GenAI4ELab/papercli-papers huggingface dataset:
Boosting Adversarial Transferability via Residual Perturbation Attack
Authors: Jinjia Peng; Zeze Tao; Huibing Wang; Meng Wang; Yang Wang
Venue: ICCV 2025 (main)

Abstract: Deep neural networks are susceptible to adversarial examples while suffering from incorrect predictions via imperceptible perturbations. Transfer-based attacks create adversarial examples for surrogate models and transfer these examples to target models under black-box scenarios. Recent studies reveal that adversarial examples in flat loss landscapes exhibit superior transferability to alleviate overfitting on surrogate models. However, the prior arts overlook the influence of perturbation directions, resulting in limited transferability. In this paper, we propose a novel attack method, named Residual Perturbation Attack (ResPA), relying on the residual gradient as the perturbation direction to guide the adversarial examples toward the flat regions of the loss function. Specifically, ResPA conducts an exponential moving average on the input gradients to obtain the first moment as the reference gradient, which encompasses the direction of historical gradients. Instead of heavily relying on the local flatness that stems from the current gradients as the perturbation direction, ResPA further considers the residual between the current gradient and the reference gradient to capture the changes in the global perturbation direction. The experimental results demonstrate the better transferability of ResPA than the existing typical transfer-based attack methods, while the transferability can be further improved by combining ResPA with the current input transformation methods. The code is available at https://github.com/ZezeTao/ResPA.
LDPose: Towards Inclusive Human Pose Estimation for Limb-Deficient Individuals in the Wild
Authors: Jiaying Ying; Heming Du; Kaihao Zhang; Lincheng Li; Xin Yu
Venue: ICCV 2025 (main)

Abstract: Human pose estimation aims to predict the location of body keypoints and enable various practical applications. However, existing research focuses solely on individuals with full physical bodies and overlooks those with limb deficiencies. As a result, current pose estimation methods cannot be generalized to individuals with limb deficiencies. In this paper, we introduce the Limb-Deficient Pose Estimation task, which not only predicts the locations of standard human body keypoints, but also estimates the endpoints of missing limbs. To support this task, we present Limb-Deficient Pose (LDPose), the first-ever human pose estimation dataset for individuals with limb deficiencies. LDPose comprises over 28k images for approximately 73k individuals across diverse limb deficiency types and ethnic backgrounds. The annotation process is guided by internationally accredited para-athletics classifiers to ensure high precision. In addition, we propose a Limb-Deficient Loss (LDLoss) to better distinguish residual limb keypoints by contrasting residual limb keypoints and intact limb keypoints. Furthermore, we design Limb-Deficient Metrics (LD Metrics) to quantitatively measure the keypoint predictions of both residual and intact limbs and benchmark our dataset using state-of-the-art human pose estimation methods. Experiment results indicate that LDPose is a challenging dataset, and we believe that it will foster further research and ultimately support individuals with limb deficiencies worldwide.
Talking to DINO: Bridging Self-Supervised Vision Backbones with Language for Open-Vocabulary Segmentation
Authors: Luca Barsellotti; Lorenzo Bianchi; Nicola Messina; Fabio Carrara; Marcella Cornia; Lorenzo Baraldi; Fabrizio Falchi; Rita Cucchiara
Venue: ICCV 2025 (main)

Abstract: Open-Vocabulary Segmentation (OVS) aims at segmenting images from free-form textual concepts without predefined training classes. While existing vision-language models such as CLIP can generate segmentation masks by leveraging coarse spatial information from Vision Transformers, they face challenges in spatial localization due to their global alignment of image and text features. Conversely, self-supervised visual models like DINO excel in fine-grained visual encoding but lack integration with language. To bridge this gap, we present Talk2DINO, a novel hybrid approach that combines the spatial accuracy of DINOv2 with the language understanding of CLIP. Our approach aligns the textual embeddings of CLIP to the patch-level features of DINOv2 through a learned mapping function without the need to fine-tune the underlying backbones. At training time, we exploit the attention maps of DINOv2 to selectively align local visual patches with textual embeddings. We show that the powerful semantic and localization abilities of Talk2DINO can enhance the segmentation process, resulting in more natural and less noisy segmentations, and that our approach can also effectively distinguish foreground objects from the background. Experimental results demonstrate that Talk2DINO achieves state-of-the-art performance across several unsupervised OVS benchmarks.
Language-Driven Multi-Label Zero-Shot Learning with Semantic Granularity
Authors: Shouwen Wang; Qian Wan; Junbin Gao; Zhigang Zeng
Venue: ICCV 2025 (main)

