GenAI4ELab/papercli-papers huggingface dataset:
QK-Edit: Revisiting Attention-based Injection in MM-DiT for Image and Video Editing
Authors: Tiancheng Shen; Zilong Huang; Xiangtai Li; Zhijie Lin; Jiyang Liu; Yitong Wang; Jiashi Feng; Ming-Hsuan Yang; Jun Hao Liew
Venue: ICCV 2025 (main)

Abstract: Multimodal Diffusion Transformers (MM-DiTs) have recently emerged as a powerful framework for unified text-vision synthesis, surpassing traditional U-Net architectures in generative tasks. One key innovation lies in its Multimodal Self-Attention (MM-SA) interaction where image and text tokens are concatenated and processed via self-attention.However, this mechanism poses significant challenges for editing, rendering conventional U-Net-based attention manipulation methods ineffective. To address this limitation, we propose QK-Edit, a training-free framework that exploits the unique attention dynamics of MM-DiTs for precise text-guided image and video editing. By introducing a novel query-key manipulation strategy, our method isolates and adjusts critical attention components to achieve an optimal balance between prompt fidelity and structural consistency. This enables seamless edits across various tasks, including object addition, object removal, object replacement, changing background, changing material, changing color, and style transformation. Notably, it can be easily implemented with feature replacement in inference.QK-Edit demonstrates superior editing performance on state-of-the-art models, such as FLUX and HunyuanVideo, effectively bridging the gap between generative power and editable flexibility in MM-DiTs, and paving the way for scalable multimodal content creation.
Visual Chronicles: Using Multimodal LLMs to Analyze Massive Collections of Images
Authors: Boyang Deng; Songyou Peng; Kyle Genova; Gordon Wetzstein; Noah Snavely; Leonidas Guibas; Thomas Funkhouser
Venue: ICCV 2025 (main)

Abstract: We present a system using Multimodal LLMs (MLLMs) to analyze a large database with tens of millions of images captured at different times, with the aim of discovering patterns in temporal changes. Specifically, we aim to capture frequent co-occurring changes ("trends") across a city over a certain period. Unlike previous visual analyses, our analysis answers open-ended queries (e.g., "what are the frequent types of changes in the city?") without any predetermined target subjects or training labels. These properties cast prior learning-based or unsupervised visual analysis tools unsuitable. We identify MLLMs as a novel tool for their open-ended semantic understanding capabilities. Yet, our datasets are four orders of magnitude too large for an MLLM to ingest as context. So we introduce a bottom-up procedure that decomposes the massive visual analysis problem into more tractable sub-problems. We carefully design MLLM-based solutions to each sub-problem. During experiments and ablation studies with our system, we find it significantly outperforms baselines and is able to discover interesting trends from images captured in large cities (e.g., "addition of outdoor dining,", "overpass was painted blue," etc.).
SAFT: Shape and Appearance of Fabrics from Template via Differentiable Physical Simulations from Monocular Video
Authors: David Stotko; Reinhard Klein
Venue: ICCV 2025 (main)

Abstract: The reconstruction of three-dimensional dynamic scenes is a well-established yet challenging task within the domain of computer vision. In this paper, we propose a novel approach that combines the domains of 3D geometry reconstruction and appearance estimation for physically based rendering and present a system that is able to perform both tasks for fabrics, utilizing only a single monocular RGB video sequence as input. In order to obtain realistic and high-quality deformations and renderings, a physical simulation of the cloth geometry and differentiable rendering are employed. In this paper, we introduce two novel regularization terms for the 3D reconstruction task that improve the plausibility of the reconstruction by addressing the depth ambiguity problem in monocular video. In comparison with the most recent methods in the field, we have reduced the error in the 3D reconstruction by a factor of 2.64 while requiring a medium runtime of 30 min per scene. Furthermore, the optimized motion achieves sufficient quality to perform an appearance estimation of the deforming object, recovering sharp details from this single monocular RGB video.
Gradient Short-Circuit: Efficient Out-of-Distribution Detection via Feature Intervention
Authors: Jiawei Gu; Ziyue Qiao; Zechao Li
Venue: ICCV 2025 (main)

