GenAI4ELab/papercli-papers huggingface dataset:
4D-Bench: Benchmarking Multi-modal Large Language Models for 4D Object Understanding
Authors: Wenxuan Zhu; Bing Li; Cheng Zheng; Jinjie Mai; Jun Chen; Letian Jiang; Abdullah Hamdi; Sara Rojas Martinez; Chia-Wen Lin; Mohamed Elhoseiny; Bernard Ghanem
Venue: ICCV 2025 (main)

Abstract: Multimodal Large Language Models (MLLMs) have demonstrated impressive 2D image/video understanding capabilities.However, there are no publicly standardized benchmarks to assess the abilities of MLLMs in understanding the 4D objects.In this paper, we introduce 4D-Bench, the first benchmark to evaluate the capabilities of MLLMs in 4D object understanding, featuring tasks in 4D object Question Answering (4D object QA) and 4D object captioning.4D-Bench provides 4D objects with diverse categories, high-quality annotations, and tasks necessitating multi-view spatial-temporal understanding, different from existing 2D image/video-based benchmarks.With 4D-Bench, we evaluate a wide range of open-source and closed-source MLLMs.The results from the 4D object captioning experiment indicate that MLLMs generally exhibit weaker temporal understanding compared to their appearance understanding, notably, while open-source models approach closed-source performance in appearance understanding, they show larger performance gaps in temporal understanding.4D object QA yields surprising findings: even with simple single-object videos, MLLMs perform poorly, with state-of-the-art GPT-4o achieving only 63% accuracy compared to the human baseline of 91%.These findings highlight a substantial gap in 4D object understanding and the need for further advancements in MLLMs.
How Do Optical Flow and Textual Prompts Collaborate to Assist in Audio-Visual Semantic Segmentation?
Authors: Yujian Lee; Peng Gao; Yongqi Xu; Wentao Fan
Venue: ICCV 2025 (main)

Abstract: Audio-visual semantic segmentation (AVSS) represents an extension of the audio-visual segmentation (AVS) task, necessitating a semantic understanding of audio-visual scenes beyond merely identifying sound-emitting objects at the visual pixel level. Contrary to a previous methodology, by decomposing the AVSS task into two discrete subtasks by initially providing a prompted segmentation mask to facilitate subsequent semantic analysis, our approach innovates on this foundational strategy. We introduce a novel collaborative framework, Stepping Stone Plus (SSP), which integrates optical flow and textual prompts to assist the segmentation process. In scenarios where sound sources frequently coexist with moving objects, our pre-mask technique leverages optical flow to capture motion dynamics, providing essential temporal context for precise segmentation. To address the challenge posed by stationary sound-emitting objects, such as alarm clocks, SSP incorporates two specific textual prompts: one identifies the category of the sound-emitting object, and the other provides a broader description of the scene. Additionally, we implement a visual-textual alignment module (VTA) to facilitate cross-modal integration, delivering more coherent and contextually relevant semantic interpretations. Our training regimen involves a post-mask technique aimed at compelling the model to learn the diagram of the optical flow. Experimental results demonstrate that SSP outperforms existing AVS methods, delivering efficient and precise segmentation results.
Devil is in the Uniformity: Exploring Diverse Learners within Transformer for Image Restoration
Authors: Shihao Zhou; Dayu Li; Jinshan Pan; Juncheng Zhou; Jinglei Shi; Jufeng Yang
Venue: ICCV 2025 (main)

