GenAI4ELab/papercli-papers huggingface dataset:
RareCLIP: Rarity-aware Online Zero-shot Industrial Anomaly Detection
Authors: Jianfang He; Min Cao; Silong Peng; Qiong Xie
Venue: ICCV 2025 (main)

Abstract: Large vision-language models such as CLIP have made significant strides in zero-shot anomaly detection through prompt engineering. However, most existing methods typically process each test image individually, ignoring the practical rarity of abnormal patches in real-world scenarios. Although some batch-based approaches exploit the rarity by processing multiple samples concurrently, they generally introduce unacceptable latency for real-time applications. To mitigate these limitations, we propose RareCLIP, a novel online zero-shot anomaly detection framework that enables sequential image processing in real-time without requiring prior knowledge of the target domain. RareCLIP capitalizes on the zero-shot capabilities of CLIP and integrates a dynamic test-time rarity estimation mechanism. A key innovation of our framework is the introduction of a prototype patch feature memory bank, which aggregates representative features from historical observations and continuously updates their corresponding rarity measures. For each incoming image patch, RareCLIP computes a rarity score by aggregating the rarity measures of its nearest neighbors within the memory bank. Moreover, we introduce a prototype sampling strategy based on dissimilarity to enhance computational efficiency, as well as a similarity calibration strategy to enhance the robustness of rarity estimation. Extensive experiments demonstrate that RareCLIP attains state-of-the-art performance with 98.2% image-level AUROC on MVTec AD and 94.4% on VisA, while achieving a latency of 59.4 ms. Code is available at https://github.com/hjf02/RareCLIP.
AdaDCP: Learning an Adapter with Discrete Cosine Prior for Clear-to-Adverse Domain Generalization
Authors: Qi Bi; Yixian Shen; Jingjun Yi; Gui-Song Xia
Venue: ICCV 2025 (main)

Abstract: Vision Foundation Model (VFM) provides an inherent generalization ability to unseen domains for downstream tasks. However, fine-tuning VFM to parse various adverse scenes (e.g., fog, snow, night) is particularly challenging, as these samples are difficult to collect. Using easy-to-acquire clear scenes as the source domain is a feasible solution, but a huge domain gap exists between clear and adverse scenes due to their dramatically different appearances. To address this challenge, this paper proposes AdaDCP, a VFM adapter with discrete cosine prior. The innovation originates from the observation that, the frequency components from a VFM exhibit either variant or invariant properties on adverse weather conditions after discrete cosine transform. Technically, the weather-invariant property learning preceives most of the scene content that is invariant to the adverse condition. The weather-variant property learning, in contrast, perceives the weather-specific information from different types of adverse conditions. Finally, the weather-invariant property alignment implicitly enforces the weather-variant components to incorporate the weather-invariant information, therefore mitigating the clear-to-adverse domain gap. Experiments conducted on eight unseen adverse scene segmentation datasets show its state-of-the-art performance.
HERMES: A Unified Self-Driving World Model for Simultaneous 3D Scene Understanding and Generation
Authors: Xin Zhou; Dingkang Liang; Sifan Tu; Xiwu Chen; Yikang Ding; Dingyuan Zhang; Feiyang Tan; Hengshuang Zhao; Xiang Bai
Venue: ICCV 2025 (main)

Abstract: Driving World Models (DWMs) have become essential for autonomous driving by enabling future scene prediction. However, existing DWMs are limited to scene generation and fail to incorporate scene understanding, which involves interpreting and reasoning about the driving environment. In this paper, we present a unified Driving World Model named HERMES. We seamlessly integrate 3D scene understanding and future scene evolution (generation) through a unified framework in driving scenarios. Specifically, HERMES leverages a Bird's-Eye View (BEV) representation to consolidate multi-view spatial information while preserving geometric relationships and interactions. We also introduce world queries, which incorporate world knowledge into BEV features via causal attention in the Large Language Model, enabling contextual enrichment for understanding and generation tasks. We conduct comprehensive studies on nuScenes and OmniDrive-nuScenes datasets to validate the effectiveness of our method. HERMES achieves state-of-the-art performance, reducing generation error by 32.4% and improving understanding metrics such as CIDEr by 8.0%. The model and code will be publicly released at https://github.com/LMD0311/HERMES.
ArgMatch: Adaptive Refinement Gathering for Efficient Dense Matching
Authors: Yuxin Deng; Kaining  Zhang; Linfeng  Tang; Jiaqi Yang; Jiayi Ma
Venue: ICCV 2025 (main)

