GenAI4ELab/papercli-papers huggingface dataset:
ConstStyle: Robust Domain Generalization with Unified Style Transformation
Authors: Nam Duong Tran; Nam Nguyen Phuong; Hieu H. Pham; Phi Le Nguyen; My T. Thai
Venue: ICCV 2025 (main)

Abstract: Deep neural networks often suffer performance drops when test data distribution differs from training data. Domain Generalization (DG) aims to address this by focusing on domain-invariant features or augmenting data for greater diversity. However, these methods often struggle with limited training domains or significant gaps between seen (training) and unseen (test) domains. To enhance DG robustness, we hypothesize that it is essential for the model to be trained on data from domains that closely resemble unseen test domains--an inherently difficult task due to the absence of prior knowledge about the unseen domains. Accordingly, we propose ConstStyle, a novel approach that leverages a unified domain to capture domain-invariant features and bridge the domain gap with theoretical analysis. During training, all samples are mapped onto this unified domain, optimized for seen domains. During testing, unseen domain samples are projected similarly before predictions. By aligning both training and testing data within this unified domain, ConstStyle effectively reduces the impact of domain shifts, even with large domain gaps or few seen domains. Extensive experiments demonstrate that ConstStyle consistently outperforms existing methods across diverse scenarios. Notably, when only a limited number of seen domains are available, ConstStyle can boost accuracy up to 19.82% compared to the next best approach.
Vision-Language Models Can't See the Obvious
Authors: Ngoc Dung Huynh; Phuc H Le-Khac; Wamiq Reyaz  Para; Ankit Singh; Sanath Narayan
Venue: ICCV 2025 (main)

Abstract: We present Saliency Benchmark (SalBench), a novel benchmark designed to assess the capability of Large Vision-Language Models (LVLM) in detecting visually salient features that are readily apparent to humans, such as a large circle amidst a grid of smaller ones. This benchmark focuses on low-level features including color, intensity, and orientation, which are fundamental to human visual processing. Our SalBench consists of images that highlight rare, unusual, or unexpected elements within scenes, and naturally draw human attention. It comprises three novel tasks for evaluating the perceptual capabilities of LVLM: Odd-One-Out Detection, Referring Odd-One-Out, and Visual Referring Odd-One-Out. We perform a comprehensive evaluation of state-of-the-art LVLM using SalBench and our findings reveal a surprising limitation: LVLM struggle to identify seemingly obvious visual anomalies, with even the advanced GPT-4o achieving only 47.6% accuracy on such a simple task. SalBench will be an important step in measuring the capabilities of LVLM that align with the subtle definition of human attention.
Perspective-Aware Teaching: Adapting Knowledge for Heterogeneous Distillation
Authors: Jhe-Hao Lin; Yi Yao; Chan-Feng Hsu; Hong-Xia Xie; Hong-Han Shuai; Wen-Huang Cheng
Venue: ICCV 2025 (main)

Abstract: Knowledge distillation (KD) involves transferring knowledge from a pre-trained heavy teacher model to a lighter student model, thereby reducing the inference cost while maintaining comparable effectiveness. Prior KD techniques typically assume homogeneity between the teacher and student models. However, as technology advances, a wide variety of architectures have emerged, ranging from initial Convolutional Neural Networks (CNNs) to Vision Transformers (ViTs), and Multi-Level Perceptrons (MLPs). Consequently, developing a universal KD framework compatible with any architecture has become an important research topic. In this paper, we introduce a perspective-aware teaching (PAT) KD framework to enable feature distillation across diverse architectures. Our framework comprises two key components. First, we design prompt tuning blocks that incorporate student feedback, allowing teacher features to adapt to the student model's learning process. Second, we propose region-aware attention to mitigate the view mismatch problem between heterogeneous architectures. By leveraging these two modules, effective distillation of intermediate features can be achieved across heterogeneous architectures. Extensive experiments on CIFAR, ImageNet, and COCO demonstrate the superiority of the proposed method. Our code is available at https://github.com/jimmylin0979/PAT.git.
GazeGaussian: High-Fidelity Gaze Redirection with 3D Gaussian Splatting
Authors: Xiaobao Wei; Peng Chen; Guangyu Li; Ming Lu; Hui Chen; Feng Tian
Venue: ICCV 2025 (main)