Abstract: Transformer-based approaches have gained significant attention in image restoration, where the core component, i.e, Multi-Head Attention (MHA), plays a crucial role in capturing diverse features and recovering high-quality results. In MHA, heads perform attention calculation independently from uniform split subspaces, and a redundancy issue is triggered to hinder the model from achieving satisfactory outputs. In this paper, we propose to improve MHA by exploring diverse learners and introducing various interactions between heads, which results in a Hierarchical multI-head atteNtion driven Transformer model, termed HINT, for image restoration. HINT contains two modules, i.e., the Hierarchical Multi-Head Attention (HMHA) and the Query-Key Cache Updating (QKCU) module, to address the redundancy problem that is rooted in vanilla MHA. Specifically, HMHA extracts diverse contextual features by employing heads to learn from subspaces of varying sizes and containing different information. Moreover, QKCU, comprising intra- and inter-layer schemes, further reduces the redundancy problem by facilitating enhanced interactions between attention heads within and across layers. Extensive experiments are conducted on 12 benchmarks across 5 image restoration tasks, including low-light enhancement, dehazing, desnowing, denoising, and deraining, to demonstrate the superiority of HINT. The source code is available in the supplementary materials.
Epona: Autoregressive Diffusion World Model for Autonomous Driving
Authors: Kaiwen Zhang; Zhenyu Tang; Xiaotao Hu; Xingang Pan; Xiaoyang Guo; Yuan Liu; Jingwei Huang; Li Yuan; Qian Zhang; Xiao-Xiao Long; Xun Cao; Wei Yin
Venue: ICCV 2025 (main)

Abstract: Diffusion models have demonstrated exceptional visual quality in video generation, making them promising for autonomous driving world modeling. However, existing video diffusion-based world models struggle with flexible-length, long-horizon predictions and integrating trajectory planning. This is because conventional video diffusion models rely on global joint distribution modeling of fixed-length frame sequences rather than sequentially constructing localized distributions at each timestep. In this work, we propose Epona, an autoregressive diffusion world model that enables localized spatiotemporal distribution modeling through two key innovations: 1) Decoupled spatiotemporal factorization that separates temporal dynamics modeling from fine-grained future world generation, and 2) Modular trajectory and video prediction that seamlessly integrate motion planning with visual modeling in an end-to-end framework. Our architecture enables high-resolution, long-duration generation while introducing a novel chain-of-forward training strategy to address error accumulation in autoregressive loops. Experimental results demonstrate state-of-the-art performance with 7.4% FVD improvement and minutes-longer prediction duration compared to prior works. The learned world model further serves as a real-time motion planner, outperforming strong end-to-end planners on NAVSIM benchmarks.
Diffusion-based 3D Hand Motion Recovery with Intuitive Physics
Authors: Yufei Zhang; Zijun Cui; Jeffrey O. Kephart; Qiang Ji
Venue: ICCV 2025 (main)

Abstract: While 3D hand reconstruction from monocular images has made significant progress, generating accurate and temporally coherent motion estimates from videos remains challenging, particularly during hand-object interactions. In this paper, we present a novel 3D hand motion recovery framework that enhances image-based reconstructions through a diffusion-based and physics-augmented motion refinement model. Our model captures the distribution of refined motion estimates conditioned on initial ones, generating improved sequences through an iterative denoising process. Instead of relying on scarce annotated video data, we train our model only using motion capture data without images. We identify valuable intuitive physics knowledge during hand-object interactions, including key motion states and their associated motion constraints. We effectively integrate these physical insights into our diffusion model to improve its performance. Extensive experiments demonstrate that our approach significantly improves various frame-wise reconstruction methods, achieving state-of-the-art (SOTA) performance on existing benchmarks.
Visual Surface Wave Elastography: Revealing Subsurface Physical Properties via Visible Surface Waves
Authors: Alexander C. Ogren; Berthy T. Feng; Jihoon Ahn; Katherine L. Bouman; Chiara Daraio
Venue: ICCV 2025 (main)

Abstract: Wave propagation on the surface of a material contains information about physical properties beneath its surface. We propose a method for inferring the thickness and stiffness of a structure from just a video of waves on its surface. Our method works by extracting a dispersion relation from the video and then solving a physics-based optimization problem to find the best-fitting thickness and stiffness parameters. We validate our method on both simulated and real data, in both cases showing strong agreement with ground-truth measurements. Our technique provides a proof-of-concept for at-home health monitoring of medically-informative tissue properties, and it is further applicable to fields such as human-computer interaction.
Leveraging Spatial Invariance to Boost Adversarial Transferability
Authors: Zihan Zhou; Li Li; Yanli Ren; Chuan Qin; Guorui Feng
Venue: ICCV 2025 (main)