Abstract: Recent methods learn class-unified prompt contexts by image data to adapt CLIP to zero-shot multi-label image classification, which achieves impressive performance. However, simply tuning prompts is insufficient to deal with novel classes across different semantic granularity levels. This limitation arises due to the sparse semantic detail in prompt class names and the hierarchical granularity competition among class names caused by CLIP's contrastive loss. We propose a language-driven zero-shot multi-label learning framework to bridge associations among classes across multiple granularity levels through class name reconstruction. To achieve this, we first leverage a language model to generate structured text descriptions for each class, which explicitly capture (1) visual attributes, (2) hierarchical relationships, and (3) co-occurrence scenes. With the enriched descriptions, we then learn class names by extracting and aligning semantic relationships and features from them in the CLIP's shared image-text embedding space. Furthermore, we consider that similar text descriptions among different classes may introduce ambiguities. We mitigate these ambiguities by imposing a pair-based loss on learnable class names to enhance their distinctiveness. During inference, we aggregate semantic predictions from multiple image snippets to reinforce the identification of classes across different granularity levels. Comprehensive experiments demonstrate that our method surpasses state-of-the-art methods in multi-label zero-shot learning and effectively handles novel classes across different granularity levels.
Beyond Perspective: Neural 360-Degree Video Compression
Authors: Andy Regensky; Marc Windsheimer; Fabian Brand; Andre Kaup
Venue: ICCV 2025 (main)

Abstract: Neural video codecs (NVCs) have seen fast-paced advancement in recent years and already perform close to state-of-the-art traditional video codecs like H.266/VVC. However, NVC investigations have so far focused on improving performance for classical perspective video leaving the increasingly important 360-degree video format unexplored. In this paper, we address this issue and present how existing NVCs can be optimized for 360-degree video while also improving performance on perspective video. As no suitable datasets for neural 360-degree video compression exist, we publish a large-scale 360-degree video dataset consisting of more than 6000 user generated 9-frame sequences with resolutions ranging from 0.5K to 8K. We propose a novel method for training data augmentation exploiting the spherical characteristics of 360-degree video that shows to be crucial for achieving maximum compression performance. An additional positional feature encoding further supports the NVC in dynamic bitrate allocation notably improving the performance for both 360-degree and perspective video. Overall, we achieve rate savings of almost 8% for 360-degree video and more than 3% for perspective video with minimal complexity overhead. The dataset is available at: https://huggingface.co/datasets/FAU-LMS/UGC360. Source code and pre-trained model weights are available at: https://github.com/FAU-LMS/NVC360.
Differentiable Room Acoustic Rendering with Multi-View Vision Priors
Authors: Derong Jin; Ruohan Gao
Venue: ICCV 2025 (main)

Abstract: An immersive acoustic experience enabled by spatial audio is just as crucial as the visual aspect in creating realistic virtual environments. However, existing methods for room impulse response estimation rely either on data-demanding learning-based models or computationally expensive physics-based modeling. In this work, we introduce Audio-Visual Differentiable Room Acoustic Rendering (AV-DAR), a framework that leverages visual cues extracted from multi-view images and acoustic beam tracing for physics-based room acoustic rendering. Experiments across six real-world environments from two datasets demonstrate that our multimodal, physics-based approach is efficient, interpretable, and accurate, significantly outperforming a series of prior methods. Notably, on the Real Acoustic Field dataset, AV-DAR achieves comparable performance to models trained on 10 times more data while delivering relative gains ranging from 16.6% to 50.9% when trained at the same scale.
An Inversion-based Measure of Memorization for Diffusion Models
Authors: Zhe Ma; Qingming Li; Xuhong Zhang; Tianyu Du; Ruixiao Lin; Zonghui Wang; Shouling Ji; Wenzhi Chen
Venue: ICCV 2025 (main)

