, outperforming baseline CLIP models. These results validate the effectiveness of our approach and underscore the importance of jointly maintaining semantic coherence and fine-grained alignment for advanced open-vocabulary dense prediction.
Multimodal Prompt Alignment for Facial Expression Recognition
Authors: Fuyan Ma; Yiran He; Bin Sun; Shutao Li
Venue: ICCV 2025 (main)

Abstract: Prompt learning has been widely adopted to efficiently adapt vision-language models (VLMs) like CLIP for various downstream tasks. Despite their success, current VLM-based facial expression recognition (FER) methods struggle to capture fine-grained textual-visual relationships, which are essential for distinguishing subtle differences between facial expressions. To address this challenge, we propose a multimodal prompt alignment framework for FER, called MPA-FER, that provides fine-grained semantic guidance to the learning process of prompted visual features, resulting in more precise and interpretable representations. Specifically, we introduce a multi-granularity hard prompt generation strategy that utilizes a large language model (LLM) like ChatGPT to generate detailed descriptions for each facial expression. The LLM-based external knowledge is injected into the soft prompts by minimizing the feature discrepancy between the soft prompts and the hard prompts. To preserve the generalization abilities of the pretrained CLIP model, our approach incorporates prototype-guided visual feature alignment, ensuring that the prompted visual features from the frozen image encoder align closely with class-specific prototypes. Additionally, we propose a cross-modal global-local alignment module that focuses on expression-relevant facial features, further improving the alignment between textual and visual features. Extensive experiments demonstrate our framework outperforms state-of-the-art methods on three FER benchmark datasets, while retaining the benefits of the pretrained model and minimizing computational costs.
Laboring on less labors: RPCA Paradigm for Pan-sharpening
Authors: Honghui Xu; Chuangjie Fang; Yibin Wang; Jie Wu; Jianwei Zheng
Venue: ICCV 2025 (main)

Abstract: Deep unfolding network (DUN) based pansharpening has shed new light on high-resolution/spectrum image acquisition, serving as a computational alternative to physical devices. While with both merits of deep feature learning and acceptable interpretability enjoyed, current pansharpening necessitates substantial effort in approximating the degradation matrices along the spatial and spectral dimensions, yet with performance hardly guaranteed within the complex scenarios. Moreover, as a key step during DUN update, current solutions rely solely on black-box networks to learn the data-driven priors, which further results in laborious architecture crafting and compromised interpretability. To counteract the dilemmas, we propose a new solution, namely RPCA-based Unfolding Network (RUN), which shrinks the original two degradations to only one. Specifically, grounded in the significant sparsity of spatial offset components, i.e., the difference between upsampled image and the desired target, we shift the original pansharpening issue into a novel Robust Principal Component Analysis (RPCA)-based paradigm. On that basis, the tricky approximation to the spatial degradation matrix as well as its transposed counterpart is naturally avoided. Specific for the prior learning step of RPCA unfolding, an efficient Nonlinear transformation-based Tensor Nuclear Norm (NTNN) is meticulously engineered, in which the computationally intensive Singular Value Decomposition is avoided with the aid of depthwise convolutions. More importantly, NTNN plays a plug-and-play role and can be easily embedded into Transformer/CNN architectures for the learning of both global and local features. Experimental results on multiple remote datasets demonstrate the superiority of the proposal over previous SOTA methods.
MVQA: Mamba with Unified Sampling for Efficient Video Quality Assessment
Authors: Yachun Mi; Yu Li; Weicheng Meng; Chaofeng Chen; Chen Hui; Shaohui Liu
Venue: ICCV 2025 (main)

