GenAI4ELab/papercli-papers huggingface dataset:
AnnofreeOD: Detecting All Classes at Low Frame Rates Without Human Annotations
Authors: Boyi Sun; Yuhang Liu; Houxin He; Yonglin Tian; Fei-Yue Wang
Venue: ICCV 2025 (main)

Abstract: Manual annotation of 3D bounding boxes in large-scale 3D scenes is expensive and time-consuming. This motivates the exploration of annotation-free 3D object detection using unlabeled point cloud data. Existing unsupervised 3D detection frameworks predominantly identify moving objects via scene flow, which has significant limitations: (1) limited detection classes (<=3), (2) difficulty in detecting stationary objects, and (3) reliance on high frame rates. To address these limitations, we propose AnnofreeOD, a novel Annotation-free Object Detection framework based on 2D-to-3D knowledge distillation. First, we explore an effective strategy to generate high-quality pseudo boxes using single-frame 2D knowledge. Second, we observe the noise from the previous step and introduce Noise-Resistant Regression (NRR) based on Box Augmentation (BA). AnnofreeOD achieves state-of-the-art performance across multiple experiments. On the nuScenes dataset, we established the first annotation-free 10-class object detection baseline, achieving 40% of fully supervised performance. Furthermore, in 3-class and class-agnostic object detection tasks, our approach surpasses prior state-of-the-art methods by +9.3% mAP (+12.2% NDS) and +6.0% AP (+4.2% NDS), significantly improving precision.
AIM: Adaptive Inference of Multi-Modal LLMs via Token Merging and Pruning
Authors: Yiwu Zhong; Zhuoming Liu; Yin Li; Liwei Wang
Venue: ICCV 2025 (main)

Abstract: Large language models (LLMs) have enabled the creation of multi-modal LLMs that exhibit strong comprehension of visual data such as images and videos. However, these models usually rely on extensive visual tokens from visual encoders, leading to high computational demands, which limits their applicability in resource-constrained environments and for long-context tasks. In this work, we propose a training-free adaptive inference method for multi-modal LLMs that can accommodate a broad range of efficiency requirements with a minimum performance drop. Our method consists of a) iterative token merging based on embedding similarity before LLMs, and b) progressive token pruning within LLM layers based on multi-modal importance. With a minimalist design, our method can be applied to both video and image LLMs. Extensive experiments on diverse video and image benchmarks demonstrate that our method substantially reduces computation load (e.g., a 7-fold reduction in FLOPs) while preserving the performance of video and image LLMs. Further, at a similar computational cost, our method outperforms the state-of-the-art methods in long video understanding (e.g., +4.6 on MLVU). Additionally, our in-depth analysis provides insights into token redundancy and LLM layer behaviors, offering guidance for future research in designing efficient multi-modal LLMs. Our code is available at https://github.com/LaVi-Lab/AIM.
PanSt3R: Multi-view Consistent Panoptic Segmentation
Authors: Lojze Zust; Yohann Cabon; Juliette Marrie; Leonid Antsfeld; Boris Chidlovskii; Jerome Revaud; Gabriela Csurka
Venue: ICCV 2025 (main)

Abstract: Panoptic segmentation in 3D is a fundamental problem in scene understanding. Existing approaches typically rely on costly test-time optimizations (often based on NeRF) to consolidate 2D predictions of off-the-shelf panoptic segmentation methods into 3D. Instead, in this work, we propose a unified and integrated approach PanSt3R, which eliminates the need for test-time optimization by jointly predicting 3D geometry and multi-view-consistent panoptic segmentation in a single forward pass. Our approach harnesses the 3D representations of MUSt3R, a recent scalable multi-view version of DUSt3R, and 2D representations of DINOv2, then performs joint multi-view panoptic prediction via a mask transformer architecture. We additionally revisit the standard post-processing mask merging procedure and introduce a more principled approach for multi-view segmentation. We also introduce a simple method for generating novel-view predictions based on the predictions of PanSt3R and vanilla 3DGS. Overall, the proposed PanSt3R is conceptually simple yet fast and scalable, and achieves state-of-the-art performance on several benchmarks, while being orders of magnitude faster.
G2SF: Geometry-Guided Score Fusion for Multimodal Industrial Anomaly Detection
Authors: Chengyu Tao; Xuanming Cao; Juan Du
Venue: ICCV 2025 (main)

