GenAI4ELab/papercli-papers huggingface dataset:
Dynamic Reconstruction of Hand-Object Interaction with Distributed Force-aware Contact Representation
Authors: Zhenjun Yu; Wenqiang Xu; Pengfei Xie; Yutong Li; Brian W. Anthony; Zhuorui Zhang; Cewu Lu
Venue: ICCV 2025 (main)

Abstract: We present ViTaM-D, a novel visual-tactile framework for reconstructing dynamic hand-object interaction with distributed tactile sensing to enhance contact modeling. Existing methods, relying solely on visual inputs, often fail to capture occluded interactions and object deformation. To address this, we introduce DF-Field, a distributed force-aware contact representation leveraging kinetic and potential energy in hand-object interactions. ViTaM-D first reconstructs interactions using a visual network with contact constraint, then refines contact details through force-aware optimization, improving object deformation modeling. To evaluate deformable object reconstruction, we introduce the HOT dataset, featuring 600 hand-object interaction sequences in a high-precision simulation environment. Experiments on DexYCB and HOT datasets show that ViTaM-D outperforms state-of-the-art methods in reconstruction accuracy for both rigid and deformable objects. DF-Field also proves more effective in refining hand poses and enhancing contact modeling than previous refinement methods. The code, models, and datasets are available at https://sites.google.com/view/vitam-d/.
CSD-VAR: Content-Style Decomposition in Visual Autoregressive Models
Authors: Quang-Binh Nguyen; Minh Luu; Quang Nguyen; Anh Tran; Khoi Nguyen
Venue: ICCV 2025 (main)

Abstract: Disentangling content and style from a single image, known as content-style decomposition (CSD), enables recontextualization of extracted content and stylization of extracted styles, offering greater creative flexibility in visual synthesis. While recent personalization methods have explored the decomposition of explicit content style, they remain tailored for diffusion models. Meanwhile, Visual Autoregressive Modeling (VAR) has emerged as a promising alternative with a next-scale prediction paradigm, achieving performance comparable to that of diffusion models. In this paper, we explore VAR as a generative framework for CSD, leveraging its scale-wise generation process for improved disentanglement. To this end, we propose CSD-VAR, a novel method that introduces three key innovations: (1) a scale-aware alternating optimization strategy that aligns content and style representation with their respective scales to enhance separation, (2) an SVD-based rectification method to mitigate content leakage into style representations, and (3) an Augmented Key-Value (K-V) memory enhancing content identity preservation. To benchmark this task, we introduce CSD-100, a dataset specifically designed for content-style decomposition, featuring diverse subjects rendered in various artistic styles. Experiments demonstrate that CSD-VAR outperforms prior approaches, achieving superior content preservation and stylization fidelity.
MSQ: Memory-Efficient Bit Sparsification Quantization
Authors: Seokho Han; Seoyeon Yoon; Jinhee Kim; Dongwei Wang; Kang Eun Jeon; Huanrui Yang; Jong Hwan Ko
Venue: ICCV 2025 (main)

Abstract: As deep neural networks (DNNs) see increased deployment on mobile and edge devices, optimizing model efficiency has become crucial. Mixed-precision quantization is widely favored, as it offers a superior balance between efficiency and accuracy compared to uniform quantization. However, finding the optimal precision for each layer is challenging. Recent studies utilizing bit-level sparsity have shown promise, yet they often introduce substantial training complexity and high GPU memory requirements. In this paper, we propose Memory-Efficient Bit Sparsification Quantization (MSQ), a novel approach that addresses these limitations. MSQ applies a round-clamp quantizer to enable differentiable computation of the least significant bits (LSBs) from model weights. It further employs regularization to induce sparsity in these LSBs, enabling effective precision reduction without explicit bit-level parameter splitting. Additionally, MSQ incorporates Hessian information, allowing the simultaneous pruning of multiple LSBs to further enhance training efficiency. Experimental results show that MSQ achieves up to 8.00x reduction in trainable parameters and up to 86% reduction in training time compared to previous bit-level quantization, while maintaining competitive accuracy and compression rates. This makes it a practical solution for training efficient DNNs on resource-constrained devices.
Disrupting Model Merging: A Parameter-Level Defense Without Sacrificing Accuracy
Authors: Wei Junhao; Yu Zhe; Jun Sakuma
Venue: ICCV 2025 (main)