Abstract: Out-of-Distribution (OOD) detection is critical for safely deploying deep models in open-world environments, where inputs may lie outside the training distribution. During inference on a model trained exclusively with In-Distribution (ID) data, we observe a salient gradient phenomenon: around an ID sample, the local gradient directions for "enhancing" that sample's predicted class remain relatively consistent, whereas OOD samples--unseen in training--exhibit disorganized or conflicting gradient directions in the same neighborhood. Motivated by this observation, we propose an inference-stage technique to short-circuit those feature coordinates that spurious gradients exploit to inflate OOD confidence, while leaving ID classification largely intact. To circumvent the expense of recomputing the logits after this gradient short-circuit, we further introduce a local first-order approximation that accurately captures the post-modification outputs without a second forward pass. Experiments on standard OOD benchmarks show our approach yields substantial improvements. Moreover, the method is lightweight and requires minimal changes to the standard inference pipeline, offering a practical path toward robust OOD detection in real-world applications.
AD-GS: Object-Aware B-Spline Gaussian Splatting for Self-Supervised Autonomous Driving
Authors: Jiawei Xu; Kai Deng; Zexin Fan; Shenlong Wang; Jin Xie; Jian Yang
Venue: ICCV 2025 (main)

Abstract: Modeling and rendering dynamic urban driving scenes is crucial for self-driving simulation. Current high-quality methods typically rely on costly manual object tracklet annotations, while self-supervised approaches fail to capture dynamic object motions accurately and decompose scenes properly, resulting in rendering artifacts. We introduce AD-GS, a novel self-supervised framework for high-quality free-viewpoint rendering of driving scenes from a single log. At its core is a novel learnable motion model that integrates locality-aware B-spline curves with global-aware trigonometric functions, enabling flexible yet precise dynamic object modeling. Rather than requiring comprehensive semantic labeling, AD-GS automatically segments scenes into objects and background with the simplified pseudo 2D segmentation, representing objects using dynamic Gaussians and bidirectional temporal visibility masks. Further, our model incorporates visibility reasoning and physically rigid regularization to enhance robustness. Extensive evaluations demonstrate that our annotation-free model significantly outperforms current state-of-the-art annotation-free methods and is competitive with annotation-dependent approaches.
HRScene: How Far Are VLMs from Effective High-Resolution Image Understanding?
Authors: Yusen Zhang; Wenliang Zheng; Aashrith Madasu; Peng Shi; Ryo Kamoi; Hao Zhou; Zhuoyang Zou; Shu Zhao; Sarkar Snigdha Sarathi Das; Vipul Gupta; Xiaoxin Lu; Nan Zhang; Ranran Haoran Zhang; Avitej Iyer; Renze Lou; Wenpeng Yin; Rui Zhang
Venue: ICCV 2025 (main)

Abstract: High-resolution image (HRI) understanding aims to process images with a large number of pixels, such as pathological images and agricultural aerial images, both of which can exceed 1 million pixels. Vision Large Language Models (VLMs) can allegedly handle HRIs, however, there is a lack of a comprehensive benchmark for VLMs to evaluate HRI understanding. To address this gap, we introduce HRScene, a novel unified benchmark for HRI understanding with rich scenes. HRScene incorporates 25 real-world datasets and 2 synthetic diagnostic datasets with resolutions ranging from 1,024 times 1,024 to 35,503 times 26,627. HRScene is collected and re-annotated by 10 graduate-level annotators, covering 25 scenarios, ranging from microscopic to radiology images, street views, long-range pictures, and telescope images. It includes HRIs of real-world objects, scanned documents, and composite multi-image. The two diagnostic evaluation datasets are synthesized by combining the target image with the gold answer and distracting images in different orders, assessing how well models utilize regions in HRI. We conduct extensive experiments involving 28 VLMs, including Gemini 2.0 Flash and GPT-4o. Experiments on HRScene show that current VLMs achieve an average accuracy of around 50% on real-world tasks, revealing significant gaps in HRI understanding. Results on synthetic datasets reveal that VLMs struggle to effectively utilize HRI regions, showing significant Regional Divergence and lost-in-middle, shedding light on future research.
Decoding Correlation-Induced Misalignment in the Stable Diffusion Workflow for Text-to-Image Generation
Authors: Yunze Tong; Fengda Zhang; Didi Zhu; Jun Xiao; Kun Kuang
Venue: ICCV 2025 (main)

