GenAI4ELab/papercli-papers huggingface dataset:
Hybrid-Tower: Fine-grained Pseudo-query Interaction and Generation for Text-to-Video Retrieval
Authors: Bangxiang Lan; Ruobing Xie; Ruixiang Zhao; Xingwu Sun; Zhanhui Kang; Gang Yang; Xirong Li
Venue: ICCV 2025 (main)

Abstract: The Text-to-Video Retrieval (T2VR) task aims to retrieve unlabeled videos by textual queries with the same semantic meanings. Recent CLIP-based approaches have explored two frameworks: Two-Tower versus Single-Tower framework, yet the former suffers from low effectiveness, while the latter suffers from low efficiency. In this study, we explore a new Hybrid-Tower framework that can hybridize the advantages of the Two-Tower and Single-Tower framework, achieving high effectiveness and efficiency simultaneously. We propose a novel hybrid method, Fine-grained Pseudo-query Interaction and Generation for T2VR, i.e. \name  , which includes a new pseudo-query generator designed to generate a pseudo-query for each video. This enables the video feature and the textual features of pseudo-query to interact in a fine-grained manner, similar to the Single-Tower approaches to hold high effectiveness, even before the real textual query is received. Simultaneously, our method introduces no additional storage or computational overhead compared to the Two-Tower framework during the inference stage, thus maintaining high efficiency. Extensive experiments on five commonly used text-video retrieval benchmarks demonstrate that our method achieves a significant improvement over the baseline, with an increase of 1.6% ~ 3.9% in R@1. Furthermore, our method matches the efficiency of Two-Tower models while achieving near state-of-the-art performance, highlighting the advantages of the Hybrid-Tower framework.
ATLAS: Decoupling Skeletal and Shape Parameters for Expressive Parametric Human Modeling
Authors: Jinhyung Park; Javier Romero; Shunsuke Saito; Fabian Prada; Takaaki Shiratori; Yichen Xu; Federica Bogo; Shoou-I Yu; Kris Kitani; Rawal Khirodkar
Venue: ICCV 2025 (main)

Abstract: Parametric body models offer expressive 3D representation of humans across a wide range of poses, shapes, and facial expressions, typically derived by learning a basis over registered 3D meshes. However, existing human mesh modeling approaches struggle to capture detailed variations across diverse body poses and shapes, largely due to limited training data diversity and restrictive modeling assumptions. Moreover, the common paradigm first optimizes the external body surface using a linear basis, then regresses internal skeletal joints from surface vertices. This approach introduces problematic dependencies between internal skeleton and outer soft tissue, limiting direct control over body height and bone lengths. To address these issues, we present ATLAS, a high-fidelity body model learned from 600k high-resolution scans captured using 240 synchronized cameras. Unlike previous methods, we explicitly decouple the shape and skeleton bases by grounding our mesh representation in the human skeleton. This decoupling enables enhanced shape expressivity, fine-grained customization of body attributes, and keypoint fitting independent of external soft-tissue characteristics. ATLAS outperforms existing methods by fitting unseen subjects in diverse poses more accurately, and quantitative evaluations show that our non-linear pose correctives more effectively capture complex poses compared to linear models.
Enhancing Reward Models for High-quality Image Generation: Beyond Text-Image Alignment
Authors: Ying Ba; Tianyu Zhang; Yalong Bai; Wenyi Mo; Tao Liang; Bing Su; Ji-Rong Wen
Venue: ICCV 2025 (main)

Abstract: Contemporary image generation systems have achieved high fidelity and superior aesthetic quality beyond basic text-image alignment. However, existing evaluation frameworks have failed to evolve in parallel. This study reveals that human preference reward models fine-tuned based on CLIP and BLIP architectures have inherent flaws: they inappropriately assign low scores to images with rich details and high aesthetic value, creating a significant discrepancy with actual human aesthetic preferences. To address this issue, we design a novel evaluation score, ICT (Image-Contained-Text) score, that achieves and surpasses the objectives of text-image alignment by assessing the degree to which images represent textual content. Building upon this foundation, we further train a HP (High-Preference) score model using solely the image modality, aiming to enhance image quality in aspects such as aesthetics and detail refinement while maintaining achieved text-image alignment.Experiments demonstrate that the proposed evaluation model improves scoring accuracy by over 10% compared to existing methods, and achieves significant results in optimizing state-of-the-art text-to-image models. This research provides theoretical foundation and empirical support for the evolution of image generation technology toward better alignment with higher-order human aesthetic preferences.
PHATNet: A Physics-guided Haze Transfer Network for Domain-adaptive Real-world Image Dehazing
Authors: Fu-Jen Tsai; Yan-Tsung Peng; Yen-Yu Lin; Chia-Wen Lin
Venue: ICCV 2025 (main)