Abstract: The rapid growth of long-duration, high-definition videos has made efficient video quality assessment (VQA) a critical challenge. Existing research typically tackles this problem through two main strategies: reducing model parameters and resampling inputs. However, light-weight Convolution Neural Networks (CNN) and Transformers often struggle to balance efficiency with high performance due to the requirement of long-range modeling capabilities. Recently, the state-space model, particularly Mamba, has emerged as a promising alternative, offering linear complexity with respect to sequence length. Meanwhile, efficient VQA heavily depends on resampling long sequences to minimize computational costs, yet current resampling methods are often weak in preserving essential semantic information. In this work, we present MVQA, a Mamba-based model designed for efficient VQA along with a novel Unified Semantic and Distortion Sampling (USDS) approach. USDS combines semantic patch sampling from low-resolution videos and distortion patch sampling from original-resolution videos. The former captures semantically dense regions, while the latter retains critical distortion details. To prevent computation increase from dual inputs, we propose a fusion mechanism using pre-defined masks, enabling a unified sampling strategy that captures both semantic and quality information without additional computational burden. Experiments show that the proposed MVQA, equipped with USDS, achieve comparable performance to state-of-the-art methods while being 2xas fast and requiring only 1/5 GPU memory. Code is available at https://github.com/xiao-mi-d/MVQA
Semantic-guided Camera Ray Regression for Visual Localization
Authors: Yesheng Zhang; Xu Zhao
Venue: ICCV 2025 (main)

Abstract: This work presents a novel framework for Visual Localization (VL), that is, regressing camera rays from query images to derive camera poses. As an overparameterized representation of the camera pose, camera rays possess superior robustness in optimization.Of particular importance, Camera Ray Regression (CRR) is privacy-preserving, rendering it a viable VL approach for real-world applications. Thus, we introduce DINO-based Multi-Mappers, coined DIMM, to achieve VL by CRR.DIMM utilizes DINO as a scene-agnostic encoder to obtain powerful features from images. To mitigate ambiguity, the features integrate both local and global perception, as well as potential geometric constraint. Then, a scene-specific mapper head regresses camera rays from these features. It incorporates a semantic attention module for soft fusion of multiple mappers, utilizing the rich semantic information in DINO features. In extensive experiments on both indoor and outdoor datasets, our methods showcase impressive performance, revealing a promising direction for advancements in VL.
HAMSt3R: Human-Aware Multi-view Stereo 3D Reconstruction
Authors: Sara Rojas; Matthieu Armando; Bernard Ghanem; Philippe Weinzaepfel; Vincent Leroy; Grégory Rogez
Venue: ICCV 2025 (main)

Abstract: Recovering the 3D geometry of a scene from a sparse set of uncalibrated images is a long-standing problem in computer vision. While recent learning-based approaches such as DUSt3R and MASt3R have demonstrated impressive results by directly predicting dense scene geometry, they are primarily trained on outdoor scenes with static environments and struggle to handle human-centric scenarios. In this work, we introduce HAMSt3R, an extension of MASt3R for joint human and scene 3D reconstruction from sparse, uncalibrated multi-view images. First, we exploit DUNE, a strong image encoder obtained by distilling, among others, the encoders from MASt3R and from a state-of-the-art Human Mesh Recovery (HMR) model, multi-HMR, for a better understanding of scene geometry and human bodies. Our method then incorporates additional network heads to segment people, estimate dense correspondences via DensePose, and predict depth in human-centric environments, enabling a more comprehensive 3D reconstruction. By leveraging the outputs of our different heads, HAMSt3R produces a dense point map enriched with human semantic information in 3D. Unlike existing methods that rely on complex optimization pipelines, our approach is fully feed-forward and efficient, making it suitable for real-world applications. We evaluate our model on EgoHumans and EgoExo4D, two challenging benchmarks containing diverse human-centric scenarios. Additionally, we validate its generalization to traditional multi-view stereo and multi-view pose regression tasks. Our results demonstrate that our method can reconstruct humans effectively while preserving strong performance in general 3D reconstruction tasks, bridging the gap between human and scene understanding in 3D vision.
TrajectoryCrafter: Redirecting Camera Trajectory for Monocular Videos via Diffusion Models
Authors: Mark Yu; Wenbo Hu; Jinbo Xing; Ying Shan
Venue: ICCV 2025 (main)

Abstract: We present TrajectoryCrafter, a novel approach to redirect camera trajectories for monocular videos. By disentangling deterministic view transformations from stochastic content generation, our method achieves precise control over user-specified camera trajectories. We propose a novel dual-stream conditional video diffusion model that concurrently integrates point cloud renders and source videos as conditions, ensuring accurate view transformations and coherent 4D content generation. Instead of leveraging scarce multi-view videos, we curate a hybrid training dataset combining web-scale monocular videos with static multi-view datasets, by our innovative double-reprojection strategy, significantly fostering robust generalization across diverse scenes. Extensive evaluations on multi-view and large-scale monocular videos demonstrate the superior performance of our method. Code and pre-trained model will be released.