Abstract: Establishing dense correspondences is crucial yet computationally demanding in multi-view tasks. Although coarse-to-fine schemes mitigate computational costs, their efficiency remains limited by the substantial demands of heavy feature extractors and global matchers. In this paper, we propose Adaptive Refinement Gathering, a refinement pipeline that reduces reliance on these costly components without sacrificing accuracy. The pipeline consists of (i) a content-aware offset estimator that leverages content information for lightweight correlation volume encoding and decoding; (ii) a locally consistent match rectifier robust to large global initial errors; (iii) a locally consistent upsampler that yields fewer artifacts around depth-discontinuous edges. Additionally, we introduce an adaptive gating strategy that, in conjunction with local consistency, dynamically modulates the contribution of different components and pixels. This enables adaptive gradient backpropagation and allows the network to fully exploit its capacity. Compared to the state-of-the-art, our lightweight network, termed ArgMatch, achieves competitive performance in serval tasks, while significantly reducing the computational cost. Codes are available in https://github.com/ACuOoOoO/argmatch.
Enhancing Image Restoration Transformer via Adaptive Translation Equivariance
Authors: JiaKui Hu; Zhengjian Yao; Lujia Jin; Hangzhou He; Yanye Lu
Venue: ICCV 2025 (main)

Abstract: Translation equivariance is a fundamental inductive bias in image restoration, ensuring that translated inputs produce translated outputs. Attention mechanisms in modern restoration transformers undermine this property, adversely impacting both training convergence and generalization. To alleviate this issue, we propose two key strategies for incorporating translation equivariance: slide indexing and component stacking. Slide indexing maintains operator responses at fixed positions, with sliding window attention being a notable example, while component stacking enables the arrangement of translation-equivariant operators in parallel or sequentially, thereby building complex architectures while preserving translation equivariance. However, these strategies still create a dilemma in model design between the high computational cost of self-attention and the fixed receptive field associated with sliding window attention. To address this, we develop an adaptive sliding indexing mechanism to efficiently select key-value pairs for each query, which are then concatenated in parallel with globally aggregated key-value pairs. The designed network, called the Translation Equivariance Adaptive Transformer (TEAFormer), is assessed across a variety of image restoration tasks. The results highlight its superiority in terms of effectiveness, training convergence, and generalization.
Free4D: Tuning-free 4D Scene Generation with Spatial-Temporal Consistency
Authors: Tianqi Liu; Zihao Huang; Zhaoxi Chen; Guangcong Wang; Shoukang Hu; Liao Shen; Huiqiang Sun; Zhiguo Cao; Wei Li; Ziwei Liu
Venue: ICCV 2025 (main)

Abstract: We present Free4D, a novel tuning-free framework for 4D scene generation from a single image. Existing methods either focus on object-level generation, making scene-level generation infeasible, or rely on large-scale multi-view video datasets for expensive training, with limited generalization ability due to the scarcity of 4D scene data. In contrast, our key insight is to distill pre-trained foundation models for consistent 4D scene representation, which offers promising advantages such as efficiency and generalizability. 1) To achieve this, we first animate the input image using image-to-video diffusion models followed by 4D geometric structure initialization. 2) To turn this coarse structure into spatial-temporal consistent multi-view videos, we design an adaptive guidance mechanism with a point-guided denoising strategy for spatial consistency and a novel latent replacement strategy for temporal coherence. 3) To lift these generated observations into consistent 4D representation, we propose a modulation-based refinement to mitigate inconsistencies while fully leveraging the generated information. The resulting 4D representation enables real-time, controllable rendering, marking a significant advancement in single-image-based 4D scene generation.
Generative Zoo
Authors: Tomasz Niewiadomski; Anastasios Yiannakidis; Hanz Cuevas-Velasquez; Soubhik Sanyal; Michael J. Black; Silvia Zuffi; Peter Kulits
Venue: ICCV 2025 (main)

