GenAI4ELab/papercli-papers huggingface dataset:
Enhancing Transferability of Targeted Adversarial Examples via Inverse Target Gradient Competition and Spatial Distance Stretching
Authors: Zhankai Li; Weiping Wang; Jie Li; Shigeng Zhang; Yunan Hu; Song Guo
Venue: ICCV 2025 (main)

Abstract: In the field of AI security, deep neural networks (DNNs) are highly sensitive to adversarial examples (AEs), which can cause incorrect predictions with minimal input perturbations. Although AEs exhibit transferability across models, targeted attack success rates (TASRs) are low due to differences in feature dimensions and decision boundaries. To enhance targeted AE transferability, we propose a novel approach using Inverse Target Gradient Competition (ITC) and Spatial Distance Stretching (SDS) in the optimization process. Specifically, we employ a siamese-network-like framework to generate both non-targeted and targeted AEs. The ITC mechanism applies non-targeted adversarial gradients each epoch to impede the optimization of targeted perturbations, thereby improving robustness. Additionally, a top-k SDS strategy guides AEs to penetrate target class regions in the latent space while distancing from non-targeted regions, achieving optimal transferability. Compared to state-of-the-art competition-based attacks, our method significantly improves transferable TASRs by 16.1% and 21.4% on mainstream CNNs and ViTs, respectively, and demonstrates superior defense-breaking capabilities.
MotionShot: Adaptive Motion Transfer across Arbitrary Objects for Text-to-Video Generation
Authors: Yanchen Liu; Yanan Sun; Zhening Xing; Junyao Gao; Kai Chen; Wenjie Pei
Venue: ICCV 2025 (main)

Abstract: Existing text-to-video methods struggle to transfer motion smoothly from a reference object to a target object with significant differences in appearance or structure between them. To address this challenge, we introduce MotionShot, a training-free framework capable of parsing reference-target correspondences in a fine-grained manner, thereby achieving high-fidelity motion transfer while preserving coherence in appearance. To be specific, MotionShot first performs semantic feature matching to ensure high-level alignments between the reference and target objects. It then further establishes low-level morphological alignments through reference-to-target shape retargeting. By encoding motion with temporal attention, our MotionShot can coherently transfer motion across objects, even in the presence of significant appearance and structure disparities, demonstrated by extensive experiments. The project page is available at: https://motionshot.github.io/.
STIV: Scalable Text and Image Conditioned Video Generation
Authors: Zongyu Lin; Wei Liu; Chen Chen; Jiasen Lu; Wenze Hu; Tsu-Jui Fu; Jesse Allardice; Zhengfeng Lai; Liangchen Song; Bowen Zhang; Cha Chen; Yiran Fei; Lezhi Li; Yinfei Yang; Yizhou Sun; Kai-Wei Chang
Venue: ICCV 2025 (main)

Abstract: We present a simple and scalable text and image conditioned video generation method. Our approach, named STIV, integrates a variable number of image conditions into a Diffusion Transformer (DiT) through frame replacement. This design enables STIV to perform both text-to-video (T2V) and text-image-to-video (TI2V) tasks simultaneously, as well as long video generation through autoregressive rollouts.Additionally, STIV can be easily extended to various applications, such as video prediction, frame interpolation, and multi-view generation, etc.With comprehensive ablation studies on T2I, T2V, TI2V, and long video generation, STIV demonstrate strong performance, despite its simple design. An 8.7B model with (512^2) resolution achieves 83.1 on VBench T2V, surpassing both leading open and closed-source models like CogVideoX-5B, Pika, Kling, and Gen-3. The same-sized model also achieves a state-of-the-art result of 90.1 on VBench I2V task at (512^2) resolution. Combine all of these, we finally scale up our model to 540p with over 200 frames. By providing a transparent recipe for building cutting-edge video generation models, we aim to empower future research and accelerate progress for video generation.
Repurposing 2D Diffusion Models with Gaussian Atlas for 3D Generation
Authors: Tiange Xiang; Kai Li; Chengjiang Long; Christian Häne; Peihong Guo; Scott Delp; Ehsan Adeli; Li Fei-Fei
Venue: ICCV 2025 (main)