Abstract: Model merging is a technique that combines multiple finetuned models into a single model without additional training, allowing a free-rider to cheaply inherit specialized capabilities. This study investigates methodologies to suppress unwanted model merging by free-riders. Existing methods such as model watermarking or fingerprinting can only detect merging in hindsight. In contrast, we propose a first proactive defense against model merging. Specifically, our defense method modifies the model parameters so that the model is disrupted if the model is merged with any other model, while its functionality is kept unchanged if not merged with others. Our approach consists of two modules, rearranging MLP parameters and scaling attention heads, which push the model out of the shared basin in parameter space, causing the merging performance with other models to degrade significantly. We conduct extensive experiments on image classification, image generation, and text classification to demonstrate that our defense severely disrupts merging while retaining the functionality of the post-protect model. Our code is available on Github: https://github.com/ISCT-W/PaRaMS
VSSD: Vision Mamba with Non-Causal State Space Duality
Authors: Yuheng Shi; Mingjia Li; Minjing Dong; Chang Xu
Venue: ICCV 2025 (main)

Abstract: Vision transformers have significantly advanced the field of computer vision, offering robust modeling capabilities and global receptive field. However, their high computational demands limit their applicability in processing long sequences. To tackle this issue, State Space Models (SSMs) have gained prominence in vision tasks as they offer linear computational complexity. Recently, State Space Duality (SSD), an improved variant of SSMs, was introduced in Mamba2 to enhance model performance and efficiency. However, the inherent causal nature of SSD/SSMs restricts their applications in non-causal vision tasks. To address this limitation, we introduce Visual State Space Duality (VSSD) model, which has a non-causal format of SSD. Specifically, we propose to discard the magnitude of interactions between the hidden state and tokens while preserving their relative weights, which relieves the dependencies of token contribution on previous tokens. Together with the involvement of multi-scan strategies, we show that the scanning results can be integrated to achieve non-causality, which not only improves the performance of SSD in vision tasks but also enhances its efficiency. We conduct extensive experiments on various benchmarks including image classification, detection, and segmentation, where VSSD surpasses existing state-of-the-art SSM-based models. Code is available at https://github.com/YuHengsss/VSSD.
Epipolar Consistent Attention Aggregation Network for Unsupervised Light Field Disparity Estimation
Authors: Chen Gao; Shuo Zhang; Youfang Lin
Venue: ICCV 2025 (main)

Abstract: Disparity estimation is an essential step in processing and analyzing Light Field (LF) images. Recent methods construct the cost volume to exploit the correspondence of the LFs over the preset maximum disparity, limiting them to process the large parallax scenes. Different from constructing cost volume, the self-attention mechanism calculates the parallax attention between epipolar lines to find the matching points. However, for LFs that have different views, the related disparity scales are different in parallax attention since the baselines with the central view are different. Moreover, if the matching information is occluded in one view, the disparity information can be explored through other views. Therefore, mapping these attentions to the same scale and selecting effective matching information are key points for disparity estimation from parallax attention. In this paper, we explore parallax attention for LF and design an unsupervised method, named Epipolar Consistent Attention Aggregation Network (ECAAN). We first introduce an epipolar consistent scale unification block by considering the consistency relationships to standardize disparity scales of the parallax attention maps. Based on the intra-properties and inter-relationships of parallax attention, we further propose a consistent occlusion-free aggregation block to integrate the information from the occlusion-free areas. Besides, we design an improved photometric loss to constrain the model. ECAAN achieves state-of-the-art performance in LF depth estimation. Notably, ECAAN attains a mean square error (MSE) of 0.2 on large-disparity LF datasets, achieving a 68% error reduction compared to the second-best method.
LLaVA-KD: A Framework of Distilling Multimodal Large Language Models
Authors: Yuxuan Cai; Jiangning Zhang; Haoyang He; Xinwei He; Ao Tong; Zhenye Gan; Chengjie Wang; Zhucun Xue; Yong Liu; Xiang Bai
Venue: ICCV 2025 (main)