Abstract: Gaze estimation encounters generalization challenges when dealing with out-of-distribution data. To address this problem, recent methods use neural radiance fields (NeRF) to generate augmented data. However, existing methods based on NeRF are computationally expensive and lack facial details. 3D Gaussian Splatting (3DGS) has become the prevailing representation of neural fields. While 3DGS has been extensively examined in head avatars, it faces challenges with accurate gaze control and generalization across different subjects. In this work, we propose GazeGaussian, the first high-fidelity gaze redirection method that uses a two-stream 3DGS model to represent the face and eye regions separately. Leveraging the unstructured nature of 3DGS, we develop a novel representation of the eye for rigid eye rotation based on the target gaze direction. To enable synthesis generalization across various subjects, we integrate an expression-guided module to inject subject-specific information into the neural renderer. Comprehensive experiments show that GazeGaussian outperforms existing methods in rendering speed, gaze redirection accuracy, and facial synthesis across multiple datasets. The code is available at: https://ucwxb.github.io/GazeGaussian
Balancing Task-invariant Interaction and Task-specific Adaptation for Unified Image Fusion
Authors: Xingyu Hu; Junjun Jiang; Chenyang Wang; Kui Jiang; Xianming Liu; Jiayi Ma
Venue: ICCV 2025 (main)

Abstract: Unified image fusion aims to integrate complementary information from multi-source images, enhancing image quality through a unified framework applicable to diverse fusion tasks. While treating all fusion tasks as a unified problem facilitates task-invariant knowledge sharing, it often overlooks task-specific characteristics, thereby limiting the overall performance. Existing general image fusion methods incorporate explicit task identification to enable adaptation to different fusion tasks. However, this dependence during inference restricts the model's generalization to unseen fusion tasks. To address these issues, we propose a novel unified image fusion framework named "TITA", which dynamically balances both Task-invariant Interaction and Task-specific Adaptation. For task-invariant interaction, we introduce the Interaction-enhanced Pixel Attention (IPA) module to enhance pixel-wise interactions for better multi-source complementary information extraction. For task-specific adaptation, the Operation-based Adaptive Fusion (OAF) module dynamically adjusts operation weights based on task properties. Additionally, we incorporate the Fast Adaptive Multitask Optimization (FAMO) strategy to mitigate the impact of gradient conflicts across tasks during joint training. Extensive experiments demonstrate that TITA not only achieves competitive performance compared to specialized methods across three image fusion scenarios but also exhibits strong generalization to unseen fusion tasks.
Stroke2Sketch: Harnessing Stroke Attributes for Training-Free Sketch Generation
Authors: Rui Yang; Huining Li; Yiyi Long; Xiaojun Wu; Shengfeng He
Venue: ICCV 2025 (main)

Abstract: Generating sketches guided by reference styles requires precise transfer of stroke attributes, such as line thickness, deformation, and texture sparsity, while preserving semantic structure and content fidelity. To this end, we propose Stroke2Sketch, a novel training-free framework that introduces cross-image stroke attention, a mechanism embedded within self-attention layers to establish fine-grained semantic correspondences and enable accurate stroke attribute transfer. This allows our method to adaptively integrate reference stroke characteristics into content images while maintaining structural integrity. Additionally, we develop adaptive contrast enhancement and semantic-focused attention to reinforce content preservation and foreground emphasis. Stroke2Sketch effectively synthesizes stylistically faithful sketches that closely resemble handcrafted results, outperforming existing methods in expressive stroke control and semantic coherence.
Mitigating Catastrophic Overfitting in Fast Adversarial Training via Label Information Elimination
Authors: Chao Pan; Ke Tang; Qing Li; Xin Yao
Venue: ICCV 2025 (main)