Abstract: The model-based estimation of 3D animal pose and shape from images enables computational modeling of animal behavior. Training models for this purpose requires large amounts of labeled image data with precise pose and shape annotations. However, capturing such data requires the use of multi-view or marker-based motion-capture systems, which are impractical to adapt to wild animals in situ and impossible to scale across a comprehensive set of animal species. Some have attempted to address the challenge of procuring training data by pseudo-labeling individual real-world images through manual 2D annotation, followed by 3D-parameter optimization to those labels. While this approach may produce silhouette-aligned samples, the obtained pose and shape parameters are often implausible due to the ill-posed nature of the monocular fitting problem. Sidestepping real-world ambiguity, others have designed complex synthetic-data-generation pipelines leveraging game engines and collections of artist-designed 3D assets. Such engines yield perfect ground-truth labels but are often lacking in visual realism and require considerable manual effort to adapt to new species or environments. We propose an alternative approach to synthetic-data generation: rendering with a conditional image-generation model. We introduce a pipeline that samples a diverse set of poses and shapes for a variety of mammalian quadrupeds and generates realistic images with corresponding ground-truth pose and shape parameters. To demonstrate the scalability of our approach, we introduce GenZoo, a synthetic dataset containing one million images of distinct subjects. We train a 3D pose and shape regressor on GenZoo, which achieves state-of-the-art performance on a real-world multi-species 3D animal pose and shape estimation benchmark, despite being trained solely on synthetic data. We release our data and pipeline at https://genzoo.is.tue.mpg.de.
Any-SSR: How Recursive Least Squares Works in Continual Learning of Large Language Model
Authors: Kai Tong; Kang Pan; Xiao Zhang; Erli Meng; Run He; Yawen Cui; Nuoyan Guo; Huiping Zhuang
Venue: ICCV 2025 (main)

Abstract: Large Language Models (LLMs) possess encompassing capabilities that can process diverse language-related tasks. However, finetuning on LLMs will diminish this general skills and continual finetuning will further cause severe degradation on accumulated knowledge. Recently, Continual Learning (CL) in Large Language Models (LLMs) arises which aims to continually adapt the LLMs to new tasks while maintaining previously learned knowledge and inheriting general skills. Existing techniques either leverage previous data to replay, leading to extra computational costs, or utilize a single parameter-efficient module to learn the downstream task, constraining new knowledge absorption with interference between different tasks. Toward these issues, this paper proposes Analytic Subspace Routing(Any-SSR) to address these challenges. For each task, we isolate the learning within a subspace of deep layers' features via low-rank adaptation, eliminating knowledge interference between different tasks. Additionally, we propose an analytic routing mechanism to properly utilize knowledge learned in different subspaces. Our approach employs Recursive Least Squares to train a multi-task router model, allowing the router to dynamically adapt to incoming data without requiring access to historical data. Also, the router effectively assigns the current task to an appropriate subspace and has a non-forgetting property of previously learned tasks with a solid theoretical guarantee. Experimental results demonstrate that our method achieves near-perfect retention of prior knowledge while seamlessly integrating new information, effectively overcoming the core limitations of existing methods. Our code is available at https://github.com/ZHUANGHP/Any-SSR
Instruction-Oriented Preference Alignment for Enhancing Multi-Modal Comprehension Capability of MLLMs
Authors: Zitian Wang; Yue Liao; Kang Rong; Fengyun Rao; Yibo Yang; Si Liu
Venue: ICCV 2025 (main)