Abstract: The past few years have witnessed substantial advances in image generation powered by diffusion models. However, it was shown that diffusion models are susceptible to training data memorization, raising significant concerns regarding copyright infringement and privacy invasion. This study delves into a rigorous analysis of memorization in diffusion models. We introduce InvMM, an inversion-based measure of memorization, which is based on inverting a sensitive latent noise distribution accounting for the replication of an image. For accurate estimation of the measure, we propose an adaptive algorithm that balances the normality and sensitivity of the noise distribution. Comprehensive experiments across four datasets, conducted on both unconditional and text-guided diffusion models, demonstrate that InvMM provides a reliable and complete quantification of memorization. Notably, InvMM is commensurable between samples, reveals the true extent of memorization from an adversarial standpoint and implies how memorization differs from membership. In practice, it serves as an auditing tool for developers to reliably assess the risk of memorization, thereby contributing to the enhancement of trustworthiness and privacy-preserving capabilities of diffusion models.
AgroBench: Vision-Language Model Benchmark in Agriculture
Authors: Risa Shinoda; Nakamasa Inoue; Hirokatsu Kataoka; Masaki Onishi; Yoshitaka Ushiku
Venue: ICCV 2025 (main)

Abstract: Precise automated understanding of agricultural tasks such as disease identification is essential for the sustainable crop production. Recent advances in vision-language models (VLMs) are expected to further expand the range of agricultural tasks by facilitating human-model interaction through easy, text-based communication. Here, we introduce AgroBench (Agronomist AI Benchmark), a benchmark for evaluating VLM models across seven agricultural topics, covering key areas in agricultural engineering and relevant to real-world farming. Unlike recent agricultural VLM benchmarks, AgroBench is annotated by expert agronomists. Our AgroBench covers a state-of-the-art range of categories, including 203 crop categories and 682 disease categories, to thoroughly evaluate VLM capabilities. In our evaluation on AgroBench, we reveal that VLMs have room for improvement in fine-grained identification tasks. Notably, in weed identification, most open-source VLMs perform close to random. With our wide range of topics and expert-annotated categories, we analyze the types of errors made by VLMs and suggest potential pathways for future VLM development. Our dataset and code are available at https://dahlian00.github.io/AgroBenchPage/.
ROAR: Reducing Inversion Error in Generative Image Watermarking
Authors: Hanyi Wang; Han Fang; Shi-Lin Wang; Ee-Chien Chang
Venue: ICCV 2025 (main)

Abstract: Generative image watermarking enables the proactive detection and traceability of generated images. Among existing methods, inversion-based frameworks achieve highly conceal ed watermark embedding by injecting watermarks into the latent representation before the diffusion process. The robustness of this approach hinges on both the embedding mechanism and inversion accuracy. However, prior works have predominantly focused on optimizing the embedding process while overlooking inversion errors, which significantly affect extraction fidelity. In this paper, we address the challenge of inversion errors and propose ROAR, a dual-domain optimization-based framework designed to mitigate errors arising from two key sources: 1) Latent-domain errors, which accumulate across inversion steps due to inherent approximation assumptions. 2) Pixel-domain errors, which result from channel distortions such as JPEG compression. To tackle these issues, we introduce two novel components: A Regeneration-based Optimization (RO) mechanism, which incorporates an optimizable starting latent to minimize latent-domain errors; A Mixture of Experts (MoE)-based distortion-adaptive restoration (AR) network, which effectively recovers watermarked distributions from pixel-level distortions.Extensive experiments demonstrate that ROAR significantly reduces inversion errors and enhances watermark extraction robustness, thereby improving the reliability of generative image watermarking.
Adding Additional Control to One-Step Diffusion with Joint Distribution Matching
Authors: Yihong Luo; Tianyang Hu; Yifan Song; Jiacheng Sun; Zhenguo Li; Jing Tang
Venue: ICCV 2025 (main)

