GenAI4ELab/papercli-papers huggingface dataset:
SegmentDreamer: Towards High-fidelity Text-to-3D Synthesis with Segmented Consistency Trajectory Distillation
Authors: Jiahao Zhu; Zixuan Chen; Guangcong Wang; Xiaohua Xie; Yi Zhou
Venue: ICCV 2025 (main)

Abstract: Recent advancements in text-to-3D generation improve the visual quality of Score Distillation Sampling (SDS) and its variants by directly connecting Consistency Distillation (CD) to score distillation.However, due to the imbalance between self-consistency and cross-consistency, these CD-based methods inherently suffer from improper conditional guidance, leading to sub-optimal generation results.To address this issue, we present SegmentDreamer, a novel framework designed to fully unleash the potential of consistency models for high-fidelity text-to-3D generation.Specifically, we reformulate SDS through the proposed Segmented Consistency Trajectory Distillation (SCTD), effectively mitigating the imbalance issues by explicitly defining the relationship between self- and cross-consistency.Moreover, SCTD partitions the Probability Flow Ordinary Differential Equation (PF-ODE) trajectory into multiple sub-trajectories and ensures consistency within each segment, which can theoretically provide a significantly tighter upper bound on distillation error.Additionally, we propose a distillation pipeline for a more swift and stable generation.Extensive experiments demonstrate that our SegmentDreamer outperforms state-of-the-art methods in visual quality, enabling high-fidelity 3D asset creation through 3D Gaussian Splatting (3DGS).
InterGSEdit: Interactive 3D Gaussian Splatting Editing with 3D Geometry-Consistent Attention Prior
Authors: Minghao Wen; Shengjie Wu; Kangkan Wang; Dong Liang
Venue: ICCV 2025 (main)

Abstract: 3D Gaussian Splatting based 3D editing has demonstrated impressive performance in recent years. However, the multi-view editing often exhibits significant local inconsistency, especially in areas of non-rigid deformation, which lead to local artifacts, texture blurring, or semantic variations in edited 3D scenes. We also found that the existing editing methods, which rely entirely on text prompts make the editing process a "one-shot deal", making it difficult for users to control the editing degree flexibly. In response to these challenges, we present InterGSEdit, a novel framework for high-quality 3DGS editing via interactively selecting key views with users' preferences. We propose a CLIP-based Semantic Consistency Selection (CSCS) strategy to adaptively screen a group of semantically consistent reference views for each user-selected key view. Then, the cross-attention maps derived from the reference views are used in a weighted Gaussian Splatting unprojectionto construct the 3D Geometry-Consistent Attention Prior (GAP^ 3D ). We project GAP^ 3D  to obtain 3D-constrained attention, which are fused with 2D cross-attention via Attention Fusion Network (AFN). AFN employs an adaptive attention strategy that prioritizes 3D-constrained attention for geometric consistency during early inference, and gradually prioritizes 2D cross-attention maps in diffusion for fine-grained features during the later inference. Extensive experiments demonstrate that InterGSEdit achieves state-of-the-art performance, delivering consistent, high-fidelity 3DGS editing with improved user experience.
Fine-Grained Evaluation of Large Vision-Language Models in Autonomous Driving
Authors: Yue Li; Meng Tian; Zhenyu Lin; Jiangtong Zhu; Dechang Zhu; Haiqiang Liu; Yueyi Zhang; Zhiwei Xiong; Xinhai Zhao
Venue: ICCV 2025 (main)

Abstract: Existing benchmarks for Vision-Language Model (VLM) in autonomous driving (AD) primarily assess interpretability through open-form visual question answering (QA) within coarse-grained tasks, which remain insufficient to assess capabilities in complex driving scenarios. To this end, we introduce VLADBench, a challenging and fine-grained benchmark featuring close-form QAs that progress from static foundational knowledge and elements to advanced reasoning for dynamic on-road situations. The elaborate VLADBench spans 5 key domains: Traffic Knowledge Understanding, General Element Recognition, Traffic Graph Generation, Target Attribute Comprehension, and Ego Decision-Making and Planning. These domains are further broken down into 11 secondary aspects and 29 tertiary tasks for a granular evaluation. A thorough assessment of general and domain-specific (DS) VLMs on this benchmark reveals both their strengths and critical limitations in AD contexts. To further exploit the cognitive and reasoning interactions among the 5 domains for AD understanding, we start from a small-scale VLM and train the DS models on individual domain datasets (collected from 1.4M DS QAs across public sources). The experimental results demonstrate that the proposed benchmark provides a crucial step toward a more comprehensive assessment of VLMs in AD, paving the way for the development of more cognitively sophisticated and reasoning-capable AD systems. The benchmark is available at https://github.com/Depth2World/VLADBench.
MaTe: Images Are All You Need for Material Transfer via Diffusion Transformer
Authors: Nisha Huang; Henglin Liu; Yizhou Lin; Kaer Huang; Chubin Chen; Jie Guo; Tong-yee Lee; Xiu Li
Venue: ICCV 2025 (main)