Abstract: Adversarial examples, crafted with imperceptible perturbations, reveal a significant vulnerability of Deep Neural Networks (DNNs). More critically, the transferability of adversarial examples allows attackers to induce unreasonable predictions without requiring knowledge about the target model. DNNs exhibit spatial invariance, meaning that the position of an object does not affect the classification result. However, existing input transformation-based adversarial attacks solely focus on behavioral patterns at a singular position, failing to fully exploit the spatial invariance exhibited by DNNs across multiple positions, thus constraining the transferability of adversarial examples. To address this, we propose a multi-scale, multi-position input transformation-based attack called Spatial Invariance Diversity (SID). Specifically, SID uses hybrid spatial-spectral fusion mechanisms within localized receptive fields, followed by multi-scale spatial downsampling and positional perturbations via random transformations, thereby crafting an ensemble of inputs to activate diverse behavioral patterns for effective adversarial perturbations. Extensive experiments on the ImageNet dataset demonstrate that SID could achieve better transferability than the current state-of-the-art input transformation-based attacks. Additionally, SID can be flexibly integrated with other input transformation-based or gradient-based attacks, further enhancing the transferability of adversarial examples. The code is available at https://github.com/TheMoss7/SID.
MoSiC: Optimal-Transport Motion Trajectory for Dense Self-Supervised Learning
Authors: Mohammadreza Salehi; Shashanka Venkataramanan; Ioana Simion; Efstratios Gavves; Cees G. M. Snoek; Yuki M Asano
Venue: ICCV 2025 (main)

Abstract: Dense self-supervised learning has shown great promise for learning pixel- and patch-level representations, but extending it to videos remains challenging due to the complexity of motion dynamics. Existing approaches struggle as they rely on static augmentations that fail under object deformations, occlusions, and camera movement, leading to inconsistent feature learning over time. We propose a motion-guided self-supervised learning framework that clus- ters dense point tracks to learn spatiotemporally consistent representations. By leveraging an off-the-shelf point tracker, we extract long-range motion trajectories and optimize feature clustering through a momentum-encoder-based optimal transport mechanism. To ensure temporal coherence, we propagate cluster assignments along tracked points, enforcing feature consistency across views despite viewpoint changes. Integrating motion as an implicit supervisory signal, our method learns representations that generalize across frames, improving robustness in dynamic scenes and challenging occlusion scenarios. By initializing from strong image-pretrained models and leveraging video data for training, we improve state-of-the-art by 1% to 6% on six image and video datasets and four evaluation benchmarks. The implementation is publicly available at our GitHub repository: github.com/SMSD75/MoSiC
GDKVM: Echocardiography Video Segmentation via Spatiotemporal Key-Value Memory with Gated Delta Rule
Authors: Rui Wang; Yimu Sun; Jingxing Guo; Huisi Wu; Jing Qin
Venue: ICCV 2025 (main)

Abstract: Accurate segmentation of cardiac chambers in echocardiography sequences is crucial for the quantitative analysis of cardiac function, aiding in clinical diagnosis and treatment. The imaging noise, artifacts, and the deformation and motion of the heart pose challenges to segmentation algorithms. While existing methods based on convolutional neural networks, Transformers and space-time memory networks, have improved segmentation accuracy, they often struggle with the trade-off between capturing long-range spatiotemporal dependencies and maintaining computational efficiency with fine-grained feature representation. In this paper, we introduce GDKVM, a novel architecture for echocardiography video segmentation. The model employs Linear Key-Value Association (LKVA) to effectively model inter-frame correlations, and introduces Gated Delta Rule (GDR) to efficiently store intermediate memory states. Key-Pixel Feature Fusion (KPFF) module is designed to integrate local and global features at multiple scales, enhancing robustness against boundary blurring and noise interference. We validated GDKVM on two mainstream echocardiography video datasets (CAMUS and EchoNet-Dynamic) and compared it with various state-of-the-art methods. Experimental results show that GDKVM outperforms existing approaches in terms of segmentation accuracy and robustness, while ensuring real-time performance. Codes are available at https://github.com/wangrui2025/GDKVM.
ViCTr: Vital Consistency Transfer for Pathology Aware Image Synthesis
Authors: Onkar Susladkar; Gayatri Deshmukh; Yalcin Tur; Gorkem Durak; Ulas Bagci
Venue: ICCV 2025 (main)