Abstract: The fundamental requirement for text-to-image generation is aligning the generated images with the provided text. With large-scale data, pre-trained Stable Diffusion (SD) models have achieved remarkable performance in this task. These models process an input prompt as text control, guiding a vision model to perform denoising operations that recover a clean image from pure noise. However, we observe that when there is correlation among text tokens, SD's generated images fail to accurately represent the semantics of the input prompt: simple yet crucial objects may be omitted, thereby disrupting text-image alignment. We refer to this problem as "object omission". Without additional external knowledge, previous methods have been ineffective at addressing this issue. To investigate this problem, we analyze the attention maps in SD and find that biased text representations mislead the visual denoising process when handling correlated tokens, impeding object generation. Moreover, we observe that even when two prompts share the same semantics, slight variations in token sequence significantly alter attention scores, consequently affecting the final generated images. Based on these findings, we propose a simple yet effective fine-tuning method that applies decorrelation to the self-attention maps in the text module, thus reducing dependencies between tokens. Our approach requires no external prior knowledge, is straightforward to implement, and operates solely on the text module of the SD model. Extensive experiments confirm that our method effectively alleviates the object omission problem under text correlations, thereby enhancing text-image alignment.
CityNav: A Large-Scale Dataset for Real-World Aerial Navigation
Authors: Jungdae Lee; Taiki Miyanishi; Shuhei Kurita; Koya Sakamoto; Daichi Azuma; Yutaka Matsuo; Nakamasa Inoue
Venue: ICCV 2025 (main)

Abstract: Vision-and-language navigation (VLN) aims to develop agents capable of navigating in realistic environments. While recent cross-modal training approaches have significantly improved navigation performance in both indoor and outdoor scenarios, aerial navigation over real-world cities remains underexplored primarily due to limited datasets and the difficulty of integrating visual and geographic information. To fill this gap, we introduce CityNav, the first large-scale real-world dataset for aerial VLN. Our dataset consists of 32,637 human demonstration trajectories, each paired with a natural language description, covering 4.65 km2 across two real cities: Cambridge and Birmingham. In contrast to existing datasets composed of synthetic scenes such as AerialVLN, our dataset presents a unique challenge because agents must interpret spatial relationships between real-world landmarks and the navigation destination, making CityNav an essential benchmark for advancing aerial VLN. Furthermore, as an initial step toward addressing this challenge, we provide a methodology of creating geographic semantic maps that can be used as an auxiliary modality input during navigation. In our experiments, we compare performance of three representative aerial VLN agents (Seq2seq, CMA and AerialVLN models) and demonstrate that the semantic map representation significantly improves their navigation performance.
Neuromanifold-Regularized KANs for Shape-fair Feature Representations
Authors: Mazlum Ferhat Arslan; Weihong Guo; Shuo Li
Venue: ICCV 2025 (main)

Abstract: Traditional deep networks struggle to acquire shape-fair representations due to their high expressivity. Kolmogorov-Arnold Networks (KANs) are promising candidates as they learn nonlinearities directly, a property that makes them more adaptive. However, KANs perform suboptimally in terms of shape-fairness because of unconstrained nonlinearities, a limitation we demonstrate for the first time. On the other hand, shape-fair networks reside on a neuromanifold of low-degree. Motivated by this, we investigate neuromanifold regularization of KANs to enable learning of shape-fair feature representations. The proposed method, NeuroManifold Regularized-KANs, is a novel regularization that addresses failure modes during the acquisition of local and global shape cues, separately. This is done by constraining the degree of the neuromanifolds of two jointly trained feature extractors. Additionally, we propose a novel Style Decorrelation Loss that promotes decorrelation of intermediate representations. Our experiments demonstrate that NMR-KAN improves shape bias over baseline convolutional KANs by 14.8% while also providing robustness under image corruptions and adversarial attacks. Code is avaliable at: http://www.github.com/kaptres/NMR-KAN
Diffusion Guided Adaptive Augmentation for Generalization in Visual Reinforcement Learning
Authors: Jeong Woon Lee; Hyoseok Hwang
Venue: ICCV 2025 (main)