Abstract: Preference alignment has emerged as an effective strategy to enhance the performance of Multimodal Large Language Models (MLLMs) following supervised fine-tuning. While existing preference alignment methods predominantly target hallucination factors, they overlook the factors essential for multi-modal comprehension capabilities, often narrowing their improvements on hallucination mitigation. To bridge this gap, we propose Instruction-oriented Preference Alignment (IPA), a scalable framework designed to automatically construct alignment preferences grounded in instruction fulfillment efficacy. Our method involves an automated preference construction coupled with a dedicated verification process that identifies instruction-oriented factors, avoiding significant variability in response representations. Additionally, IPA incorporates a progressive preference collection pipeline, further recalling challenging samples through model self-evolution and reference-guided refinement. Experiments conducted on Qwen2VL-7B demonstrate IPA's effectiveness across multiple benchmarks, including hallucination evaluation, visual question answering, and text understanding tasks, highlighting its capability to enhance general comprehension.
RapVerse: Coherent Vocals and Whole-Body Motion Generation from Text
Authors: Jiaben Chen; Xin Yan; Yihang Chen; Siyuan Cen; Zixin Wang; Qinwei Ma; Haoyu Zhen; Kaizhi Qian; Lie Lu; Chuang Gan
Venue: ICCV 2025 (main)

Abstract: In this work, we introduce a challenging task for simultaneously generating 3D holistic body motions and singing vocals directly from textual lyrics inputs, advancing beyond existing works that typically address these two modalities in isolation. To facilitate this, we first collect the RapVerse dataset, a large dataset containing synchronous rapping vocals, lyrics, and high-quality 3D holistic body meshes. With the RapVerse dataset, we investigate the extent to which scaling autoregressive multimodal transformers across language, audio, and motion can enhance the coherent and realistic generation of vocals and whole-body human motions. For modality unification, a vector-quantized variational autoencoder is employed to encode whole-body motion sequences into discrete motion tokens, while a vocal-to-unit model is leveraged to obtain quantized audio tokens preserving content, prosodic information and singer identity. By jointly performing transformer modeling on these three modalities in a unified way, our framework ensures a seamless and realistic blend of vocals and human motions. Extensive experiments demonstrate that our unified generation framework not only produces coherent and realistic singing vocals alongside human motions directly from textual inputs, but also rivals the performance of specialized single-modality generation systems, establishing new benchmarks for joint vocal-motion generation.
MoFRR: Mixture of Diffusion Models for Face Retouching Restoration
Authors: Jiaxin Liu; Qichao Ying; Zhenxing Qian; Sheng Li; Runqi Zhang; Jian Liu; Xinpeng Zhang
Venue: ICCV 2025 (main)

Abstract: The widespread use of face retouching on social media platforms raises concerns about the authenticity of face images. While existing methods focus on detecting face retouching, how to accurately recover the original faces from the retouched ones has yet to be answered. This paper introduces Face Retouching Restoration (FRR), a novel computer vision task aimed at restoring original faces from their retouched counterparts. FRR differs from traditional image restoration tasks by addressing the complex retouching operations with various types and degrees, which focuses more on the restoration of the low-frequency information of the faces. To tackle this challenge, we propose MoFRR, Mixture of Diffusion Models for FRR. Inspired by DeepSeek's expert isolation strategy, the MoFRR uses sparse activation of specialized experts handling distinct retouching types and the engagement of a shared expert dealing with universal retouching traces. Each specialized expert follows a dual-branch structure with a DDIM-based low-frequency branch guided by an Iterative Distortion Evaluation Module (IDEM) and a Cross-Attention-based High-Frequency branch (HFCAM) for detail refinement. Extensive experiments on a newly constructed face retouching dataset, RetouchingFFHQ++, demonstrate the effectiveness of MoFRR for FRR.
SFUOD: Source-Free Unknown Object Detection
Authors: Keon-Hee Park; Seun-An Choe; Gyeong-Moon Park
Venue: ICCV 2025 (main)