Abstract: Image dehazing aims to remove unwanted hazy artifacts in images. Although previous research has collected paired real-world hazy and haze-free images to improve dehazing models' performance in real-world scenarios, these models often experience significant performance drops when handling unseen real-world hazy images due to limited training data. This issue motivates us to develop a flexible domain adaptation method to enhance dehazing performance during testing. Observing that predicting haze patterns is generally easier than recovering clean content, we propose the Physics-guided Haze Transfer Network (PHATNet) which transfers haze patterns from unseen target domains to source-domain haze-free images, creating domain-specific fine-tuning sets to update dehazing models for effective domain adaptation. Additionally, we introduce a Haze-Transfer-Consistency loss and a Content-Leakage Loss to enhance PHATNet's disentanglement ability. Experimental results demonstrate that PHATNet significantly boosts state-of-the-art dehazing models on benchmark real-world image dehazing datasets.
Diagnosing Pretrained Models for Out-of-distribution Detection
Authors: Haipeng Xiong; Kai Xu; Angela Yao
Venue: ICCV 2025 (main)

Abstract: This work questions a common assumption of OOD detection, that models with higher in-distribution (ID) accuracy tend to have better OOD performance. Recent findings show this assumption doesn't always hold. A direct observation is that the later version of torchvision models improves ID accuracy but suffers from a significant drop in OOD performance. We systematically diagnose torchvision training recipes and explain this effect by analyzing the maximal logits of ID and OOD samples. We then propose post-hoc and training-time solutions to mitigate the OOD decrease by fixing problematic augmentations in torchvision recipes. Both solutions enhance OOD detection and maintain strong ID performance.
LD-RPS: Zero-Shot Unified Image Restoration via Latent Diffusion Recurrent Posterior Sampling
Authors: Huaqiu Li; Yong Wang; Tongwen Huang; Hailang Huang; Haoqian Wang; Xiangxiang Chu
Venue: ICCV 2025 (main)

Abstract: Unified image restoration is a significantly challenging task in low-level vision. Existing methods either make tailored designs for specific tasks, limiting their generalizability across various types of degradation, or rely on training with paired datasets, thereby suffering from closed-set constraints. To address these issues, we propose a novel, dataset-free, and unified approach through recurrent posterior sampling utilizing a pretrained latent diffusion model. Our method incorporates the multimodal understanding model to provide sematic priors for the generative model under a task-blind condition. Furthermore, it utilizes a lightweight module to align the degraded input with the generated preference of the diffusion model, and employs recurrent refinement for posterior sampling. Extensive experiments demonstrate that our method outperforms state-of-the-art methods, validating its effectiveness and robustness. Our code and data will be available at https://github.com/AMAP-ML/LD-RPS.
Outlier-Aware Post-Training Quantization for Image Super-Resolution
Authors: Hailing Wang; Jianglin Lu; Yitian Zhang; Yun Fu
Venue: ICCV 2025 (main)