Abstract: The success of Large Language Models (LLMs) has inspired the development of Multimodal Large Language Models (MLLMs) for unified understanding of vision and language. However, the increasing model size and computational complexity of large-scale MLLMs (l-MLLMs) limit their use in resource-constrained scenarios. Although small-scale MLLMs (s-MLLMs) are designed to reduce computational costs, they typically suffer from performance degradation.To mitigate this limitation, we propose a novel LLaVA-KD framework to transfer knowledge from l-MLLMs to s-MLLMs. Specifically, we introduce Multimodal Distillation (MDist) to transfer teacher model's robust representations across both visual and linguistic modalities, and Relation Distillation (RDist) to transfer teacher model's ability to capture visual token relationships.Additionally, we propose a three-stage training scheme to fully exploit the potential of the proposed distillation strategy: 1) Distilled Pre-Training to strengthen the alignment between visual-linguistic representations in s-MLLMs, 2) Supervised Fine-Tuning to equip the s-MLLMs with multimodal understanding capacity, and 3) Distilled Fine-Tuning to refine s-MLLM's knowledge.Our approach significantly improves s-MLLMs performance without altering the model architecture. Extensive experiments and ablation studies validate the effectiveness of each proposed component. Code will be available.
CLIP-GS: Unifying Vision-Language Representation with 3D Gaussian Splatting
Authors: Siyu Jiao; Haoye Dong; Yuyang Yin; Zequn Jie; Yinlong Qian; Yao Zhao; Humphrey Shi; Yunchao Wei
Venue: ICCV 2025 (main)

Abstract: Recent works in 3D representation learning and multimodal pre-training have made remarkable progress. However, typically multimodal 3D models are only capable of handling point clouds. Compared to the emerging 3D representation technique, 3D Gaussian Splatting (3DGS), the spatially sparse point cloud cannot depict the texture information of 3D objects, resulting in inferior reconstruction capabilities. This limitation constrains the potential of point cloud-based 3D multimodal representation learning. In this paper, we present CLIP-GS, a novel multimodal representation learning framework grounded in 3DGS. We introduce the GS Tokenizer to generate serialized gaussian tokens, which are then processed through a series of transformer layers pre-initialized with weights from point cloud models, resulting in the 3DGS embeddings. CLIP-GS leverages contrastive loss between 3DGS and the visual-text embeddings of CLIP, and we introduce an image voting loss to guide the directionality and convergence of gradient optimization. Furthermore, we develop an efficient way to generate triplets of 3DGS, images, and text, facilitating CLIP-GS in learning unified multimodal representations. Leveraging the well-aligned multimodal representations, CLIP-GS demonstrates versatility and outperforms point cloud-based models on various 3D tasks, including multimodal retrieval, zero-shot, and few-shot classification.
Region-Level Data Attribution for Text-to-Image Generative Models
Authors: Trong Bang Nguyen; Phi Le Nguyen; Simon Lucey; Minh Hoai
Venue: ICCV 2025 (main)

Abstract: Data attribution in text-to-image generative models is a crucial yet underexplored problem, particularly at the regional level, where identifying the most influential training regions for generated content can enhance transparency, copyright protection, and error diagnosis. Existing data attribution methods either operate at the whole-image level or lack scalability for large-scale generative models. In this work, we propose a novel framework for region-level data attribution. At its core is the Attribution Region (AR) detector, which localizes influential regions in training images used by the text-to-image generative model. To support this research, we construct a large-scale synthetic dataset with ground-truth region-level attributions, enabling both training and evaluation of our method. Empirical results show that our method outperforms existing attribution techniques in accurately tracing generated content back to training data. Additionally, we demonstrate practical applications, including identifying artifacts in generated images and suggesting improved replacements for generated content. Our dataset and framework will be released to advance further research in region-level data attribution for generative models.
TAPNext: Tracking Any Point (TAP) as Next Token Prediction
Authors: Artem Zholus; Carl Doersch; Yi Yang; Skanda Koppula; Viorica Patraucean; Xu Owen He; Ignacio Rocco; Mehdi S. M. Sajjadi; Sarath Chandar; Ross Goroshin
Venue: ICCV 2025 (main)