Abstract: Industrial quality inspection plays a critical role in modern manufacturing by identifying defective products during production. While single-modality approaches using either 3D point clouds or 2D RGB images suffer from information incompleteness, multimodal anomaly detection offers promise through the complementary fusion of crossmodal data. However, existing methods face challenges in effectively integrating unimodal results and improving discriminative power. To address these limitations, we first reinterpret memory bank-based anomaly scores in single modalities as isotropic Euclidean distances in local feature spaces. Dynamically evolving from Euclidean metrics, we propose a novel \underline G eometry-\underline G uided \underline S core \underline F usion (G^ 2 SF) framework that progressively learns an anisotropic local distance metric as a unified score for the fusion task. Through a geometric encoding operator, a novel Local Scale Prediction Network (LSPN) is proposed to predict direction-aware scaling factors that characterize first-order local feature distributions, thereby enhancing discrimination between normal and anomalous patterns. Additionally, we develop specialized loss functions and score aggregation strategy from geometric priors to ensure both metric generalization and efficacy. Comprehensive evaluations on the MVTec-3D AD and Eyecandies datasets demonstrate the state-of-the-art detection performance of our method, and detailed ablation analysis validates each component's contribution. Our code is available at https://github.com/ctaoaa/G2SF.
SA-Occ: Satellite-Assisted 3D Occupancy Prediction in Real World
Authors: Chen Chen; Zhirui Wang; Taowei Sheng; Yi Jiang; Yundu Li; Peirui Cheng; Luning Zhang; Kaiqiang Chen; Yanfeng Hu; Xue Yang; Xian Sun
Venue: ICCV 2025 (main)

Abstract: Existing vision-based 3D occupancy prediction methods are inherently limited in accuracy due to their exclusive reliance on street-view imagery, neglecting the potential benefits of incorporating satellite views. We propose SA-Occ, the first Satellite-Assisted 3D occupancy prediction model, which leverages GPS & IMU to integrate historical yet readily available satellite imagery into real-time applications, effectively mitigating limitations of ego-vehicle perceptions, involving occlusions and degraded performance in distant regions. To address the core challenges of cross-view perception, we propose: 1) Dynamic-Decoupling Fusion, which resolves inconsistencies in dynamic regions caused by the temporal asynchrony between satellite and street views; 2) 3D-Proj Guidance, a module that enhances 3D feature extraction from inherently 2D satellite imagery; and 3) Uniform Sampling Alignment, which aligns the sampling density between street and satellite views. Evaluated on Occ3D-nuScenes, SA-Occ achieves state-of-the-art performance, especially among single-frame methods, with a 39.05% mIoU (a 6.97% improvement), while incurring only 6.93 ms of additional latency per frame.
Generic Event Boundary Detection via Denoising Diffusion
Authors: Jaejun Hwang; Dayoung Gong; Manjin Kim; Minsu Cho
Venue: ICCV 2025 (main)

Abstract: Generic event boundary detection (GEBD) aims to identify natural boundaries in a video, segmenting it into distinct and meaningful chunks. Despite the inherent subjectivity of event boundaries, previous methods have focused on deterministic predictions, overlooking the diversity of plausible solutions. In this paper, we introduce a novel diffusion-based boundary detection model, dubbed DiffGEBD, that tackles the problem of GEBD from a generative perspective. The proposed model encodes relevant changes across adjacent frames via temporal self-similarity and then iteratively decodes random noise into plausible event boundaries being conditioned on the encoded features. Classifier-free guidance allows the degree of diversity to be controlled in denoising diffusion. In addition, we introduce a new evaluation metric to assess the quality of predictions considering both diversity and fidelity. Experiments show that our method achieves strong performance on two standard benchmarks, Kinetics-GEBD and TAPOS, generating diverse and plausible event boundaries.
ImageGem: In-the-wild Generative Image Interaction Dataset for Generative Model Personalization
Authors: Yuanhe Guo; Linxi Xie; Zhuoran Chen; Kangrui Yu; Ryan Po; Guandao Yang; Gordon Wetzstein; Hongyi Wen
Venue: ICCV 2025 (main)