Abstract: Quantization techniques, including quantization-aware training (QAT) and post-training quantization (PTQ), have become essential for inference acceleration of image super-resolution (SR) networks. Compared to QAT, PTQ has garnered significant attention as it eliminates the need for ground truth and model retraining. However, existing PTQ methods for SR often fail to achieve satisfactory performance as they overlook the impact of outliers in activation. Our empirical analysis reveals that these prevalent activation outliers are strongly correlated with image color information, and directly removing them leads to significant performance degradation. Motivated by this, we propose a dual-region quantization strategy that partitions activations into an outlier region and a dense region, applying uniform quantization to each region independently to better balance bit-width allocation. Furthermore, we observe that different network layers exhibit varying sensitivities to quantization, leading to different levels of performance degradation. To address this, we introduce sensitivity-aware finetuning that encourages the model to focus more on highly sensitive layers, further enhancing quantization performance. Extensive experiments demonstrate that our method outperforms existing PTQ approaches across various SR networks and datasets, while achieving performance comparable to QAT methods in most scenarios with at least a 75x speedup.
Equipping Vision Foundation Model with Mixture of Experts for Out-of-Distribution Detection
Authors: Shizhen  Zhao; Jiahui Liu; Xin Wen; Haoru Tan; Xiaojuan Qi
Venue: ICCV 2025 (main)

Abstract: Pre-trained vision foundation models have transformed many computer vision tasks. Despite their strong ability to learn discriminative and generalizable features crucial for out-of-distribution (OOD) detection, their impact on this task remains underexplored. Motivated by this gap, we systematically investigate representative vision foundation models for OOD detection. Our findings reveal that a pre-trained DINOv2 model, even without fine-tuning on in-domain (ID) data, naturally provides a highly discriminative feature space for OOD detection, achieving performance comparable to existing state-of-the-art methods without requiring complex designs. Beyond this, we explore how fine-tuning foundation models on in-domain (ID) data can enhance OOD detection. However, we observe that the performance of vision foundation models remains unsatisfactory in scenarios with a large semantic space. This is due to the increased complexity of decision boundaries as the number of categories grows, which complicates the optimization process. To mitigate this, we propose the Mixture of Feature Experts (MoFE) module, which partitions features into subspaces, effectively capturing complex data distributions and refining decision boundaries. Further, we introduce a Dynamic-b Mixup strategy, which samples interpolation weights from a dynamic beta distribution. This adapts to varying levels of learning difficulty across categories, improving feature learning for more challenging categories. Extensive experiments demonstrate the effectiveness of our approach, significantly outperforming baseline methods.
The Curse of Conditions: Analyzing and Improving Optimal Transport for Conditional Flow-Based Generation
Authors: Ho Kei Cheng; Alexander Schwing
Venue: ICCV 2025 (main)

Abstract: Minibatch optimal transport coupling straightens paths in unconditional flow matching. This leads to computationally less demanding inference as fewer integration steps and less complex numerical solvers can be employed when numerically solving an ordinary differential equation at test time. However, in the conditional setting, minibatch optimal transport falls short. This is because the default optimal transport mapping disregards conditions, resulting in a conditionally skewed prior distribution during training. In contrast, at test time, we have no access to the skewed prior, and instead sample from the full, unbiased prior distribution. This gap between training and testing leads to a subpar performance. To bridge this gap, we propose conditional optimal transport (C2OT) that adds a conditional weighting term in the cost matrix when computing the optimal transport assignment. Experiments demonstrate that this simple fix works with both discrete and continuous conditions in 8gaussians-to-moons, CIFAR-10, ImageNet-32x32, and ImageNet-256x256. Our method performs better overall compared to the existing baselines across different function evaluation budgets. Code is available at hkchengrex.github.io/C2OT
BlinkTrack: Feature Tracking over 80 FPS via Events and Images
Authors: Yichen Shen; Yijin Li; Shuo Chen; Guanglin Li; Zhaoyang Huang; Hujun Bao; Zhaopeng Cui; Guofeng Zhang
Venue: ICCV 2025 (main)

Abstract: Event cameras, known for their high temporal resolution and ability to capture asynchronous changes, have gained significant attention for their potential in feature tracking, especially in challenging conditions. However, event cameras lack the fine-grained texture information that conventional cameras provide, leading to error accumulation in tracking. To address this, we propose a novel framework, BlinkTrack, which integrates event data with grayscale images for high-frequency feature tracking. Our method extends the traditional Kalman filter into a learning-based framework, utilizing differentiable Kalman filters in both event and image branches. This approach improves single-modality tracking and effectively solves the data association and fusion from asynchronous event and image data. We also introduce new synthetic and augmented datasets to better evaluate our model. Experimental results indicate that BlinkTrack significantly outperforms existing methods, exceeding 80 FPS with multi-modality data and 100 FPS with preprocessed event data. Codes and dataset are available at https://github.com/ColieShen/BlinkTrack.
Less is More: Improving Motion Diffusion Models with Sparse Keyframes
Authors: Jinseok Bae; Inwoo Hwang; Young-Yoon Lee; Ziyu Guo; Joseph Liu; Yizhak Ben-Shabat; Young Min Kim; Mubbasir Kapadia
Venue: ICCV 2025 (main)