Abstract: Reinforcement learning (RL) has proven its potential in complex decision-making tasks. Yet, many RL systems rely on manually crafted state representations, requiring effort in feature engineering. Visual Reinforcement Learning (VRL) offers a way to address this challenge by enabling agents to learn directly from raw visual input. Nonetheless, VRL continues to face generalization issues, as models often overfit to specific domain features.To tackle this issue, we propose Diffusion Guided Adaptive Augmentation (DGA2), an augmentation method that utilizes Stable Diffusion to enhance domain diversity.We introduce an Adaptive Domain Shift strategy that dynamically adjusts the degree of domain shift according to the agent's learning progress for effective augmentation with Stable Diffusion.Additionally, we employ saliency as the mask to preserve the semantics of data.Our experiments on the DMControl-GB, Adroit, and Procgen environments demonstrate that DGA2 improves generalization performance compared to existing data augmentation and generalization methods.
Graph Domain Adaptation with Dual-branch Encoder and Two-level Alignment for Whole Slide Image-based Survival Prediction
Authors: Yuntao Shou; Xiangyong Cao; Peiqiang Yan; Qiao Hui; Qian Zhao; Deyu Meng
Venue: ICCV 2025 (main)

Abstract: In recent years, whole slide image (WSI)-based survival analysis has attracted much attention. In practice, WSIs usually come from different hospitals (or domains) and may have significant differences. These differences generally result in large gaps in distribution between different WSI domains and thus, the survival analysis models trained on one domain may fail to transfer to another. To address this issue, we propose a Dual-branch Encoder and Two-level Alignment (DETA) framework to explore both feature and category-level alignment between different WSI domains. Specifically, we first formulate the concerned problem as graph domain adaptation (GDA) using the graph representation of WSIs. Then, we construct a dual-branch graph encoder, including the message passing (MP) and the shortest path (SP) branches, to explicitly and implicitly extract semantic information from the graph-represented WSIs. To realize GDA, we propose a two-level alignment approach: at the category level, we develop a coupling technique by virtue of the dual-branch structure, leading to reduced divergence between the category distributions of the two domains; at the feature level, we introduce an adversarial perturbation strategy to better augment source domain feature, resulting in improved alignment in feature distribution. Extensive experiments have demonstrated the effectiveness of our proposed DETA framework in WSI-based survival analysis under the domain shift scenario.
Vision-Language Interactive Relation Mining for Open-Vocabulary Scene Graph Generation
Authors: Yukuan Min; Muli Yang; Jinhao Zhang; Yuxuan Wang; Aming Wu; Cheng Deng
Venue: ICCV 2025 (main)

Abstract: To promote the deployment of scenario understanding in the real world, Open-Vocabulary Scene Graph Generation (OV-SGG) has attracted much attention recently, aiming to generalize beyond the limited number of relation categories labeled during training and detect those unseen relations during inference. Towards OV-SGG, one feasible solution is to leverage the large-scale pre-trained vision-language models (VLMs) containing plentiful category-level content to capture accurate correspondences between images and text. However, due to the lack of quadratic relation-aware knowledge in VLMs, directly using the category-level correspondence in the base dataset could not sufficiently represent generalized relations involved in open world. Therefore, designing an effective open-vocabulary relation mining framework is challenging and meaningful. To this end, we propose a novel Vision-Language Interactive Relation Mining model (VL-IRM) for OV-SGG, which explores learning generalized relation-aware knowledge through multi-modal interaction. Specifically, first, to enhance the generalization of the relation text to visual content, we present a generative relation model to make the text modality explore possible open-ended relations based on visual content. Then, we employ visual modality to guide the relation text for spatial and semantic extension. Extensive experiments demonstrate the superior OV-SGG performance of our method.
FedAGC: Federated Continual Learning with Asymmetric Gradient Correction
Authors: Chengchao Zhang; Fanhua Shang; Hongying Liu; Liang Wan; Wei Feng
Venue: ICCV 2025 (main)

