GenAI4ELab/papercli-papers huggingface dataset:
ViLU: Learning Vision-Language Uncertainties for Failure Prediction
Authors: Marc Lafon; Yannis Karmim; Julio Silva-Rodríguez; Paul Couairon; Clément Rambour; Raphael Fournier-Sniehotta; Ismail Ben Ayed; Jose Dolz; Nicolas Thome
Venue: ICCV 2025 (main)

Abstract: Reliable Uncertainty Quantification (UQ) and failure prediction remain open challenges for Vision-Language Models (VLMs). We introduce ViLU, a new Vision-Language Uncertainty quantification framework that contextualizes uncertainty estimates by leveraging all task-relevant textual representations. ViLU constructs an uncertainty-aware multi-modal representation by integrating the visual embedding, the predicted textual embedding, and an image-conditioned textual representation via cross-attention. Unlike traditional UQ methods based on loss prediction, ViLU trains an uncertainty predictor as a binary classifier to distinguish correct from incorrect predictions using a weighted binary cross-entropy loss, making it loss-agnostic. In particular, our proposed approach is well-suited for post-hoc settings, where only vision and text embeddings are available without direct access to the model itself. Extensive experiments on diverse datasets show the significant gains of our method compared to state-of-the-art failure prediction methods. We apply our method to standard classification datasets, such as ImageNet-1k, as well as large-scale image-caption datasets like CC12M and LAION-400M. Ablation studies highlight the critical role of our architecture and training in achieving effective uncertainty quantification.
A Linear N-Point Solver for Structure and Motion from Asynchronous Tracks
Authors: Hang Su; Yunlong Feng; Daniel Gehrig; Panfeng Jiang; Ling Gao; Xavier Lagorce; Laurent Kneip
Venue: ICCV 2025 (main)

Abstract: Structure and continuous motion estimation from point correspondences is a fundamental problem in computer vision that has been powered by well-known algorithms such as the familiar 5-point or 8-point algorithm. However, despite their acclaim, these algorithms are limited to processing point correspondences originating from a pair of views each one representing an instantaneous capture of the scene. Yet, in the case of rolling shutter cameras, or more recently, event cameras, this synchronization breaks down. In this work, we present a unified approach for structure and linear motion estimation from 2D point correspondences with arbitrary timestamps, from an arbitrary set of views. By formulating the problem in terms of first-order dynamics and leveraging a constant velocity motion model, we derive a novel, linear point incidence relation allowing for the efficient recovery of both linear velocity and 3D points with predictable degeneracies and solution multiplicities. Owing to its general formulation, it can handle correspondences from a wide range of sensing modalities such as global shutter, rolling shutter, and event cameras, and can even combine correspondences from different collocated sensors. We validate the effectiveness of our solver on both simulated and real-world data, where we show consistent improvement across all modalities when compared to recent approaches. We believe our work opens the door to efficient structure and motion estimation from asynchronous data.
Constraint-Aware Feature Learning for Parametric Point Cloud
Authors: Xi Cheng; Ruiqi Lei; Di Huang; Zhichao Liao; Fengyuan Piao; Yan Chen; Pingfa Feng; Long Zeng
Venue: ICCV 2025 (main)

Abstract: Parametric point clouds are sampled from CAD shapes and are becoming increasingly common in industrial manufacturing. Most CAD-specific deep learning methods focus on geometric features, while overlooking constraints inherent in CAD shapes. This limits their ability to discern CAD shapes with similar appearances but different constraints. To tackle this challenge, we first analyze the constraint importance via simple validation experiments. Then, we introduce a deep learning-friendly constraint representation with three components, and design a constraint-aware feature learning network (CstNet), which includes two stages. Stage 1 extracts constraint representation from BRep data or point cloud based on local features. It enables better generalization ability to unseen dataset after pre-training. Stage 2 employs attention layers to adaptively adjust the weights of three constraints' components. It facilitates the effective utilization of constraints. In addition, we built the first multi-modal parametric-purpose dataset, i.e. Param20K, comprising about 20K CAD instances of 75 classes. On this dataset, CstNet achieved 3.49% (classification) and 26.17% (rotation robustness) accuracy improvements over the state-of-the-art. To the best of our knowledge, CstNet is the first constraint-aware deep learning method tailored for parametric point cloud analysis.
Unleashing Vecset Diffusion Model for Fast Shape Generation
Authors: Zeqiang Lai; Yunfei Zhao; Zibo Zhao; Haolin Liu; Fuyun Wang; Huiwen Shi; Xianghui Yang; Qingxiang Lin; Jingwei Huang; Yuhong Liu; Jie Jiang; Chunchao Guo; Xiangyu Yue
Venue: ICCV 2025 (main)