Abstract: Recent advances in motion diffusion models have led to remarkable progress in diverse motion generation tasks, including text-to-motion synthesis.However, existing approaches represent motions as dense frame sequences, requiring the model to process redundant or less informative frames.The processing of dense animation frames imposes significant training complexity, especially when learning intricate distributions of large motion datasets even with modern neural architectures. This severely limits the performance of generative motion models for downstream tasks.Inspired by professional animators who mainly focus on sparse keyframes, we propose a novel diffusion framework explicitly designed around sparse and geometrically meaningful keyframes.Our method reduces computation by masking non-keyframes and efficiently interpolating missing frames. We dynamically refine the keyframe mask during inference to prioritize informative frames in later diffusion steps.Extensive experiments show that our approach consistently outperforms state-of-the-art methods in text alignment and motion realism, while also effectively maintaining high performance at significantly fewer diffusion steps.We further validate the robustness of our framework by using it as a generative prior and adapting it to different downstream tasks.
CAVIS: Context-Aware Video Instance Segmentation
Authors: Seunghun Lee; Jiwan Seo; Kiljoon Han; Minwoo Choi; Sunghoon Im
Venue: ICCV 2025 (main)

Abstract: In this paper, we introduce the Context-Aware Video Instance Segmentation (CAVIS), a novel framework designed to enhance instance association by integrating contextual information adjacent to each object. To efficiently extract and leverage this information, we propose the Context-Aware Instance Tracker (CAIT), which merges contextual data surrounding the instances with the core instance features to improve tracking accuracy. Additionally, we design the Prototypical Cross-frame Contrastive (PCC) loss, which ensures consistency in object-level features across frames, thereby significantly enhancing instance matching accuracy. CAVIS demonstrates superior performance over state-of-the-art methods on all benchmark datasets in video instance segmentation (VIS) and video panoptic segmentation (VPS). Notably, our method excels on the OVIS dataset, which is known for its particularly challenging videos.
LeanVAE: An Ultra-Efficient Reconstruction VAE for Video Diffusion Models
Authors: Yu Cheng; Fajie Yuan
Venue: ICCV 2025 (main)

Abstract: Recent advances in Latent Video Diffusion Models (LVDMs) have revolutionized video generation by leveraging Video Variational Autoencoders (Video VAEs) to compress intricate video data into a compact latent space. However, as LVDM training scales, the computational overhead of Video VAEs becomes a critical bottleneck, particularly for encoding high-resolution videos. To address this, we propose LeanVAE, a novel and ultra-efficient Video VAE framework that introduce two key innovations: (1) a lightweight architecture based on a Neighborhood-Aware Feedforward (NAF) module and non-overlapping patch operations, drastically reducing computational cost, and (2) the integration of wavelet transforms and compressed sensing techniques to enhance reconstruction quality. Extensive experiments validate LeanVAE's superiority in video reconstruction and generation, particularly in enhancing efficiency over existing Video VAEs. Our model offers up to 50x fewer FLOPs and 44x faster inference speed while maintaining competitive reconstruction quality, providing insights for scalable, efficient video generation. Our models and code are available at https://github.com/westlake-repl/LeanVAE.
Monocular Semantic Scene Completion via Masked Recurrent Networks
Authors: Xuzhi Wang; Xinran Wu; Song Wang; Lingdong Kong; Ziping Zhao
Venue: ICCV 2025 (main)