Abstract: Text-to-image diffusion models have seen significant development recently due to increasing availability of paired 2D data. Although a similar trend is emerging in 3D generation, the limited availability of high-quality 3D data has resulted in less competitive 3D diffusion models compared to their 2D counterparts. In this work, we show how 2D diffusion models, originally trained for text-to-image generation, can be repurposed for 3D object generation. We introduce Gaussian Atlas, a representation of 3D Gaussians with dense 2D grids, which enables the fine-tuning of 2D diffusion models for generating 3D Gaussians. Our approach shows a successful transfer learning from a pretrained 2D diffusion model to 2D manifold flattend from 3D structures. To facilitate model training, a large-scale dataset, Gaussian Atlas, is compiled to comprise 205K high-quality 3D Gaussian fittings of a diverse array of 3D objects. Our experiment results indicate that text-to-image diffusion models can also serve as 3D content generators.
Generating, Fast and Slow: Scalable Parallel Video Generation with Video Interface Networks
Authors: Bhishma Dedhia; David Bourgin; Krishna Kumar Singh; Yuheng Li; Yan Kang; Zhan Xu; Niraj K. Jha; Yuchen Liu
Venue: ICCV 2025 (main)

Abstract: Diffusion Transformers (DiTs) can generate short photorealistic videos, yet directly training and sampling longer videos with full attention across the video remains computationally challenging. Alternative methods break long videos down into sequential generation of short video segments, requiring multiple sampling chain iterations and specialized consistency modules. To overcome these challenges, we introduce a new paradigm called Video Interface Networks (VINs), which augment DiTs with an abstraction module to enable parallel inference of video chunks. At each diffusion step, VINs encode global semantics from the noisy input of local chunks and the encoded representations, in turn, guide DiTs in denoising chunks in parallel. The coupling of VIN and DiT is learned end-to-end on the denoising objective. Further, the VIN architecture maintains fixed-size encoding tokens that encode the input via a single cross-attention step. Disentangling the encoding tokens from the input thus enables VIN to scale to long videos and learn essential semantics. Experiments on VBench demonstrate that VINs surpass existing chunk-based methods in preserving background consistency and subject coherence. We then show via an optical flow analysis that our approach attains state-of-the-art motion smoothness while using 25-40% fewer FLOPs than full generation. Finally, human raters favorably assessed the overall video quality and temporal consistency of our method in a user study.
The Devil is in the Spurious Correlations: Boosting Moment Retrieval with Dynamic Learning
Authors: Xinyang Zhou; Fanyue Wei; Lixin Duan; Angela Yao; Wen Li
Venue: ICCV 2025 (main)

Abstract: Given a textual query along with a corresponding video, the objective of moment retrieval aims to localize the moments relevant to the query within the video. While commendable results have been demonstrated by existing transformer-based approaches, predicting the accurate temporal span of the target moment is still a major challenge. This paper reveals that a crucial reason stems from the spurious correlation between the text query and the moment context. Namely, the model makes predictions by overly associating queries with background frames rather than distinguishing target moments. To address this issue, we propose a dynamic learning approach for moment retrieval, where two strategies are designed to mitigate the spurious correlation. First, we introduce a novel video synthesis approach to construct a dynamic context for the queried moment, enabling the model to attend to the target moment of the corresponding query across dynamic backgrounds. Second, to alleviate the over-association with backgrounds, we enhance representations temporally by incorporating text-dynamics interaction, which encourages the model to align text with target moments through complementary dynamic representations. With the proposed method, our model significantly alleviates the spurious correlation issue in moment retrieval and establishes new state-of-the-art performance on two popular benchmarks, i.e., QVHighlights and Charades-STA. In addition, detailed ablation studies and evaluations across different architectures demonstrate the generalization and effectiveness of the proposed strategies. Our code is available at https://github.com/xyangzhou/TD-DETR.
Guiding Noisy Label Conditional Diffusion Models with Score-based Discriminator Correction
Authors: Dat Nguyen Cong; Hieu Tran Bao; Tung Hoang-Thanh
Venue: ICCV 2025 (main)