Abstract: Recent diffusion-based methods for material transfer rely on image fine-tuning or complex architectures with assistive networks, but face challenges including text dependency, extra computational costs, and feature misalignment. To address these limitations, we propose MaTe, a streamlined diffusion framework that eliminates textual guidance and reference networks. MaTe integrates input images at the token level, enabling unified processing via multi-modal attention in a shared latent space. This design removes the need for additional adapters, ControlNet, inversion sampling, or model fine-tuning. Extensive experiments demonstrate that MaTe achieves high-quality material generation under a zero-shot, training-free paradigm. It outperforms state-of-the-art methods in both visual quality and efficiency while preserving precise detail alignment, significantly simplifying inference prerequisites.
Ph-GAN: Physics-Inspired GAN for Generating SAR Images Under Limited Data
Authors: Xidan Zhang; Yihan Zhuang; Qian Guo; Haodong Yang; Xuelin Qian; Gong Cheng; Junwei Han; Zhongling Huang
Venue: ICCV 2025 (main)

Abstract: Approaches for improving generative adversarial networks (GANs) training under a few samples have been explored for natural images. However, these methods have limited effectiveness for synthetic aperture radar (SAR) images, as they do not account for the unique electromagnetic scattering properties of SAR. To remedy this, we propose a physics-inspired regularization method dubbed Phi-GAN, which incorporates the ideal point scattering center (PSC) model of SAR with two physical consistency losses. The PSC model approximates SAR targets using physical parameters, ensuring that Phi-GAN generates SAR images consistent with real physical properties while preventing discriminator overfitting by focusing on PSC-based decision cues. To embed the PSC model into GANs for end-to-end training, we introduce a physics-inspired neural module capable of estimating the physical parameters of SAR targets efficiently. This module retains the interpretability of the physical model and can be trained with limited data. We propose two physical loss functions: one for the generator, guiding it to produce SAR images with physical parameters consistent with real ones, and one for the discriminator, enhancing its robustness by basing decisions on PSC attributes. We evaluate Phi-GAN across several conditional GAN (cGAN) models, demonstrating state-of-the-art performance in data-scarce scenarios on three SAR image datasets.
Interpretable point cloud classification using multiple instance learning
Authors: Matt De Vries; Reed Naidoo; Olga Fourkioti; Lucas G. Dent; Nathan Curry; Chris Dunsby; Chris Bakal
Venue: ICCV 2025 (main)

Abstract: Understanding 3D cell shape is crucial in biomedical research, where morphology serves as a key indicator of disease, cellular state, and drug response. However, many existing 3D point cloud classification models lack interpretability, limiting their utility for extracting biologically meaningful insights. In this work, we unify standard point cloud backbones and feature aggregation strategies within a Multiple Instance Learning (MIL) framework to enable inherently interpretable classification. Our approach, PointMIL, improves classification performance while providing fine-grained point-level explanations without relying on post hoc analysis. We demonstrate state-of-the-art mACC (97.3%) and F1 (97.5%) in the IntrA biomedical dataset and evaluate the interpretability using quantitative and qualitative metrics. Additionally, we introduce ATLAS-1, a novel dataset of drug-treated 3D cancer cells, and use it to show how PointMIL captures fine-grained morphological effects of chemical treatments. Beyond biomedical applications, PointMIL generalises to standard benchmarks such as ModelNet40 and ScanObjectNN, offering interpretable 3D object recognition across domains
Efficient Fine-Tuning of Large Models via Nested Low-Rank Adaptation
Authors: Lujun Li; Cheng  Lin; Dezhi Li; You-Liang Huang; Wei  Li; Tianyu  Wu; Jie  Zou; Wei  Xue; Sirui  Han; Yike  Guo
Venue: ICCV 2025 (main)