Abstract: Federated Continual Learning (FCL) has emerged as a prominent distributed learning paradigm and aims at addressing model learning challenges in both federated and continual learning settings. Efficient personalization in FCL remains a major challenge, as it must handle not only conflicts between old and new knowledge within parallel task streams but also heterogeneous knowledge conflicts from different clients. Recent approaches attempt to mitigate these issues through gradient correction. However, they often overlook the combined impact of gradient magnitude and direction, leading to unsatisfactory gradient solutions. To address these issues, we propose a novel federated continual learning method (called FedAGC) with asymmetric gradient correction, which performs memory rehearsal using representative samples selected via a centroid-based approach from historical tasks. By formulating the problem as a multi-objective optimization problem, FedAGC derives more effective gradients while incorporating group-level personalization to facilitate useful knowledge integration and irrelevant knowledge isolation, effectively mitigating both temporal and spatial catastrophic forgetting. Extensive experiments confirm the effectiveness of FedAGC.
Free-running vs Synchronous: Single-Photon Lidar for High-flux 3D Imaging
Authors: Ruangrawee Kitichotkul; Shashwath Bharadwaj; Joshua Rapp; Yanting Ma; Alexander Mehta; Vivek K Goyal
Venue: ICCV 2025 (main)

Abstract: Conventional wisdom suggests that single-photon lidar (SPL) should operate in low-light conditions to minimize dead-time effects.Many methods have been developed to mitigate these effects in synchronous SPL systems. However, solutions for free-running SPL remain limited despite the advantage of reduced histogram distortion from dead times.To improve the accuracy of free-running SPL, we propose a computationally efficient joint maximum likelihood estimator of the signal flux, the background flux, and the depth, along with a complementary regularization framework that incorporates a learned point cloud score model as a prior.Simulations and experiments demonstrate that free-running SPL yields lower estimation errors than its synchronous counterpart under identical conditions, with our regularization further improving accuracy.
Continuous-Time Human Motion Field from Event Cameras
Authors: Ziyun Wang; Ruijun Zhang; Zi-Yan Liu; Yufu Wang; Kostas Daniilidis
Venue: ICCV 2025 (main)

Abstract: This paper addresses the challenges of estimating a continuous-time field from a stream of events. Existing Human Mesh Recovery (HMR) methods rely predominantly on frame-based approaches, which are prone to aliasing and inaccuracies due to limited temporal resolution and motion blur. In this work, we predict a continuous-time human motion field from events caused by human motion. Prior state-of-the-art methods rely on computationally intensive optimization across a fixed number of poses at high frame rates, which becomes prohibitively expensive as we increase the temporal resolution. In comparison, our model leverages a recurrent feed-forward neural network to predict human motion in the latent space of possible human motions. We present the first work that replaces traditional event volume-based discrete-time pre-dictions with a continuous human motion field represented as a time-implicit function, enabling parallel pose queries at arbitrary temporal resolutions. To advance the evaluation of continuous-time human pose estimation, we introduce the Beam-splitter Event Agile Human Motion Dataset--a hardware-synchronized high-speed human dataset tailored for this purpose. EvHuman improves joint errors by 23.8 % compared to previous event human methods, while reducing the computational time by 69%.
MagicMirror: ID-Preserved Video Generation in Video Diffusion Transformers
Authors: Yuechen Zhang; Yaoyang Liu; Bin Xia; Bohao Peng; Zexin Yan; Eric Lo; Jiaya Jia
Venue: ICCV 2025 (main)

Abstract: We present MagicMirror, a framework for generating identity-preserved videos with cinematic-level quality and dynamic motion. While recent advances in video diffusion models have shown impressive capabilities in text-to-video generation, maintaining consistent identity while producing natural motion remains challenging. Previous methods either require person-specific fine-tuning or struggle to balance identity preservation with motion diversity. Built upon Video Diffusion Transformers, our method introduces three key components: (1) a dual-branch facial feature extractor that captures both identity and structural features, (2) a lightweight cross-modal adapter with Conditioned Adaptive Normalization for efficient identity integration, and (3) a two-stage training strategy combining synthetic identity pairs with video data. Extensive experiments demonstrate that MagicMirror effectively balances identity consistency with natural motion, outperforming existing methods across multiple metrics while requiring minimal parameters added. The code and model will be made publicly available.