Abstract: We introduce ImageGem, a dataset for studying generative models that understand fine-grained individual preferences. We posit that a key challenge hindering the development of such a generative model is the lack of in-the-wild and fine-grained user preference annotations. Our dataset features real-world interaction data from 57K users, who collectively have built 242K customized LoRAs, written 3M text prompts, and created 5M generated images. With user preference annotations from our dataset, we were able to train better preference alignment models. In addition, leveraging individual user preference, we investigated the performance of retrieval models and a vision-language model on personalized image retrieval and generative model recommendation. Finally, we propose an end-to-end framework for editing customized diffusion models in a latent weight space to align with individual user preferences. Our results demonstrate that the ImageGem dataset enables, for the first time, a new paradigm for generative model personalization.
Implicit Counterfactual Learning for Audio-Visual Segmentation
Authors: Mingfeng Zha; Tianyu Li; Guoqing Wang; Peng Wang; Yangyang Wu; Yang Yang; Heng Tao Shen
Venue: ICCV 2025 (main)

Abstract: Audio-visual segmentation (AVS) aims to segment objects in videos based on audio cues. Existing AVS methods are primarily designed to enhance interaction efficiency but pay limited attention to modality representation discrepancies and imbalances. To overcome this, we propose the implicit counterfactual framework (ICF) to achieve unbiased cross-modal understanding. Due to the lack of semantics, heterogeneous representations may lead to erroneous matches, especially in complex scenes with ambiguous visual content or interference from multiple audio sources. We introduce the multi-granularity implicit text (MIT) involving video-, segment- and frame-level as the bridge to establish the modality-shared space, reducing modality gaps and providing prior guidance. Visual content carries more information and typically dominates, thereby marginalizing audio features in the decision-making. To mitigate knowledge preference, we propose the semantic counterfactual (SC) to learn orthogonal representations in the latent space, generating diverse counterfactual samples, thus avoiding biases introduced by complex functional designs and explicit modifications of text structures or attributes. We further formulate the collaborative distribution-aware contrastive learning (CDCL), incorporating factual-counterfactual and inter-modality contrasts to align representations, promoting cohesion and decoupling. Extensive experiments on three public datasets validate that the proposed method achieves state-of-the-art performance.
Context Guided Transformer Entropy Modeling for Video Compression
Authors: Junlong Tong; Wei Zhang; Yaohui Jin; Xiaoyu Shen
Venue: ICCV 2025 (main)

Abstract: Conditional entropy models effectively leverage spatio-temporal contexts to reduce video redundancy. However, incorporating temporal context often introduces additional model complexity and increases computational cost. In parallel, many existing spatial context models lack explicit modeling the ordering of spatial dependencies, which may limit the availability of relevant context during decoding. To address these issues, we propose the Context Guided Transformer (CGT) entropy model, which estimates probability mass functions of the current frame conditioned on resampled temporal context and dependency-weighted spatial context. A temporal context resampler learns predefined latent queries to extract critical temporal information using transformer encoders, reducing downstream computational overhead. Meanwhile, a teacher-student network is designed as dependency-weighted spatial context assigner to explicitly model the dependency of spatial context order. The teacher generates an attention map to represent token importance and an entropy map to reflect prediction certainty from randomly masked inputs, guiding the student to select the weighted top-k tokens with the highest spatial dependency. During inference, only the student is used to predict undecoded tokens based on high-dependency context. Experimental results demonstrate that our CGT model reduces entropy modeling time by approximately 65% and achieves an 11% BD-Rate reduction compared to the previous state-of-the-art conditional entropy model.
FlowEdit: Inversion-Free Text-Based Editing Using Pre-Trained Flow Models
Authors: Vladimir Kulikov; Matan Kleiner; Inbar Huberman-Spiegelglas; Tomer Michaeli
Venue: ICCV 2025 (main)