Abstract: Low-Rank Adaptation (LoRA) has become a popular paradigm for fine-tuning large models, but it still necessitates a substantial number of training parameters. To address this issue, we first conduct comprehensive empirical studies on parameter-efficient LoRA structure. Then, we establish design guidelines that emphasize the use of serial structures, optimal placements, and nested LoRA. Based on these insights, we present NoRA, a nested parameter-efficient LoRA structure that revolutionizes the initialization and fine-tuning of projection matrices. Our NoRA's innovative approach involves freezing outer layer LoRA weights and employing a serial inner layer design, enabling precise task-specific adaptations while maintaining compact training parameters. In addition, we propose an activation-aware Singular Value Decomposition (AwSVD) that adjusts the weight matrices based on activation distributions for initialization of outer layer LoRA weights. This schema enhances decomposition accuracy and mitigates computational errors. Extensive evaluations across multiple large models demonstrate that NoRA outperforms state-of-the-art LoRA variants, achieving significant improvements in performance-efficiency trade-off on visual few-shot tasks, visual instruction tuning and subject-driven generation. Codes are available at https://github.com/lliai/LoRA-Zoo.
CoSMIC: Continual Self-supervised Learning for Multi-Domain Medical Imaging via Conditional Mutual Information Maximization
Authors: Yihang Liu; Ying Wen; Longzhen Yang; Lianghua He; Heng Tao Shen
Venue: ICCV 2025 (main)

Abstract: Medical foundation models, pre-trained on diverse data sources, have shown significant potential for multi-domain medical imaging tasks.However, the domain shifts across different anatomical types significantly hinder their performance compared to domain-specific models.To address this challenge, we propose CoSMIC, a Continual Self-supervised learning framework for Multi-domain medIcal image analysis, with the core idea of Conditional mutual information maximization. Specifically, CoSMIC (i) acquires domain-specific knowledge sequentially, bypassing domain shifts caused by joint pre-training; (ii) enhances generalized representations by proposing a novel conditional contrastive loss to prevent catastrophic forgetting. This loss hierarchically aligns multi-view features within the current domain, maximizing their mutual information conditioned on domain-invariant representations extracted from prior domains through Anatomy-Guided Calibration. We pre-train CoSMIC across four medical domains and evaluate it on fifteen downstream datasets from five domains: Retinoscopy, Radiography, Ophthalmoscopy, Dermoscopy, and Histopathology (unseen). Experimental results show that CoSMIC (i) achieves robust feature extraction ability comparable to domain-specific models, (ii) exhibits exceptional generalization capability, significantly surpassing SOTA medical foundation models, and (iii) demonstrates superior transferability to new domains, overcoming current continual pre-training methods.
STAR: Spatial-Temporal Augmentation with Text-to-Video Models for Real-World Video Super-Resolution
Authors: Rui Xie; Yinhong Liu; Penghao Zhou; Chen Zhao; Jun Zhou; Kai Zhang; Zhenyu Zhang; Jian Yang; Zhenheng Yang; Ying Tai
Venue: ICCV 2025 (main)

Abstract: Image diffusion models have been adapted for real-world video super-resolution to tackle over-smoothing issues in GAN-based methods. However, these models struggle to maintain temporal consistency, as they are trained on static images, limiting their ability to capture temporal dynamics effectively. Integrating text-to-video (T2V) models into video super-resolution for improved temporal modeling is straightforward. However, two key challenges remain: artifacts introduced by complex degradations in real-world scenarios, and compromised fidelity due to the strong generative capacity of powerful T2V models (e.g., CogVideoX-5B). To enhance the spatio-temporal quality of restored videos, we introduce STAR (Spatial-Temporal Augmentation with T2V models for Real-world video super-resolution), a novel approach that leverages T2V models for real-world video super-resolution, achieving realistic spatial details and robust temporal consistency. Specifically, we introduce a Local Information Enhancement Module (LIEM) before the global attention block to enrich local details and mitigate degradation artifacts. Moreover, we propose a Dynamic Frequency (DF) Loss to reinforce fidelity, guiding the modelto focus on different frequency components across diffusion steps. Extensive experiments demonstrate STAR outperforms state-of-the-art methods on both synthetic and real-world datasets.
SCAN: Bootstrapping Contrastive Pre-training for Data Efficiency
Authors: Yangyang Guo; Mohan Kankanhalli
Venue: ICCV 2025 (main)