Abstract: Source-free object detection adapts a detector pre-trained on a source domain to an unlabeled target domain without requiring access to labeled source data. While this setting is practical as it eliminates the need for the source dataset during domain adaptation, it operates under the restrictive assumption that only pre-defined objects from the source domain exist in the target domain. This closed-set setting prevents the detector from detecting undefined objects. To ease this assumption, we propose Source-Free Unknown Object Detection (SFUOD), a novel scenario which enables the detector to not only recognize known objects but also detect undefined objects as unknown objects. To this end, we propose CollaPAUL (Collaborative tuning and Principal Axis-based Unknown Labeling), a novel framework for SFUOD. Collaborative tuning enhances knowledge adaptation by integrating target-dependent knowledge from the auxiliary encoder with source-dependent knowledge from the pre-trained detector through a cross-domain attention mechanism. Additionally, principal axis-based unknown labeling assigns pseudo-labels to unknown objects by estimating objectness via principal axes projection and confidence scores from model predictions. The proposed CollaPAUL achieves state-of-the-art performances on SFUOD benchmarks, and extensive experiments validate its effectiveness. The code will be released after the review.
UniEgoMotion: A Unified Model for Egocentric Motion Reconstruction, Forecasting, and Generation
Authors: Chaitanya Patel; Hiroki Nakamura; Yuta Kyuragi; Kazuki Kozuka; Juan Carlos Niebles; Ehsan Adeli
Venue: ICCV 2025 (main)

Abstract: Egocentric human motion generation and forecasting with scene-context is crucial for enhancing AR/VR experiences, improving human-robot interaction, advancing assistive technologies, and enabling adaptive healthcare solutions by accurately predicting and simulating movement from a first-person perspective. However, existing methods primarily focus on third-person motion synthesis with structured 3D scene contexts, limiting their effectiveness in real-world egocentric settings where limited field of view, frequent occlusions, and dynamic cameras hinder scene perception. To bridge this gap, we introduce Egocentric Motion Generation and Egocentric Motion Forecasting, two novel tasks that utilize first-person images for scene-aware motion synthesis without relying on explicit 3D scene. We propose UniEgoMotion, a unified conditional motion diffusion model with a novel head-centric motion representation tailored for egocentric devices. UniEgoMotion's simple yet effective design supports egocentric motion reconstruction, forecasting, and generation from first-person visual inputs in a unified framework. Unlike previous works that overlook scene semantics, our model effectively extracts image-based scene context to infer plausible 3D motion. To facilitate training, we introduce EE4D-Motion, a large-scale dataset derived from EgoExo4D, augmented with pseudo-ground-truth 3D motion annotations. UniEgoMotion achieves state-of-the-art performance in egocentric motion reconstruction and is the first to generate motion from a single egocentric image. Extensive evaluations demonstrate the effectiveness of our unified framework, setting a new benchmark for egocentric motion modeling and unlocking new possibilities for egocentric applications.
ToolVQA: A Dataset for Multi-step Reasoning VQA with External Tools
Authors: Shaofeng Yin; Ting Lei; Yang Liu
Venue: ICCV 2025 (main)

Abstract: Integrating external tools into Large Foundation Models (LFMs) has emerged as a promising approach to enhance their problem-solving capabilities. While existing studies have demonstrated strong performance in tool-augmented Visual Question Answering (VQA), recent benchmarks re- veal significant gaps in real-world tool-use proficiency, particularly in functionally diverse multimodal settings re- quiring multi-step reasoning. In this work, we intro- duce ToolVQA, a large-scale multimodal dataset compris- ing 23K samples, designed to bridge this gap. Unlike pre- vious datasets that rely on synthetic scenarios and sim- plified queries, ToolVQA features real-world visual con- texts and challenging implicit multi-step reasoning tasks, better aligning with real user interactions. To construct this dataset, we propose ToolEngine, a novel data genera- tion pipeline that employs image-guided Depth-First Search (DFS) with a Longest Common Subsequence (LCS)-based example matching mechanism to simulate human-like tool- use reasoning. ToolVQA encompasses 10 multimodal tools across 7 diverse domains, with an average inference length of 2.78 reasoning steps per sample. The LLaVA-7B model fine-tuned on ToolVQA not only achieves impressive per- formance on the ToolVQA test set, but also surpasses the large closed-source model GPT-3.5-turbo on five out-of- distribution (OOD) datasets, showing strong generalizabil- ity in real-world tool-use scenarios. Code is available at https://github.com/Fugtemypt123/ToolVQA-release.