Abstract: Editing real images using a pre-trained text-to-image (T2I) diffusion/flow model often involves inverting the image into its corresponding noise map. However, inversion by itself is typically insufficient for obtaining satisfactory results, and therefore many methods additionally intervene in the sampling process. Such methods achieve improved results but are not seamlessly transferable between model architectures. Here, we introduce FlowEdit, a text-based editing method for pre-trained T2I flow models, which is inversion-free, optimization-free and model agnostic. Our method constructs an ODE that directly maps between the source and target distributions (corresponding to the source and target text prompts) and achieves a lower transport cost than the inversion approach. This leads to state-of-the-art results, as we illustrate with Stable Diffusion 3 and FLUX.
CMT: A Cascade MAR with Topology Predictor for Multimodal Conditional CAD Generation
Authors: Jianyu Wu; Yizhou Wang; Xiangyu Yue; Xinzhu Ma; Jinyang Guo; Dongzhan Zhou; Wanli Ouyang; Shixiang Tang
Venue: ICCV 2025 (main)

Abstract: While accurate and user-friendly Computer-Aided Design (CAD) is crucial for industrial design and manufacturing, existing methods still struggle to achieve this due to their over-simplified representations or architectures incapable of supporting multimodal design requirements. In this paper, we attempt to tackle this problem from both methods and datasets aspects. First, we propose a cascade MAR with topology predictor (CMT), the first multimodal framework for CAD generation based on Boundary Representation (B-Rep). Specifically, the cascade MAR can effectively capture the "edge-counters-surface" priors that are essential in B-Reps, while the topology predictor directly estimates topology in B-Reps from the compact tokens in MAR. Second, to facilitate large-scale training, we develop a large-scale multimodal CAD dataset, mmABC, which includes over 1.3 million B-Rep models with multimodal annotations, including point clouds, text descriptions, and multi-view images. Extensive experiments show the superior of CMT in both conditional and unconditional CAD generation tasks. For example, we improve Coverage and Valid ratio by +10.68% and +10.3%, respectively, compared to state-of-the-art methods on ABC in unconditional generation. CMT also improves +4.01 Chamfer on image conditioned CAD generation on mmABC.
CLIPSym: Delving into Symmetry Detection with CLIP
Authors: Tinghan Yang; Md Ashiqur Rahman; Raymond A. Yeh
Venue: ICCV 2025 (main)

Abstract: Symmetry is one of the most fundamental geometric cues in computer vision, and detecting it has been an ongoing challenge. With the recent advances in vision-language models, i.e., CLIP, we investigate whether a pre-trained CLIP model can aid symmetry detection by leveraging the additional symmetry cues found in the natural image descriptions. We propose CLIPSym, which leverages CLIP's image and language encoders and a rotation-equivariant decoder based on a hybrid of Transformer and G-Convolution to detect rotation and reflection symmetries. To fully utilize CLIP's language encoder, we have developed a novel prompting technique called Semantic-Aware Prompt Grouping (SAPG), which aggregates a diverse set of frequent object-based prompts to better integrate the semantic cues for symmetry detection. Empirically, we show that CLIPSym outperforms the current state-of-the-art on three standard symmetry detection datasets (DENDI, SDRW, and LDRS). Finally, we conduct detailed ablations verifying the benefits of CLIP's pre-training, the proposed equivariant decoder, and the SAPG technique.
MRGen: Segmentation Data Engine For Underrepresented MRI Modalities
Authors: Haoning Wu; Ziheng Zhao; Ya Zhang; Yanfeng Wang; Weidi Xie
Venue: ICCV 2025 (main)

Abstract: Training medical image segmentation models for rare yet clinically important imaging modalities is challenging due to the scarcity of annotated data, and manual mask annotations can be costly and labor-intensive to acquire. This paper investigates leveraging generative models to synthesize data, for training segmentation models for underrepresented modalities, particularly on annotation-scarce MRI. Concretely, our contributions are threefold: (i) we introduce MRGen-DB, a large-scale radiology image-text dataset comprising extensive samples with rich metadata, including modality labels, attributes, regions, and organs information, with a subset featuring pixel-wise mask annotations; (ii) we present MRGen, a diffusion-based data engine for controllable medical image synthesis, conditioned on text prompts and segmentation masks. MRGen can generate realistic images for diverse MRI modalities lacking mask annotations, facilitating segmentation training in low-source domains; (iii) extensive experiments across multiple modalities demonstrate that MRGen significantly improves segmentation performance on unannotated modalities by providing high-quality synthetic data. We believe that our method bridges a critical gap in medical image analysis, extending segmentation capabilities to scenarios that are challenging to acquire manual annotations. The codes, models, and data will be publicly available at: https://haoningwu3639.github.io/MRGen/
PrimHOI: Compositional Human-Object Interaction via Reusable Primitives
Authors: Kai Jia; Tengyu Liu; Mingtao Pei; Yixin Zhu; Siyuan Huang
Venue: ICCV 2025 (main)