Abstract: Fast Adversarial Training (FAT) employs the single-step Fast Gradient Sign Method (FGSM) to generate adversarial examples, reducing the computational costs of traditional adversarial training. However, FAT suffers from Catastrophic Overfitting (CO), where models' robust accuracy against multi-step attacks plummets to zero during training. Recent studies indicate that CO occurs because single-step adversarial perturbations contain label information that models exploit for prediction, leading to overfitting and diminished robustness against more complex attacks. In this paper, we discover that after CO occurs, the label information of certain samples can transfer across different samples, significantly increasing the likelihood of modified images being classified as the intended label. This discovery offers a new perspective on why various adversarial initialization strategies are effective. To address this issue, we introduce an innovative FAT strategy that leverages special samples to capture transferable label information and proactively removes potential label information during training, complemented by a non-uniform label smoothing technique to further eliminate label information. Experimental results across three datasets demonstrate that our method maintains competitive robustness against several attacks compared to other FAT approaches, with ablation studies confirming the effectiveness of our methodology. The code is available at https://github.com/fzjcdt/LIET.
Pi-GPS: Enhancing Geometry Problem Solving by Unleashing the Power of Diagrammatic Information
Authors: Junbo Zhao; Ting Zhang; Jiayu Sun; Mi Tian; Hua Huang
Venue: ICCV 2025 (main)

Abstract: Geometry problem solving has garnered increasing attention due to its potential applications in intelligent education field. Inspired by the observation that text often introduces ambiguities that diagrams can clarify, this paper presents Pi-GPS, a novel framework that unleashes the power of diagrammatic information to resolve textual ambiguities, an aspect largely overlooked in prior research. Specifically, we design a micro module comprising a rectifier and verifier: the rectifier employs MLLMs to disambiguate text based on the diagrammatic context, while the verifier ensures the rectified output adherence to geometric rules, mitigating model hallucinations. Additionally, we explore the impact of LLMs in theorem predictor based on the disambiguated formal language. Empirical results demonstrate that Pi-GPS surpasses state-of-the-art models, achieving a nearly 10% improvement on Geometry3K over prior neural-symbolic approaches. We hope this work highlights the significance of resolving textual ambiguity in multimodal mathematical reasoning, a crucial factor limiting performance.
CountSE: Soft Exemplar Open-set Object Counting
Authors: Shuai Liu; Peng Zhang; Shiwei Zhang; Wei Ke
Venue: ICCV 2025 (main)

Abstract: Open-set counting is garnering increasing attention due to its capability to enumerate objects of arbitrary category. It can be generally categorized into two methodologies: text-guided zero-shot counting methods and exemplar-guided few-shot counting methods. Previous text-guided zero-shot methods only provide limited object information through text, resulting in poor performance. Besides, though exemplar-guided few-shot approaches gain better results, they rely heavily on manually annotated visual exemplars, resulting in low efficiency and high labor intensity. Therefore, we propose CountSE, which simultaneously achieves high efficiency and high performance. CountSE is a new text-guided zero-shot object counting algorithm that generates multiple precise soft exemplars at different scales to enhance counting models driven solely by semantics. Specifically, to obtain richer object information and address the diversity in object scales, we introduce Semantic-guided Exemplar Selection, a module that generates candidate soft exemplars at various scales and selects those with high similarity scores. Then, to ensure accuracy and representativeness, Clustering-based Exemplar Filtering is introduced to refine the candidate exemplars by effectively eliminating inaccurate exemplars through clustering analysis. In the text-guided zero-shot setting, CountSE outperforms all state-of-the-art methods on the FSC-147 benchmark by at least 15%. Additionally, experiments on two other widely used datasets demonstrate that CountSE significantly outperforms all previous text-guided zero-shot counting methods and is competitive with the most advanced exemplar-guided few-shot methods. Codes will be available. Code is available at https://github.com/pppppz22/CountSE.
Beyond Isolated Words: Diffusion Brush for Handwritten Text-Line Generation
Authors: Gang Dai; Yifan Zhang; Yutao Qin; Qiangya Guo; Shuangping Huang; Shuicheng Yan
Venue: ICCV 2025 (main)