Abstract: Tracking Any Point (TAP) in a video is a challenging computer vision problem with many demonstrated applications in robotics, video editing, and 3D reconstruction. Existing methods for TAP rely heavily on complex tracking-specific inductive biases and heuristics, limiting their generality and potential for scaling. To address these challenges, we present TAPNext, a new approach that casts TAP as sequential masked token decoding. Our model is causal, tracks in a purely online fashion, and removes tracking-specific inductive biases. This enables TAPNext to run with minimal latency, and removes the temporal windowing required by many existing state of art trackers. Despite its simplicity, TAPNext achieves a new state-of-the-art tracking performance among both online and offline trackers. Finally, we present evidence that many widely used tracking heuristics emerge naturally in TAPNext through end-to-end training.
Learnable Feature Patches and Vectors for Boosting Low-light Image Enhancement without External Knowledge
Authors: Xiaogang Xu; Jiafei Wu; Qingsen Yan; Jiequan Cui; Richang Hong; Bei Yu
Venue: ICCV 2025 (main)

Abstract: A major challenge in Low-Light Image Enhancement (LLIE) is its ill-posed nature: low-light images often lack sufficient information to align with normal-light ones (e.g., not all training data can be fully fitted to the ground truth). Numerous studies have attempted to bridge the gap between low- and normal-light data by introducing effective additional information, which is called "references" in this paper. However, existing methods overlook the valuable references hidden within the training dataset itself. In this work, we propose a novel LLIE strategy that simultaneously learns image-specific features by neural networks while formulating effective common features from the training data as the reference. These common features are correlated with the samples that are not fully fitted by the LLIE network itself, and they are represented as a set of Learnable Feature Patches and Vectors (LFPVs) in the hidden feature space. LFPVs are updated through two mechanisms: the sample-updater, which extracts useful features from training samples to refine LFPVs, and the mutual-updater, which propagates information across LFPVs to mutually update them. LFPVs can be adaptively aligned with image-specific features via our designed query-and-fusion procedure, boosting the LLIE performance. Our proposed method can be integrated into any LLIE framework, improving both enhancement quality and downstream task performance. Extensive experiments on various benchmarks demonstrate the effectiveness of our approach.
Cycle Consistency as Reward: Learning Image-Text Alignment without Human Preferences
Authors: Hyojin Bahng; Caroline Chan; Fredo Durand; Phillip Isola
Venue: ICCV 2025 (main)

Abstract: Learning alignment between language and vision is a fundamental challenge, especially as multimodal data becomes increasingly detailed and complex. Existing methods often rely on collecting human or AI preferences, which can be costly and time-intensive. We propose an alternative approach that leverages cycle consistency as a supervisory signal. Given an image and generated text, we map the text back to image space using a text-to-image model and compute the similarity between the original image and its reconstruction. Analogously, for text-to-image generation, we measure the textual similarity between an input caption and its reconstruction through the cycle. We use the cycle consistency score to rank candidates and construct a preference dataset of 866K comparison pairs. The reward model trained on our dataset, CycleReward, outperforms state-of-the-art alignment metrics on detailed captioning, with superior inference-time scalability when used as a verifier for Best-of-N sampling, while maintaining speed and differentiability. Furthermore, performing DPO and Diffusion DPO using our dataset enhances performance across a wide range of vision-language tasks and text-to-image generation. Our dataset, model, and code are publicly released at https://cyclereward.github.io/.
Vid-Group: Temporal Video Grounding Pretraining from Unlabeled Videos in the Wild
Authors: Peijun Bao; Chenqi Kong; Siyuan Yang; Zihao Shao; Xinghao Jiang; Boon Poh Ng; Meng Hwa Er; Alex Kot
Venue: ICCV 2025 (main)