Abstract: We introduce ViCTr (Vital Consistency Transfer), a framework for advancing medical image synthesis through a principled integration with Rectified Flow trajectories. Unlike traditional approaches, we modify the Tweedie formulation to accommodate linear trajectories within the Rectified Flow framework, enabling more accurate initial state approximation and consistent trajectory paths. ViCTr's design allows for precise control over anatomical accuracy and pathological attributes across CT and MRI modalities via a two-stage architecture. In Stage 1, it performs anatomical learning on the ATLAS-8k dataset using Elastic Weight Consolidation (EWC) to selectively train model weights tailored for medical data. In Stage 2, an adversarial fine-tuning strategy is applied: the base model from Stage 1 remains frozen while a LoRA adapter is exclusively applied to the weights tuned in Stage 1, allowing targeted adaptation for downstream tasks while preserving the core medical data properties learned during pretraining. ViCTr achieves notable improvements by utilizing segmentation maps and textual prompts to enable refined control over CT and MRI synthesis. Extensive experiments on benchmark datasets, including BTCV, AMOS, and CirrMRI600+, demonstrate ViCTr's superiority, showing significant enhancements in quantitative metrics and clinical detail, such as liver surface nodularity in cirrhosis synthesis. These results establish ViCTr as a major advancement in medical image synthesis with impactful applications in data augmentation and clinical training.
DanceEditor: Towards Iterative Editable Music-driven Dance Generation with Open-Vocabulary Descriptions
Authors: Hengyuan Zhang; Zhe Li; Xingqun Qi; Mengze Li; Muyi Sun; Siye Wang; Man Zhang; Sirui Han
Venue: ICCV 2025 (main)

Abstract: Generating coherent and diverse human dances from music signals has gained tremendous progress in animating virtual avatars. While existing methods support direct dance synthesis, they fail to recognize that enabling users to edit dance movements is far more practical in real-world choreography scenarios. Moreover, the lack of high-quality dance datasets incorporating iterative editing also limits addressing this challenge. To achieve this goal, we first construct DanceRemix, a large-scale multi-turn editable dance dataset comprising the prompt featuring over 25.3M dance frames and 84.5K pairs. In addition, we propose a novel framework for iterative and editable dance generation coherently aligned with given music signals, namely DanceEditor. Considering the dance motion should be both musical rhythmic and enable iterative editing by user descriptions, our framework is built upon a prediction-then-editing paradigm unifying multi-modal conditions. At the initial prediction stage, our framework improves the authority of generated results by directly modeling dance movements from tailored, aligned music. Moreover, at the subsequent iterative editing stages, we incorporate text descriptions as conditioning information to draw the editable results through a specifically designed Cross-modality Edition Module (CEM). Specifically, CEM adaptively integrates the initial prediction with music and text prompts as temporal motion cues to guide the synthesized sequences.Thereby, the results display music harmony while preserving fine-grained semantic alignment with text descriptions. Extensive experiments demonstrate that our method outperforms the state-of-the-art models on our newly collected DanceRemix dataset. Code is available at https://lzvsdy.github.io/DanceEditor/.
When Pixel Difference Patterns Meet ViT: PiDiViT for Few-Shot Object Detection
Authors: Hongliang Zhou; Yongxiang Liu; Canyu Mo; Weijie Li; Bowen Peng; Li Liu
Venue: ICCV 2025 (main)