Abstract: Diffusion models have gained prominence as state-of-the-art techniques for synthesizing images and videos, particularly due to their ability to scale effectively with large datasets. Recent studies have uncovered that these extensive datasets often contain mistakes from manual labeling processes. However, the extent to which such errors compromise the generative capabilities and controllability of diffusion models is not well studied. This paper introduces Score-based Discriminator Correction (SBDC), a guidance technique for aligning noisy pre-trained conditional diffusion models. The guidance is built on discriminator training using adversarial loss, drawing on prior noise detection techniques to assess the authenticity of each sample.We further show that limiting the usage of our guidance to the early phase of the generation process leads to better performance.Our method is computationally efficient, only marginally increases inference time, and does not require retraining diffusion models.Experiments on different noise settings demonstrate the superiority of our method over previous state-of-the-art methods.
A Conditional Probability Framework for Compositional Zero-shot Learning
Authors: Peng Wu; Qiuxia Lai; Hao Fang; Guo-Sen Xie; Yilong Yin; Xiankai Lu; Wenguan Wang
Venue: ICCV 2025 (main)

Abstract: Compositional Zero-Shot Learning (CZSL) aims to recognize unseen combinations of known objects and attributes by leveraging knowledge from previously seen compositions. Traditional approaches primarily focus on disentangling attributes and objects, treating them as independent entities during learning. However, this assumption overlooks the semantic constraints and contextual dependencies inside a composition. For example, certain attributes naturally pair with specific objects (e.g., "striped" applies to "zebra" or "shirts" but not "sky" or "water"), while the same attribute can manifest differently depending on context (e.g., "young" in "young tree" *vs* "young dog"). Thus, capturing attribute-object interdependence remains a fundamental yet long-ignored challenge in CZSL.In this paper, we adopt a Conditional Probability Framework (CPF) to explicitly model attribute-object dependencies. We decompose the probability of a composition into two components: the likelihood of an object and the conditional likelihood of its attribute. To enhance object feature learning, we incorporate textual descriptors to highlight semantically relevant image regions. These enhanced object features then guide attribute learning through a cross-attention mechanism, ensuring better contextual alignment. By jointly optimizing object likelihood and conditional attribute likelihood, our method effectively captures compositional dependencies and generalizes well to unseen compositions. Extensive experiments on multiple CZSL benchmarks demonstrate the superiority of our approach. Code is available at here.
IAP: Invisible Adversarial Patch Attack through Perceptibility-Aware Localization and Perturbation Optimization
Authors: Subrat Kishore Dutta; Xiao Zhang
Venue: ICCV 2025 (main)

Abstract: Despite modifying only a small localized input region, adversarial patches can drastically change the prediction of computer vision models. However, prior methods either cannot perform satisfactorily under targeted attack scenarios or fail to produce contextually coherent adversarial patches, causing them to be easily noticeable by human examiners and insufficiently stealthy against automatic patch defenses. In this paper, we introduce IAP, a novel attack framework that generates highly invisible adversarial patches based on perceptibility-aware localization and perturbation optimization schemes. Specifically, IAP first searches for a proper location to place the patch by leveraging classwise localization and sensitivity maps, balancing the susceptibility of patch location to both victim model prediction and human visual system, then employs a perceptibility-regularized adversarial loss and a gradient update rule that prioritizes color constancy for optimizing invisible perturbations. Comprehensive experiments across various image benchmarks and model architectures demonstrate that IAP consistently achieves competitive attack success rates in targeted settings with significantly improved patch invisibility compared to existing baselines. In addition to being highly imperceptible to humans, IAP is shown to be stealthy enough to render several state-of-the-art patch defenses ineffective.
Advancing Textual Prompt Learning with Anchored Attributes
Authors: Zheng Li; Yibing Song; Ming-Ming Cheng; Xiang Li; Jian Yang
Venue: ICCV 2025 (main)