Abstract: While diffusion distillation has enabled one-step generation through methods like Variational Score Distillation, adapting distilled models to emerging *new controls* -- such as novel structural constraints or latest user preferences -- remains challenging. Conventional approaches typically requires modifying the base diffusion model and redistilling it -- a process that is both computationally intensive and time-consuming. To address these challenges, we introduce Joint Distribution Matching (JDM), a novel approach that minimizes the reverse KL divergence between image-condition joint distributions. By deriving a tractable upper bound, JDM decouples fidelity learning from condition learning. This asymmetric distillation scheme enables our one-step student to handle controls unknown to the teacher model and facilitates improved classifier-free guidance (CFG) usage and seamless integration of human feedback learning (HFL). Experimental results demonstrate that JDM surpasses baseline methods such as multi-step ControlNet by mere one-step in most cases, while achieving state-of-the-art performance in one-step text-to-image synthesis through improved usage of CFG or HFL integration.
TimeBooth: Disentangled Facial Invariant Representation for Diverse and Personalized Face Aging
Authors: Zepeng Su; Zhulin Liu; Zongyan Zhang; Tong Zhang; C.L.Philip Chen
Venue: ICCV 2025 (main)

Abstract: Face aging is a typical ill-posed problem influenced by various factors such as environment and genetics, leading to highly diverse outcomes. However, existing methods primarily rely on numerical age representations, making it difficult to accurately capture individual or group-level aging patterns. To address this, we introduce a novel disentangled face representation, where age features are modeled in the image modality--referred to as the Age Prompt--providing richer prior age information to constrain the generation results. To this end, we design an ID-age multi-task co-learning framework and propose the Bidirectional Adversarial Disentanglement(BAD) strategy. This strategy maximizes the disentanglement of ID and age representation through bidirectional adversarial learning, extracting their attribute-invariant representations. Based on this representation, we propose TimeBooth, a personalized face aging model capable of generating diverse and individualized aging results. To optimize training, we construct a cross-age hybrid data pipeline and introduce various training strategies. Finally, we propose the R-AgeMAE metric and validate our method through extensive experiments, demonstrating that TimeBooth outperforms existing methods in both diversity and controllability. Our code will be publicly available at https://github.com/szp-aigc/TimeBooth.
Flexi-FSCIL: Adaptive Knowledge Retention for Breaking the Stability-Plasticity Dilemma in Few-Shot Class-Incremental Learning
Authors: Wufei Xie; Yalin Wang; Chenliang Liu; Zhaohui Jiang; Xue Yang
Venue: ICCV 2025 (main)

Abstract: Few-Shot Class-Incremental Learning (FSCIL) is challenged by limited data and expanding class spaces, leading to overfitting and catastrophic forgetting. Existing methods, which often freeze feature extractors and use Nearest Class Mean classifiers, sacrifice adaptability to new feature distributions. To address these issues, we propose Flexi-FSCIL, a semi-supervised framework that integrates three novel strategies: Adaptive Gated Residual Fusion (AGRF), Attention-Guided Dynamic Hybrid Distillation (ADHD), and Prototype Offset Equilibrium (POE). Flexi-FSCIL effectively balances stability and plasticity in FSCIL. AGRF resolves the rigidity of frozen feature extractors by integrating both frozen and trainable components, enabling adaptive feature learning while retaining old-class knowledge. ADHD tackles the imbalance between old and new tasks by dynamically aligning features using cross-attention maps and direct matching, preserving old-class knowledge while facilitating new-class learning. POE addresses the issue of prototype drift in semi-supervised settings by selecting high-quality unlabeled samples, maintaining feature space separability and preventing overfitting. Evaluated on three benchmark datasets, Flexi-FSCIL achieves state-of-the-art performance, significantly outperforming existing FSCIL methods with only 12.97 performance drop on CUB200.
Learning Beyond Still Frames: Scaling Vision-Language Models with Video
Authors: Yiyuan Zhang; Handong Li; Jing Liu; Xiangyu Yue
Venue: ICCV 2025 (main)