Abstract: Synthesizing realistic Human-Object Interaction (HOI) motions is essential for creating believable digital characters and intelligent robots. Existing approaches rely on data-intensive learning models that struggle with the compositional structure of daily HOI motions, particularly for complex multi-object manipulation tasks. The exponential growth of possible interaction scenarios makes comprehensive data collection prohibitively expensive. The fundamental challenge is synthesizing unseen, complex HOI sequences without extensive task-specific training data. Here we show that PrimHOI generates complex HOI motions through spatial and temporal composition of generalizable interaction primitives defined by relative geometry. Our approach demonstrates that repetitive local contact patterns -- grasping, clamping, and supporting -- serve as reusable building blocks for diverse interaction sequences. Unlike previous data-driven methods requiring end-to-end training for each task variant, PrimHOI achieves zero-shot transfer to unseen scenarios through hierarchical primitive planning. Experimental validation demonstrates substantial improvements in adaptability, diversity, and motion quality compared to existing approaches.
Nautilus: Locality-aware Autoencoder for Scalable Mesh Generation
Authors: Yuxuan Wang; Xuanyu Yi; Haohan Weng; Qingshan Xu; Xiaokang Wei; Xianghui Yang; Chunchao Guo; Long Chen; Hanwang Zhang
Venue: ICCV 2025 (main)

Abstract: Triangle meshes are fundamental to 3D applications. Current automatic mesh generation methods typically rely on intermediate representations that lack the continuous surface quality inherent to meshes. Converting these representations into meshes produces dense, suboptimal outputs. Although recent autoregressive approaches demonstrate promise in directly modeling mesh vertices and faces, they are constrained by the limitation in face count, scalability, and structural fidelity.To address these challenges, we propose Nautilus, a locality-aware autoencoder for artist-like mesh generation that leverages the local properties of manifold meshes to achieve structural fidelity and efficient representation. Our approach introduces a novel tokenization algorithm that preserves face proximity relationships and compresses sequence length through locally shared vertices and edges, enabling the generation of meshes with an unprecedented scale of up to 5,000 faces. Furthermore, we develop a Dual-stream Point Conditioner that captures fine-grained geometry, ensuring global consistency and local structural fidelity. Our experiments demonstrate that Nautilus significantly outperforms existing methods in generation quality.
What Changed and What Could Have Changed? State-Change Counterfactuals for Procedure-Aware Video Representation Learning
Authors: Chi-Hsi Kung; Frangil Ramirez; Juhyung Ha; Yi-Ting Chen; David Crandall; Yi-Hsuan Tsai
Venue: ICCV 2025 (main)

Abstract: Understanding a procedural activity requires modeling both how action steps transform the scene, and how evolving scene transformations can influence the sequence of action steps, even those that are accidental or erroneous. Existing work has studied procedure-aware video representations by modeling the temporal order of actions, but has not explicitly learned the state changes (scene transformations). In this work, we study procedure-aware video representation learning by incorporating state-change descriptions generated by Large Language Models (LLMs) as supervision signals for video encoders. Moreover, we generate state-change counterfactuals that simulate hypothesized failure outcomes, allowing models to learn by imagining unseen "What if" scenarios. This counterfactual reasoning facilitates the model's ability to understand the cause and effect of each step in an activity. We conduct extensive experiments on procedure-aware tasks, including temporal action segmentation, error detection, action phase classification, frame retrieval, multi-instance retrieval, and action recognition. Our results demonstrate the effectiveness of the proposed state-change descriptions and their counterfactuals, and achieve significant improvements on multiple tasks. Code is available at https://github.com/HCIS-Lab/counterfactual-video-pretrain.