Abstract: While contrastive pre-training is widely employed, its data efficiency problem has remained relatively under-explored thus far. Existing methods often rely on static coreset selection algorithms to pre-identify important data for training. However, this static nature renders them unable to dynamically track the data utility throughout pre-training, leading to subpar pre-trained models. To address this challenge, our paper introduces a novel dynamic bootstrapping dataset pruning method. It involves pruning data preparation followed by dataset mutation operations, both of which undergo iterative and dynamic updates. We apply this method to two prevalent contrastive pre-training frameworks: CLIP and MoCo, representing vision-language and vision-centric domains, respectively. In particular, we individually pre-train seven CLIP models on two large-scale image-text pair datasets, and two MoCo models on the ImageNet dataset, resulting in a total of 16 pre-trained models. With a data pruning rate of 30-35% across all 16 models, our method exhibits only marginal performance degradation (less than 1% on average) compared to corresponding models trained on the full dataset counterparts across various downstream datasets, and also surpasses several baselines with a large performance margin. Additionally, the byproduct from our method, i.e., coresets derived from the original datasets after pre-training, also demonstrates significant superiority in terms of downstream performance over other static coreset selection approaches. Code is available at https://github.com/guoyang9/SCAN.
Learnable Retrieval Enhanced Visual-Text Alignment and Fusion for Radiology Report Generation
Authors: Qin Zhou; Guoyan Liang; Xindi Li; Jingyuan Chen; Zhe Wang; Chang Yao; Sai Wu
Venue: ICCV 2025 (main)

Abstract: Automated radiology report generation is essential for improving diagnostic efficiency and reducing the workload of medical professionals. However, existing methods face significant challenges, such as disease class imbalance and insufficient cross-modal fusion. To address these issues, we propose the learnable Retrieval Enhanced Visual-Text Alignment and Fusion (REVTAF) framework, which effectively tackles both class imbalance and visual-text fusion in report generation. REVTAF incorporates two core components: (1) a Learnable Retrieval Enhancer (LRE) that utilizes semantic hierarchies from hyperbolic space and intra-batch context through a ranking-based metric. LRE adaptively retrieves the most relevant reference reports, enhancing image representations, particularly for underrepresented (tail) class inputs; and (2) a fine-grained visual-text alignment and fusion strategy that ensures consistency across multi-source cross-attention maps for precise alignment. This component further employs an optimal transport-based cross-attention mechanism to dynamically integrate task-relevant textual knowledge for improved report generation. By combining adaptive retrieval with multi-source alignment and fusion, REVTAF achieves fine-grained visual-text integration under weak image-report level supervision while effectively mitigating data imbalance issues. The experiments demonstrate that REVTAF outperforms state-of-the-art methods, achieving an average improvement of 7.4% on the MIMIC-CXR dataset and 2.9% on the IU X-Ray dataset. Comparisons with mainstream multimodal LLMs (e.g., GPT-series models), further highlight its superiority in radiology report generation.
Revisiting Adversarial Patch Defenses on Object Detectors: Unified Evaluation, Large-Scale Dataset, and New Insights
Authors: Junhao Zheng; Jiahao Sun; Chenhao Lin; Zhengyu Zhao; Chen Ma; Chong Zhang; Cong Wang; Qian Wang; Chao Shen
Venue: ICCV 2025 (main)

Abstract: Developing reliable defenses against patch attacks on object detectors has attracted increasing interest. However, we identify that existing defense evaluations lack a unified and comprehensive framework, resulting in inconsistent and incomplete assessments of current methods. To address this issue, we revisit 11 representative defenses and present the first patch defense benchmark, involving 2 attack goals, 13 patch attacks, 11 object detectors, and 4 diverse metrics. This leads to the large-scale adversarial patch dataset with 94 types of patches and 94,000 images. Our comprehensive analyses reveal new insights: (1) The difficulty in defending against naturalistic patches lies in the data distribution, rather than the commonly believed high frequencies. Our new dataset with diverse patch distributions can be used to improve existing defenses by 15.09% AP@0.5. (2) The average precision of the attacked object, rather than the commonly pursued patch detection accuracy, shows high consistency with defense performance. (3) Adaptive attacks can substantially bypass existing defenses, and defenses with complex/stochastic models or universal patch properties are relatively robust. We hope that our analyses will serve as guidance on properly evaluating patch attacks/defenses and advancing their design. Code and dataset are available at https://github.com/Gandolfczjh/APDE, where we will keep integrating new attacks/defenses.
V.I.P. : Iterative Online Preference Distillation for Efficient Video Diffusion Models
Authors: Jisoo Kim; Wooseok Seo; Junwan Kim; Seungho Park; Sooyeon Park; Youngjae Yu
Venue: ICCV 2025 (main)