Abstract: Given a natural language query, temporal video grounding aims to localize the described temporal moment in an untrimmed video. A major challenge of this task is its heavy dependence on labor-intensive annotations for training. Unlike existing works that directly train models on manually curated data, we propose a novel paradigm to reduce annotation costs: pretraining the model on unlabeled, real-world videos. To support this, we introduce Temporal Video Grounding Pretraining (Vid-Group), a large-scale dataset collected in a scalable manner with minimal human intervention, consisting of over 50K videos captured in the wild and 200K pseudo annotations. Direct pretraining on these imperfect pseudo annotations, however, presents significant challenges, including mismatched sentence-video pairs and imprecise temporal boundaries. To address these issues, we propose the ReCorrect algorithm, which comprises two main phases: semantics-guided refinement and memory-consensus correction. The semantics-guided refinement enhances the pseudo labels by leveraging semantic similarity with video frames to clean out unpaired data and make initial adjustments to temporal boundaries. In the following memory-consensus correction phase, a memory bank tracks the model predictions, progressively correcting the temporal boundaries based on consensus within the memory. Comprehensive experiments demonstrate ReCorrect's strong generalization abilities across multiple downstream settings. The code, dataset, and pretrained models are available at https://github.com/baopj/Vid-Group.
BANet: Bilateral Aggregation Network for Mobile Stereo Matching
Authors: Gangwei Xu; Jiaxin Liu; Xianqi Wang; Junda Cheng; Yong Deng; Jinliang Zang; Yurui Chen; Xin Yang
Venue: ICCV 2025 (main)

Abstract: State-of-the-art stereo matching methods typically use costly 3D convolutions to aggregate a full cost volume, but their computational demands make mobile deployment challenging. Directly applying 2D convolutions for cost aggregation often results in edge blurring, detail loss, and mismatches in textureless regions. Some complex operations, like deformable convolutions and iterative warping, can partially alleviate this issue; however, they are not mobile-friendly, limiting their deployment on mobile devices. In this paper, we present a novel bilateral aggregation network (BANet) for mobile stereo matching that produces high-quality results with sharp edges and fine details using only 2D convolutions. Specifically, we first separate the full cost volume into detailed and smooth volumes using a spatial attention map, then perform detailed and smooth aggregations accordingly, ultimately fusing both to obtain the final disparity map. Experimental results demonstrate that our BANet-2D significantly outperforms other mobile-friendly methods, achieving 35.3% higher accuracy on the KITTI 2015 leaderboard than MobileStereoNet-2D, with faster runtime on mobile devices.
Diffuman4D: 4D Consistent Human View Synthesis from Sparse-View Videos with Spatio-Temporal Diffusion Models
Authors: Yudong Jin; Sida Peng; Xuan Wang; Tao Xie; Zhen Xu; Yifan Yang; Yujun Shen; Hujun Bao; Xiaowei Zhou
Venue: ICCV 2025 (main)

Abstract: This paper addresses the challenge of high-fidelity view synthesis of humans with sparse-view videos as input. Previous methods solve the issue of insufficient observation by leveraging 4D diffusion models to generate videos at novel viewpoints. However, the generated videos from these models often lack spatio-temporal consistency, thus degrading view synthesis quality. In this paper, we propose a novel sliding iterative denoising process to enhance the spatio-temporal consistency of the 4D diffusion model. Specifically, we define a latent grid in which each latent encodes the image, camera pose, and human pose for a certain viewpoint and timestamp, then alternately denoise the latent grid along spatial and temporal dimensions with a sliding window, and finally decode the videos at target viewpoints from the corresponding denoised latents. Through the iterative sliding, information flows sufficiently across the latent grid, allowing the diffusion model to obtain a large receptive field and thus enhance the 4D consistency of the output, while making the GPU memory consumption affordable. The experiments on the DNA-Rendering and ActorsHQ datasets demonstrate that our method is able to synthesize high-quality and consistent novel-view videos and significantly outperforms the existing approaches. See our project page for interactive demos and video results: https://diffuman4d.github.io/.