Abstract: Textual-based prompt learning methods primarily employ multiple learnable soft prompts and hard class tokens in a cascading manner as text inputs, aiming to align image and text (category) spaces for downstream tasks. However, current training is restricted to aligning images with predefined known categories and cannot be associated with unknown categories. In this work, we propose utilizing universal attributes as a bridge to enhance the alignment between images and unknown categories. Specifically, we introduce an Attribute-anchored Textual Prompt learning method for vision-language models, named ATPrompt. This approach expands the learning space of soft prompts from the original one-dimensional category level into the multi-dimensional attribute level by incorporating multiple attribute tokens into the learnable soft prompts. Through this modification, we transform the text prompt from a category-centric form to an attribute-category hybrid form. Additionally, we introduce a straightforward differentiable attribute search method to identify representative and suitable attributes for downstream tasks. As an easy-to-use plug-in technique, ATPrompt can seamlessly replace the existing basic prompt format in textual-based methods, providing general improvements at a negligible computational cost. Extensive experiments across 11 datasets validate the effectiveness of our method. Code is publicly available at https://github.com/zhengli97/ATPrompt.
Event-based Visual Vibrometry
Authors: Xinyu Zhou; Peiqi Duan; Yeliduosi Xiaokaiti; Chao Xu; Boxin Shi
Venue: ICCV 2025 (main)

Abstract: Visual vibrometry has emerged as a powerful technique for remote acquisition of audio and the physical properties of materials. To capture high-frequency vibrations, frame-based approaches often require a high-speed video camera and bright lighting to compensate for the short exposure time. In this paper, we introduce event-based visual vibrometry, a new high-speed visual vibration sensing method using an event camera. By leveraging the high temporal resolution and low bandwidth characteristics of event cameras, event-based visual vibrometry enables high-speed vibration sensing under ambient lighting conditions with improved data efficiency. Specifically, we leverage a hybrid camera system and propose an event-based subtle motion estimation framework that integrates an optimization-based approach based on the event generation model and a motion refinement network. We demonstrate our method by capturing vibration caused by audio sources and estimating material properties for various objects.
Keep Your Friends Close, and Your Enemies Farther: Distance-aware Voxel-wise Contrastive Learning for Semi-supervised Multi-organ Segmentation
Authors: Haochen Zhao; Jianwei Niu; Xuefeng Liu; Xiaozheng Xie; Li Kuang; Haotian Yang; Bin Dai; Hui Meng; Yong Wang
Venue: ICCV 2025 (main)

Abstract: Based on pseudo-labels, voxel-wise contrastive learning (VCL) is a prominent approach designed to learn effective feature representations for semi-supervised medical image segmentation. However, in multi-organ segmentation (MoS), the complex anatomical structures of certain organs often lead to many unreliable pseudo-labels. Directly applying VCL can introduce confirmation bias, resulting in poor segmentation performance. A common practice is to first transform these unreliable pseudo-labels into complementary ones, which represent classes that voxels are least likely to belong to, and then push voxels away from the generated complementary labels. However, we find that this approach may fail to allow voxels with unreliable pseudo-labels (unreliable voxels) to fully benefit from the advantages of VCL. In this paper, we propose DVCL, a novel distance-aware VCL method for semi-supervised MoS. DVCL is based on the observation that unreliable voxels, which may not form discriminative feature boundaries, still form clear clusters. Hence, voxels close to each other in the feature space ('neighbors') likely belong to the same semantic class, while distant ones ('outsiders') likely belong to different classes. In DVCL, we first identify neighbors and outsiders for all unreliable voxels, and then pull their neighbors into the same clusters while pushing outsiders away. In this way, unreliable voxels can learn more discriminative features, thereby fully enjoying the advantages of VCL. However, DVCL itself will inevitably introduce the problem of noisy neighbors and outliers. To address these challenges, we further propose a neighbor partitioning strategy and a query outlier strategy to provide more stable feature representations for DVCL. Extensive experiments demonstrate the effectiveness of our method.
Online Reasoning Video Segmentation with Just-in-Time Digital Twins
Authors: Yiqing Shen; Bohan Liu; Chenjia Li; Lalithkumar Seenivasan; Mathias Unberath
Venue: ICCV 2025 (main)