Abstract: High-quality image-text data is critical in enhancing Vision-Language Models (VLMs), but traditional image-based pretraining approaches face limitations. These methods are resource-intensive, relying on curated, high-quality interleaved data that is costly and challenging to collect at scale. Additionally, while such datasets improve static image-text understanding, they fail to develop the temporal and motion comprehension needed for video understanding. To address these gaps, we propose incorporating video pretraining into VLMs to improve the model's ability to capture temporal dynamics and general visual perception, which requires reconciling spatial redundancy with strict temporal causality. Therefore, we propose Causal Hierarchical Aggregation to separate computation-heavy spatial encoding from lightweight temporal propagation and construct hierarchical receptive fields at varying granularities. As we scale video context to more than 100 billion tokens, our method excels in high throughput and state-of-the-art performances on both Image and Video understanding, as shown in Figure 1, providing a scalable solution to enhance multimodal learning in dynamic contexts.
MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot Learning
Authors: Ylli Sadikaj; Hongkuan Zhou; Lavdim Halilaj; Stefan Schmid; Steffen Staab; Claudia Plant
Venue: ICCV 2025 (main)

Abstract: Precise optical inspection in industrial applications is crucial for minimizing scrap rates and reducing the associated costs. Besides merely detecting if a product is anomalous or not, it is crucial to know the distinct types of defects, such as a bent, cut, or scratch. The ability to recognize the "exact" defect type enables automated treatments of the anomalies in modern production lines. Current methods are limited to solely detecting whether a product is defective or not, without providing any insights into the defect type, but nevertheless detecting and identifying multiple defects. We propose MultiADS, a zero-shot learning approach, able to perform Multi-type Anomaly Detection and Segmentation. The architecture of MultiADS comprises CLIP and extra linear layers to align the visual and textual representation in a joint feature space. To the best of our knowledge, our proposal is the first approach to perform a multitype anomaly segmentation task in zero-shot learning. Contrary to the other baselines, our approach i) generates specific anomaly masks for each distinct defect type, ii) learns to distinguish defect types, and iii) simultaneously identifies multiple defect types present in an anomalous product. Additionally, our approach outperforms zero/few-shot learning SoTA methods on image-level and pixel-level anomaly detection and segmentation tasks on five commonly used datasets: MVTec-AD, Visa, MPDD, MAD, and Real-IAD.
SEGS-SLAM: Structure-enhanced 3D Gaussian Splatting SLAM with Appearance Embedding
Authors: Tianci Wen; Zhiang Liu; Yongchun Fang
Venue: ICCV 2025 (main)

Abstract: 3D Gaussian splatting (3D-GS) has recently revolutionized novel view synthesis in the simultaneous localization and mapping (SLAM) problem. However, most existing algorithms fail to fully capture the underlying structure, resulting in structural inconsistency. Additionally, they struggle with abrupt appearance variations, leading to inconsistent visual quality. To address these problems, we propose SEGS-SLAM, a structure-enhanced 3D Gaussian Splatting SLAM, which achieves high-quality photorealistic mapping. Our main contributions are two-fold. First, we propose a structure-enhanced photorealistic mapping (SEPM) framework that, for the first time, leverages highly structured point cloud to initialize structured 3D Gaussians, leading to significant improvements in rendering quality. Second, we propose Appearance-from-Motion embedding (AfME), enabling 3D Gaussians to better model image appearance variations across different camera poses. Extensive experiments on monocular, stereo, and RGB-D datasets demonstrate that SEGS-SLAM significantly outperforms state-of-the-art (SOTA) methods in photorealistic mapping quality, e.g., an improvement of 19.86% in PSNR over MonoGS on the TUM RGB-D dataset for monocular cameras. The project page is available at https://segs-slam.github.io/.