Abstract: With growing interest in deploying text-to-video (T2V) models in resource-constrained environments, reducing their high computational cost has become crucial, leading to extensive research on pruning and knowledge distillation methods while maintaining performance. However, existing distillation methods primarily rely on supervised fine-tuning (SFT), which often leads to mode collapse as pruned models with reduced capacity fail to directly match the teacher's outputs, ultimately resulting in degraded quality. To address this challenge, we propose an effective distillation method, \loss, that integrates DPO and SFT. Our approach leverages DPO to guide the student model to focus on recovering only the targeted properties, rather than passively imitating the teacher, while also utilizing SFT to enhance overall performance. We additionally propose \ours, a novel framework for filtering and curating high-quality pair datasets, along with a step-by-step online approach for calibrated training. We validate our method on two leading T2V models, VideoCrafter2 and AnimateDiff, achieving parameter reduction of 36.2% and 67.5% each, while maintaining or even surpassing the performance of full models. Further experiments demonstrate the effectiveness of both \loss and \ours framework in enabling efficient and high-quality video generation. Our code and videos are available at https://jiiiisoo.github.io/VIP.github.io/.
Diffusion-based Source-biased Model for Single Domain Generalized Object Detection
Authors: Han Jiang; Wenfei Yang; Tianzhu Zhang; Yongdong Zhang
Venue: ICCV 2025 (main)

Abstract: Single domain generalized object detection aims to train an object detector on a single source domain and generalize it to any unseen domain. Although existing approaches based on data augmentation exhibit promising results, they overlook domain discrepancies across multiple augmented domains, which limits the performance of object detectors. To tackle these problems, we propose a novel diffusion-based framework, termed SDG-DiffDet, to mitigate the impact of domain gaps on object detectors. The proposed SDG-DiffDet consists of a memory-guided diffusion module and a source-guided denoising module. Specifically, in the memory-guided diffusion module, we design feature statistics memories that mine diverse style information from local parts to augment source features. The augmented features further serve as noise in the diffusion process, enabling the model to capture distribution differences between practical domain distributions. In the source-guided denoising module, we design a text-guided condition to facilitate distribution transfer from any unseen distribution to source distribution in the denoising process. By combining these two designs, our proposed SDG-DiffDet effectively models feature augmentation and target-to-source distribution transfer within a unified diffusion framework, thereby enhancing the generalization ability of object detector. Extensive experiments demonstrate that the proposed SDG-DiffDet achieves state-of-the-art performance across two challenge scenarios.
Domain Generalizable Portrait Style Transfer
Authors: Xinbo Wang; Wenju Xu; Qing Zhang; Wei-Shi Zheng
Venue: ICCV 2025 (main)

Abstract: This paper presents a portrait style transfer method that generalizes well to various different domains while enabling high-quality semantic-aligned stylization on regions including hair, eyes, eyelashes, skins, lips, and background. To this end, we propose to establish dense semantic correspondence between the given input and reference portraits based on a pre-trained model and a semantic adapter, with which we obtain a warped reference semantically aligned with the input. To ensure effective yet controllable style transfer, we devise an AdaIN-Wavelet transform to balance content preservation and stylization by blending low-frequency information of the warped reference with high-frequency information of the input in the latent space. A style adapter is also designed to provide style guidance from the warped reference. With the stylized latent from AdaIN-Wavelet transform, we employ a dual-conditional diffusion model that integrates a ControlNet recording high-frequency information and the style guidance to generate the final result. Extensive experiments demonstrate the superiority of our method. Our code and trained model are available at https://github.com/wangxb29/DGPST  https://github.com/wangxb29/DGPST.