Abstract: Monocular Semantic Scene Completion (MSSC) aims to predict the voxel-wise occupancy and semantic category from a single-view RGB image. Existing methods adopt a single-stage framework that aims to simultaneously achieve visible region segmentation and occluded region hallucination, while also being affected by inaccurate depth estimation. Such methods often achieve suboptimal performance, especially in complex scenes. We propose a novel two-stage framework that decomposes MSSC into coarse MSSC followed by the Masked Recurrent Network. Specifically, we propose the Masked Sparse Gated Recurrent Unit (MS-GRU) which concentrates on the occupied regions by the proposed mask updating mechanism, and a sparse GRU design is proposed to reduce the computation cost. Additionally, we propose the distance attention projection to reduce projection errors by assigning different attention scores according to the distance to the observed surface. Experimental results demonstrate that our proposed unified framework, MonoMRN, effectively supports both indoor and outdoor scenes and achieves state-of-the-art performance on the NYUv2 and SemanticKITTI datasets. Furthermore, we conduct robustness analysis under various disturbances, highlighting the role of the Masked Recurrent Network in enhancing the model's resilience to such challenges. The source code is publicly available at: https://github.com/alanWXZ/MonoMRN.
TurboReg: TurboClique for Robust and Efficient Point Cloud Registration
Authors: Shaocheng Yan; Pengcheng Shi; Zhenjun Zhao; Kaixin Wang; Kuang Cao; Ji Wu; Jiayuan Li
Venue: ICCV 2025 (main)

Abstract: Robust estimation is essential in correspondence-based Point Cloud Registration (PCR). Existing methods using maximal clique search in compatibility graphs achieve high recall but suffer from exponential time complexity, limiting their use in time-sensitive applications. To address this challenge, we propose a fast and robust estimator, TurboReg, built upon a novel lightweight clique, TurboClique, and a highly parallelizable Pivot-Guided Search (PGS) algorithm. First, we define the TurboClique as a 3-clique within a highly-constrained compatibility graph. The lightweight nature of the 3-clique allows for efficient parallel searching, and the highly-constrained compatibility graph ensures robust spatial consistency for stable transformation estimation. Next, PGS selects matching pairs with high SC^2 scores as pivots, effectively guiding the search toward TurboCliques with higher inlier ratios. Moreover, the PGS algorithm has linear time complexity and is significantly more efficient than the maximal clique search with exponential time complexity. Extensive experiments show that TurboReg achieves state-of-the-art performance across multiple real-world datasets, with substantial speed improvements. For example, on the 3DMatch+FCGF dataset, TurboReg (1K) operates 208.22xfaster than 3DMAC while also achieving higher recall. Our code is accessible at \href https://github.com/Laka-3DV/TurboReg  \texttt TurboReg  .
AR-VRM: Imitating Human Motions for Visual Robot Manipulation with Analogical Reasoning
Authors: Dejie Yang; Zijing Zhao; Yang Liu
Venue: ICCV 2025 (main)

Abstract: Visual Robot Manipulation (VRM) aims to enable a robot to follow natural language instructions based on robot states and visual observations, and therefore requires costly multi- modal data. To compensate for the deficiency of robot data, existing approaches have employed vision-language pre- training with large-scale data. However, they either utilize web data that differs from robotic tasks, or train the model in an implicit way (e.g., predicting future frames at the pixel level), thus showing limited generalization ability under in- sufficient robot data. In this paper, we propose to learn from large-scale human action video datasets in an explicit way (i.e., imitating human actions from hand keypoints), introduc- ing Visual Robot Manipulation with Analogical Reasoning (AR-VRM). To acquire action knowledge explicitly from hu- man action videos, we propose a keypoint Vision-Language Model (VLM) pretraining scheme, enabling the VLM to learn human action knowledge and directly predict human hand keypoints. During fine-tuning on robot data, to facilitate the robotic arm in imitating the action patterns of human motions, we first retrieve human action videos that perform similar manipulation tasks and have similar historical obser- vations, and then learn the Analogical Reasoning (AR) map between human hand keypoints and robot components. Tak- ing advantage of focusing on action keypoints instead of irrel- evant visual cues, our method achieves leading performance on the CALVIN benchmark and real-world experiments. In few-shot scenarios, our AR-VRM outperforms previous meth- ods by large margins, underscoring the effectiveness of explicitly imitating human actions under data scarcity. Code available at https://github.com/idejie/ar.