Abstract: 3D shape generation has greatly flourished through the development of so-called "native" 3D diffusion, particularly through the Vectset Diffusion Model (VDM). While recent advancements have shown promising results in generating high-resolution 3D shapes, VDM still struggles at high-speed generation. Challenges exist because of not only difficulties in accelerating diffusion sampling but also VAE decoding in VDM -- areas under-explored in previous works. To address these challenges, we present FlashVDM, a systematic framework for accelerating both VAE and DiT in VDM. For DiT, FlashVDM enables flexible diffusion sampling with as few as 5 inference steps, while maintaining comparable quality, which is made possible by stabilizing consistency distillation with our newly introduced Progressive Flow Distillation technique. For VAE, we introduce a lightning vectset decoder equipped with Adaptive KV Selection, Hierarchical Volume Decoding,, and Efficient Network Design. By exploiting the locality of vectset and the sparsity of shape surface in the volume, the proposed decoder drastically lowers FLOPs, minimizing the overall decoding overhead. We apply FlashVDM to the current state-of-the-art open-source shape generation model Hunyuan3D-2, resulting in Hunyuan3D-2 Turbo. Through systematic evaluation for both generation and reconstruction, we demonstrate that our model outperforms existing fast 3D generation methods by a significant margin, achieving comparable performance to the state-of-the-art models while reducing inference time by over 45x for reconstruction and 32x for generation. Code and models are publicly available at https://github.com/Tencent-Hunyuan/FlashVDM.
Revisiting Point Cloud Completion: Are We Ready For The Real-World?
Authors: Stuti Pathak; Prashant Kumar; Dheeraj Baiju; Nicholus Mboga; Gunther Steenackers; Rudi Penne
Venue: ICCV 2025 (main)

Abstract: Point clouds acquired in constrained, challenging, uncontrolled, and multi-sensor real-world settings are noisy, incomplete, and non-uniformly sparse. This presents acute challenges for the vital task of point cloud completion. Using tools from Algebraic Topology and Persistent Homology (PH), we demonstrate that current benchmark object point clouds lack rich topological features that are integral part of point clouds captured in realistic environments. To facilitate research in this direction, we contribute the first real-world industrial dataset for point cloud completion, RealPC - a diverse, rich and varied set of point clouds. It consists of   40,000 pairs across 21 categories of industrial structures in railway establishments. Benchmark results on several strong baselines reveal that existing methods fail in real-world scenarios. We discover a striking observation - unlike current datasets, RealPC consists of multiple 0- and 1-dimensional PH-based topological features. We prove that integrating these topological priors into existing works helps improve completion. We present how 0-dimensional PH priors extract the global topology of a complete shape in the form of a 3D skeleton and assist a model in generating topologically consistent complete shapes. Since computing Homology is expensive, we present a simple, yet effective Homology Sampler guided network, BOSHNet that bypasses the Homology computation by sampling proxy backbones akin to 0-dim PH. These backbones provide similar benefits of 0-dim PH right from the start of the training, unlike similar methods where accurate backbones are obtained only during later phases of the training.
RayZer: A Self-supervised Large View Synthesis Model
Authors: Hanwen Jiang; Hao Tan; Peng Wang; Haian Jin; Yue Zhao; Sai Bi; Kai Zhang; Fujun Luan; Kalyan Sunkavalli; Qixing Huang; Georgios Pavlakos
Venue: ICCV 2025 (main)