Abstract: Existing handwritten text generation methods primarily focus on isolated words. However, realistic handwritten text demands attention not only to individual words but also to the relationships between them, such as vertical alignment and horizontal spacing. Therefore, generating entire text line emerges as a more promising and comprehensive task. However, this task poses significant challenges, including the accurate modeling of complex style patterns--encompassing both intra- and inter-word relationships--and maintaining content accuracy across numerous characters. To address these challenges, we propose DiffBrush, a novel diffusion-based model for handwritten text-line generation. Unlike existing methods, DiffBrush excels in both style imitation and content accuracy through two key strategies: (1) content-decoupled style learning, which disentangles style from content to better capture intra-word and inter-word style patterns by using column- and row-wise masking; and (2) multi-scale content learning, which employs line and word discriminators to ensure global coherence and local accuracy of textual content. Extensive experiments show that DiffBrush excels in generating high-quality text-lines, particularly in style reproduction and content preservation. Code is available at https://github.com/dailenson/DiffBrush.
Wasserstein Style Distribution Analysis and Transform for Stylized Image Generation
Authors: Xi Yu; Xiang Gu; Zhihao Shi; Jian Sun
Venue: ICCV 2025 (main)

Abstract: Large-scale text-to-image diffusion models have achieved remarkable success in image generation, thereby driving the development of stylized image generation technologies. Recent studies introduce style information by empirically replacing specific features in attention blocks with style features. However, the relationship between features and style remains unclear. In this paper, we systematically analyze the relationship between features in attention blocks and style. By quantifying the distribution discrepancy induced by style variations using the Wasserstein distance, we find that features in self-attention blocks exhibit high sensitivity to style compared to features in cross-attention blocks. Our analysis provides valuable insights into the contribution of different features to style. Based on our findings, we propose a novel Wasserstein Style Distribution Transform (WSDT) method, which generates stylized images by transforming the distribution of style-sensitive features to align with that of style features. WSDT applies channel adaptive distribution transform to ensure that information not related to the style is not introduced. Our approach is simple yet efficient, optimization-free, and can be seamlessly integrated into attention-based text-to-image diffusion models. Extensive experiments demonstrate the effectiveness of our approach in stylized image generation tasks.
PRE-Mamba: A 4D State Space Model for Ultra-High-Frequent Event Camera Deraining
Authors: Ciyu Ruan; Ruishan Guo; Zihang Gong; Jingao Xu; Wenhan Yang; Xinlei Chen
Venue: ICCV 2025 (main)

Abstract: Event cameras excel in high temporal resolution and dynamic range but suffer from dense noise in rainy conditions. Existing event deraining methods face trade-offs between temporal precision, deraining effectiveness, and computational efficiency. In this paper, we propose PRE-Mamba, a novel point-based event camera deraining framework that fully exploits the spatiotemporal characteristics of raw event and rain. Our framework introduces a 4D event cloud representation that integrates dual temporal scales to preserve high temporal precision, a Spatio-Temporal Decoupling and Fusion module (STDF) that enhances deraining capability by enabling shallow decoupling and interaction of temporal and spatial information, and a Multi-Scale State Space Model (MS3M) that captures deeper rain dynamics across dual-temporal and multi-spatial scales with linear computational complexity. Enhanced by frequency-domain regularization, PRE-Mamba achieves superior performance (0.95 SR, 0.91 NR, and 0.4s/M events) with only 0.26M parameters on EventRain-27K, a comprehensive dataset with labeled synthetic and real-world sequences. Moreover, our method generalizes well across varying rain intensities, viewpoints, and even snowy conditions. Code and dataset: https://github.com/softword-tt/PRE-Mamba.
Future-Aware Interaction Network For Motion Forecasting
Authors: Shijie Li; Chunyu Liu; Xun Xu; Si Yong Yeo; Xulei Yang
Venue: ICCV 2025 (main)

Abstract: Motion forecasting is a crucial component of autonomous driving systems, enabling the generation of accurate and smooth future trajectories to ensure safe navigation to the destination. In previous methods, potential future trajectories are often absent in the scene encoding stage, which may lead to suboptimal outcomes. Additionally, prior approaches typically employ transformer architectures for spatiotemporal modeling of trajectories and map information, which suffer from the quadratic scaling complexity of the transformer architecture. In this work, we propose an interaction-based method, named Future-Aware Interaction Network, that introduces potential future trajectories into scene encoding for a comprehensive traffic representation. Furthermore, a State Space Model (SSM), specifically Mamba, is introduced for both spatial and temporal modeling. To adapt Mamba for spatial interaction modeling, we propose an adaptive reordering strategy that transforms unordered data into a structured sequence. Additionally, Mamba is employed to refine generated future trajectories temporally, ensuring more consistent predictions. These enhancements not only improve model efficiency but also enhance the accuracy and diversity of predictions. We conduct comprehensive experiments on the widely used Argoverse 1 and Argoverse 2 datasets, demonstrating that the proposed method achieves superior performance compared to previous approaches in a more efficient way. The code is available here.
SuMa: A Subspace Mapping Approach for Robust and Effective Concept Erasure in Text-to-Image Diffusion Models
Authors: Kien Nguyen; Anh Tran; Cuong Pham
Venue: ICCV 2025 (main)