Abstract: Few-shot object detection aims to detect novel classes with limited samples. Recent methods have leveraged the rich semantic representations of pretrained vision transformer (ViT) to overcome the limitations of model fine-tuning, thereby improving the performance on novel classes. However, existing pretrained ViT schemes only perform transformer encoding in the feature dimension, ignoring the exploration of pixel-wise differences in low-level features and multiscale variations. The current challenges lie in: (i) the extracted features suffer from blurred boundary features and smooth transition from center to boundary, leading to insufficient distinction between objects and backgrounds, and (ii) how to balance the extraction of local details and global contour features under multiscale scenarios. So Pixel Difference Vision Transformer (PiDiViT) is proposed. Innovations include: (i) difference convolution fusion module (DCFM), which enhances the feature differences from object centers to boundaries and effectively preserves global information by fusing pixel-wise central difference features with original features through an attention mechanism, and (ii) multiscale feature fusion module (MFFM), which adaptively fuses features extracted by five different scale convolutional kernels using a scale attention mechanism to generate attention weights, achieving an optimal balance between local detail and global semantic information extraction. PiDiViT achieves SOTA on COCO benchmark: surpassing few-shot detection SOTA by 2.7 nAP50 (10-shot) and 4.0 nAP50 (30-shot) for novel classes, exceeding one-shot detection SOTA by 4.4 nAP50 and open-vocabulary detection SOTA by 3.7 nAP50. The code is available at https://github.com/Seaz9/PiDiViT.
AnimalClue: Recognizing Animals by their Traces
Authors: Risa Shinoda; Nakamasa Inoue; Iro Laina; Christian Rupprecht; Hirokatsu Kataoka
Venue: ICCV 2025 (main)

Abstract: Wildlife observation plays an important role in biodiversity conservation, necessitating robust methodologies for monitoring wildlife populations and interspecies interactions. Recent advances in computer vision have significantly contributed to automating fundamental wildlife observation tasks, such as animal detection and species identification. However, accurately identifying species from indirect evidence like footprints and feces remains relatively underexplored, despite its importance in contributing to wildlife monitoring. To bridge this gap, we introduce AnimalClue, the first large-scale dataset for species identification from images of indirect evidence. Our dataset consists of 159,605 bounding boxes encompassing five categories of indirect clues: footprints, feces, eggs, bones, and feathers. It covers 968 species, 200 families, and 65 orders. Each image is annotated with species-level labels, bounding boxes or segmentation masks, and fine-grained trait information, including activity patterns and habitat preferences. Unlike existing datasets primarily focused on direct visual features (e.g., animal appearances), AnimalClue presents unique challenges for classification, detection, and instance segmentation tasks due to the need for recognizing more detailed and subtle visual features. In our experiments, we extensively evaluate representative vision models and identify key challenges in animal identification from their traces. Our dataset and code are available at https://dahlian00.github.io/AnimalCluePage/.
From Objects to Events: Unlocking Complex Visual Understanding in Object Detectors via LLM-guided Symbolic Reasoning
Authors: Yuhui Zeng; Haoxiang Wu; Wenjie Nie; Guangyao Chen; Xiawu Zheng; Yunhang Shen; Jun Peng; Yonghong Tian; Rongrong Ji
Venue: ICCV 2025 (main)

Abstract: Current object detectors excel at entity localization and classification, yet exhibit inherent limitations in event recognition capabilities. This deficiency arises from their architecture's emphasis on discrete object identification rather than modeling the compositional reasoning, inter-object correlations, and contextual semantics essential for comprehensive event understanding. To address this challenge, we present a novel framework that expands the capability of standard object detectors beyond mere object recognition to complex event understanding through LLM-guided symbolic reasoning. Our key innovation lies in bridging the semantic gap between object detection and event understanding without requiring expensive task-specific training. The proposed plug-and-play framework interfaces with any open-vocabulary detector while extending their inherent capabilities across architectures. At its core, our approach combines (i) a symbolic regression mechanism exploring relationship patterns among detected entities and (ii) a LLM-guided strategically guiding the search toward meaningful expressions. These discovered symbolic rules transform low-level visual perception into interpretable event understanding, providing a transparent reasoning path from objects to events with strong transferability across domains.We compared our training-free framework against specialized event recognition systems across diverse application domains. Experiments demonstrate that our framework enhances multiple object detector architectures to recognize complex events such as illegal fishing activities (75 %AUROC, +8.36 %improvement), construction safety violations (+15.77%), and abnormal crowd behaviors (+23.16%).