Abstract: We present RayZer, a self-supervised multi-view 3D Vision model trained without any 3D supervision, i.e., camera poses and scene geometry, while exhibiting emerging 3D awareness. Concretely, RayZer takes unposed and uncalibrated images as input, recovers camera parameters, reconstructs a scene representation, and synthesizes novel views. During training, RayZer relies solely on its self-predicted camera poses to render target views, eliminating the need for any ground-truth camera annotations and allowing RayZer to be trained with 2D image supervision. The emerging 3D awareness of RayZer is attributed to two key factors. First, we design a self-supervised framework, which achieves 3D-aware auto-encoding of input images by disentangling camera and scene representations. Second, we design a transformer-based model in which the only 3D prior is the ray structure, connecting camera, pixel, and scene simultaneously. RayZer demonstrates comparable or even superior novel view synthesis performance than "oracle" methods that rely on pose annotations in both training and testing.
Perception-as-Control: Fine-grained Controllable Image Animation with 3D-aware Motion Representation
Authors: Yingjie Chen; Yifang Men; Yuan Yao; Miaomiao Cui; Liefeng Bo
Venue: ICCV 2025 (main)

Abstract: Motion-controllable image animation is a fundamental task with a wide range of potential applications. Recent works have made progress in controlling camera or object motion via various motion representations, while they still struggle to support collaborative camera and object motion control with adaptive control granularity. To this end, we introduce 3D-aware motion representation and propose an image animation framework, called Perception-as-Control, to achieve fine-grained collaborative motion control. Specifically, we construct 3D-aware motion representation from a reference image, manipulate it based on interpreted user instructions, and perceive it from different viewpoints. In this way, camera and object motions are transformed into intuitive and consistent visual changes. Then, our framework leverages the perception results as motion control signals, enabling it to support various motion-related video synthesis tasks in a unified and flexible way. Experiments demonstrate the superiority of the proposed approach.
Structure-aware Semantic Discrepancy and Consistency for 3D Medical Image Self-supervised Learning
Authors: Tan Pan; Zhaorui Tan; Kaiyu Guo; Dongli Xu; Weidi Xu; Chen Jiang; Xin Guo; Yuan Qi; Yuan Cheng
Venue: ICCV 2025 (main)

Abstract: 3D medical image self-supervised learning (mSSL) holds great promise for medical analysis. Effectively supporting broader applications requires considering anatomical structure variations in location, scale, and morphology, which are crucial for capturing meaningful distinctions. However, previous mSSL methods partition images with fixed-size patches, often ignoring the structure variations. In this work, we introduce a novel perspective on 3D medical images with the goal of learning structure-aware representations. We assume that patches within the same structure share the same semantics (semantic consistency) while those from different structures exhibit distinct semantics (semantic discrepancy). Based on this assumption, we propose an mSSL framework named S2DC, achieving Structure-aware Semantic Discrepancy and Consistency in two steps. First, S2DC enforces distinct representations for different patches to increase semantic discrepancy by leveraging an optimal transport strategy. Second, S2DC advances semantic consistency at the structural level based on neighborhood similarity distribution. By bridging patch-level and structure-level representations, S2DC achieves structure-aware representations. Thoroughly evaluated across 10 datasets, 4 tasks, and 3 modalities, our proposed method consistently outperforms the state-of-the-art methods in mSSL. The code is available at https://github.com/Ashespt/S2DC.
Do It Yourself: Learning Semantic Correspondence from Pseudo-Labels
Authors: Olaf Dünkel; Thomas Wimmer; Christian Theobalt; Christian Rupprecht; Adam Kortylewski
Venue: ICCV 2025 (main)