Abstract: The rapid growth of text-to-image diffusion models has raised concerns about their potential misuse in generat- ing harmful or unauthorized contents. To address these issues, several Concept Erasure methods have been pro- posed. However, most of them fail to achieve both robust- ness, i.e., the ability to robustly remove the target concept., and effectiveness, i.e., maintaining image quality. While few recent techniques successfully achieve these goals for NSFW concepts, none could handle narrow concepts such as copyrighted characters or celebrities. Erasing these nar- row concepts is critical in addressing copyright and legal concerns. However, erasing them is challenging due to their close distances to non-target neighboring concepts, requir- ing finer-grained manipulation. In this paper, we introduce Subspace Mapping (SuMa), a novel method specifically de- signed to achieve both robustness and effectiveness in eas- ing these narrow concepts. SuMa first derives a target sub- space representing the concept to be erased and then neu- tralizes it by mapping it to a reference subspace that mini- mizes the distance between the two. This mapping ensures the target concept is robustly erased while preserving im- age quality. We conduct extensive experiments with SuMa across four tasks: subclass erasure, celebrity erasure, artis- tic style erasure, and instance erasure and compare the results with current state-of-the-art methods. Our method achieves image quality comparable to approaches focused on effectiveness, while also yielding results that are on par with methods targeting completeness.
Hierarchical-aware Orthogonal Disentanglement Framework for Fine-grained Skeleton-based Action Recognition
Authors: Haochen Chang; Pengfei Ren; Haoyang Zhang; Liang Xie; Hongbo Chen; Erwei Yin
Venue: ICCV 2025 (main)

Abstract: In recent years, skeleton-based action recognition has gained significant attention due to its robustness in varying environmental conditions. However, most existing methods struggle to distinguish fine-grained actions due to subtle motion features, minimal inter-class variation, and they often fail to consider the underlying similarity relationships between action classes. To address these limitations, we propose a Hierarchical-aware Orthogonal Disentanglement framework (HiOD). We disentangle coarse-grained and fine-grained features by employing independent spatial-temporal granularity-aware bases, which encode semantic representations at varying levels of granularity. Additionally, we design a cross-granularity feature interaction mechanism that leverages complementary information between coarse-grained and fine-grained features. We further enhance the learning process through hierarchical prototype contrastive learning, which utilizes the parent class hierarchy to guide the learning of coarse-grained features while ensuring the distinguishability of fine-grained features within child classes. Extensive experiments on FineGYM, FSD-10, NTU RGB+D, and NTU RGB+D 120 datasets demonstrate the superiority of our method in fine-grained action recognition tasks.
Stochastic Interpolants for Revealing Stylistic Flows across the History of Art
Authors: Pingchuan Ma; Ming Gui; Johannes Schusterbauer; Xiaopei Yang; Olga Grebenkova; Vincent Tao Hu; Björn Ommer
Venue: ICCV 2025 (main)

Abstract: Generative models have made rapid progress in content creation, particularly in synthesizing artworks and capturing stylistic variation. However, most methods operate at the level of individual images, limiting their ability to reveal broader stylistic trends and temporal transitions. We address this by introducing a framework that models stylistic evolution as an optimal transport problem in a learned style space, using stochastic interpolants and dual diffusion implicit bridges to align artistic distributions across time without requiring paired data. A central contribution is a diverse dataset of over 650,000 artworks spanning 500 years, curated with metadata across multiple genres. Together, our method and dataset enable tracing long-range stylistic transitions and plausible futures of individual artworks, supporting fine-grained temporal analysis. This offers a new tool for modeling historical patterns in visual culture and opens up promising directions in visual understanding. Code and dataset: https://github.com/CompVis/Art-fm