Abstract: Finding correspondences between semantically similar points across images and object instances is one of the everlasting challenges in computer vision. While large pre-trained vision models have recently been demonstrated as effective priors for semantic matching, they still suffer from ambiguities for symmetric objects or repeated object parts. We propose improving semantic correspondence estimation through 3D-aware pseudo-labeling. Specifically, we train an adapter to refine off-the-shelf features using pseudo-labels obtained via 3D-aware chaining, filtering wrong labels through relaxed cyclic consistency, and 3D spherical prototype mapping constraints. While reducing the need for dataset-specific annotations compared to prior work, we establish a new state-of-the-art on SPair-71k, achieving an absolute gain of over 4% and of over 7% compared to methods with similar supervision requirements. The generality of our proposed approach simplifies the extension of training to other data sources, which we demonstrate in our experiments.
SA-LUT: Spatial Adaptive 4D Look-Up Table for Photorealistic Style Transfer
Authors: Zerui Gong; Zhonghua Wu; Qingyi Tao; Qinyue Li; Chen Change Loy
Venue: ICCV 2025 (main)

Abstract: Photorealistic style transfer (PST) enables real-world color grading by adapting reference image colors while preserving content structure.Existing methods mainly follow either approaches: generation-based methods that prioritize stylistic fidelity at the cost of content integrity and efficiency, or global color transformation methods such as LUT, which preserve structure but lack local adaptability. To bridge this gap, we propose Spatial Adaptive 4D Look-Up Table (SA-LUT), combining LUT efficiency with neural network adaptability. SA-LUT features: (1) a Style-guided 4D LUT Generator that extracts multi-scale features from the style image to predict a 4D LUT, and (2) a Context Generator using content-style cross-attention to produce a context map. This context map enables spatially-adaptive adjustments, allowing our 4D LUT to apply precise color transformations while preserving structural integrity. To establish a rigorous evaluation framework for photorealistic style transfer, we introduce PST50, the first benchmark specifically designed for PST assessment. Experiments demonstrate that SA-LUT substantially outperforms state-of-the-art methods, achieving a 66.7% reduction in LPIPS score compared to 3D LUT approaches, while maintaining real-time performance at 16 FPS for video stylization. Code and benchmark will be released.
DuET: Dual Incremental Object Detection via Exemplar-Free Task Arithmetic
Authors: Munish Monga; Vishal Chudasama; Pankaj Wasnik; Biplab Banerjee
Venue: ICCV 2025 (main)

Abstract: Real-world object detection systems, such as those in autonomous driving and surveillance, must continuously learn new object categories and simultaneously adapt to changing environmental conditions. Existing approaches, Class Incremental Object Detection (CIOD) and Domain Incremental Object Detection (DIOD)--only address one aspect of this challenge. CIOD struggles in unseen domains, while DIOD suffers from catastrophic forgetting when learning new classes, limiting their real-world applicability. To overcome these limitations, we introduce Dual Incremental Object Detection (DuIOD), a more practical setting that simultaneously handles class and domain shifts in an exemplar-free manner. We propose DuET, a Task Arithmetic-based model merging framework that enables stable incremental learning while mitigating sign conflicts through a novel Directional Consistency Loss. Unlike prior methods, DuET is detector-agnostic, allowing models like YOLO11 and RT-DETR to function as real-time incremental object detectors. To comprehensively evaluate both retention and adaptation, we introduce the Retention-Adaptability Index (RAI), which combines the Average Retention Index (Avg RI) for catastrophic forgetting and the Average Generalization Index for domain adaptability into a common ground. Extensive experiments on the Pascal Series and Diverse Weather Series demonstrate DuET's effectiveness, achieving a +13.12% RAI improvement while preserving 89.3% Avg RI on the Pascal Series (4 tasks), as well as a +11.39% RAI improvement with 88.57% Avg RI on the Diverse Weather Series (3 tasks), outperforming existing methods.
BATCLIP: Bimodal Online Test-Time Adaptation for CLIP
Authors: Sarthak Maharana; Baoming Zhang; Leonid Karlinsky; Rogerio Feris; Yunhui Guo
Venue: ICCV 2025 (main)

Abstract: Although open-vocabulary classification models like Contrastive Language Image Pretraining (CLIP) have demonstrated strong zero-shot learning capabilities, their robustness to common image corruptions remains poorly understood. Through extensive experiments, we show that zero-shot CLIP lacks robustness to common image corruptions during test-time, necessitating the adaptation of CLIP to unlabeled corrupted images using test-time adaptation (TTA). However, we found that existing TTA methods have severe limitations in adapting CLIP due to their unimodal nature. To address these limitations, we propose \texttt BATCLIP , a bimodal online TTA method designed to improve CLIP's robustness to common image corruptions. The key insight of our approach is not only to adapt the visual encoders for improving image features but also to strengthen the alignment between image and text features by promoting a stronger association between the image class prototype, computed using pseudo-labels, and the corresponding text feature. We evaluate our approach on benchmark image corruption datasets and achieve state-of-the-art results in online TTA for CLIP. Furthermore, we evaluate our proposed TTA approach on various domain generalization datasets to demonstrate its generalization capabilities. Our code is available at https://github.com/sarthaxxxxx/BATCLIP
MagShield: Towards Better Robustness in Sparse Inertial Motion Capture Under Magnetic Disturbances
Authors: Yunzhe Shao; Xinyu Yi; Lu Yin; Shihui Guo; Junhai Yong; Feng Xu
Venue: ICCV 2025 (main)

Abstract: This paper proposes a novel method, named MagShield, designed to address the issue of magnetic disturbances in sparse inertial motion capture (MoCap) systems. Existing Inertial Measurement Units (IMUs) are prone to orientation estimation errors in magnetically disturbed environments, limiting the practical application of inertial Mocap systems in real-world scenarios. To address this problem, MagShield employs a "detect-then-correct" strategy, first detecting magnetic disturbances through multi-IMU joint analysis, and then correcting orientation errors using human motion priors. MagShield can be ntegrated with most existing sparse inertial MoCap systems, improving their performance in magnetically disturbed environments. Experimental results demonstrate that MagShield significantly enhances the accuracy of motion capture under magnetic interference and exhibits good compatibility across different sparse inertial MoCap systems. Code and dataset are available at https://github.com/YZ-Shiao/MagShield.
SHIFT: Smoothing Hallucinations by Information Flow Tuning for Multimodal Large Language Models
Authors: Sudong Wang; Yunjian Zhang; Yao Zhu; Enci Liu; Jianing Li; Yanwei Liu; Xiangyang Ji
Venue: ICCV 2025 (main)

Abstract: Large Language Models (LLMs) are prone to hallucinations, which pose significant risks in their applications. Most existing hallucination detection methods rely on internal probabilities or external knowledge, and they are limited to identifying hallucinations at the sentence or passage level. In this paper, we introduce the first token-level, zero-resource hallucination detection framework, leveraging a novel approach inspired by the Mad Libs game. This method assesses the reliability of the input text by evaluating the consistency of information before and after the game. Building on this framework, we also propose an innovative automated hallucination generation technique and introduce a high-quality hallucination dataset, HalluWiki. Extensive experiments demonstrate that our approach achieves over 90% detection accuracy across different levels, establishing a new frontier in hallucination detection for LLMs.
Bias-Resilient Weakly Supervised Semantic Segmentation Using Normalizing Flows
Authors: Xianglin Qiu; Xiaoyang Wang; Zhen Zhang; Jimin Xiao
Venue: ICCV 2025 (main)

Abstract: Weakly supervised semantic segmentation (WSSS) aims to generate dense labels using sparse annotations, such as image-level labels. Existing class activation map (CAM) generation methods have been able to locate rough objects. However, due to the limited information provided by image level labels, the bias activation problem, including over-activation, becomes another key obstacle in WSSS. To rectify such bias activation, we attempt to mine pixel level class feature distribution information from the entire dataset. Specifically, we propose to use normalizing flow to model the class feature distribution of all pixels across the entire dataset and design a Bias-Resilient WSSS framework based on Normalizing Flow (BRNF). Normalizing flow has the ability to map complex distributions to normal distributions. Building upon it, we designed an additional Gaussian mixture classifier which classifies pixels from the perspective of feature distributions, providing supplementary information to the conventional MLP based classifier. In addition, we use this distribution to sample low bias features as positive anchors for contrastive learning, thereby encouraging feature optimization toward the correct low-bias direction. Experimental results demonstrate that our method significantly outperforms existing baselines, achieving state-of-the-art performance on WSSS benchmarks. Code will be available at https://github.com/DpDark/BRNF.