Abstract: Reasoning segmentation (RS) aims to identify and segment objects of interest based on implicit text queries. As such, RS is a catalyst for embodied AI agents, enabling them to interpret high-level commands without requiring explicit step-by-step guidance. However, current RS approaches rely heavily on the visual perception capabilities of multimodal large language models (LLMs), leading to several major limitations. First, they struggle with queries that require multiple steps of reasoning or those that involve complex spatial/temporal relationships. Second, they necessitate LLM fine-tuning, which may require frequent updates to maintain compatibility with contemporary LLMs and may increase risks of catastrophic forgetting during fine-tuning. Finally, being primarily designed for static images or offline video processing, they scale poorly to online video data. To address these limitations, we propose an agent framework that disentangles perception and reasoning for online video RS without LLM fine-tuning. Our innovation is the introduction of a just-in-time digital twin concept, where -- given an implicit query -- an LLM plans the construction of a low-level scene representation from high-level video using specialist vision models. We refer to this approach to creating a digital twin as "just-in-time" because the LLM planner will anticipate the need for specific information and only request this limited subset instead of always evaluating every specialist model. The LLM then performs reasoning on this digital twin representation to identify target objects. To evaluate our approach, we introduce a new comprehensive video reasoning segmentation benchmark comprising 200 videos with 895 implicit text queries. The benchmark spans three reasoning categories (semantic, spatial, and temporal) with three different reasoning chain complexity. Experimental results demonstrate that our method performs best across all reasoning categories, suggesting that our just-in-time digital twin can bridge the gap between high-level reasoning and low-level perception in embodied AI. Benchmark is available at https://github.com/yiqings/jitbench/.
Robust Multi-View Learning via Representation Fusion of Sample-Level Attention and Alignment of Simulated Perturbation
Authors: Jie Xu; Na Zhao; Gang Niu; Masashi Sugiyama; Xiaofeng Zhu
Venue: ICCV 2025 (main)

Abstract: Recently, multi-view learning (MVL) has garnered significant attention due to its ability to fuse discriminative information from multiple views. However, real-world multi-view datasets are often heterogeneous and imperfect, which usually causes MVL methods designed for specific combinations of views to lack application potential and limits their effectiveness. To address this issue, we propose a novel robust MVL method (namely RML) with simultaneous representation fusion and alignment. Specifically, we introduce a simple yet effective multi-view transformer fusion network where we transform heterogeneous multi-view data into homogeneous word embeddings, and then integrate multiple views by the sample-level attention mechanism to obtain a fused representation. Furthermore, we propose a simulated perturbation based multi-view contrastive learning framework that dynamically generates the noise and unusable perturbations for simulating imperfect data conditions. The simulated noisy and unusable data obtain two distinct fused representations, and we utilize contrastive learning to align them for learning discriminative and robust representations. Our RML is self-supervised and can also be applied for downstream tasks as a regularization. In experiments, we employ it in multi-view unsupervised clustering, noise-label classification, and as a plug-and-play module for cross-modal hashing retrieval. Extensive comparison experiments and ablation studies validate RML's effectiveness. Code is available at https://github.com/SubmissionsIn/RML.
InterSyn: Interleaved Learning for Dynamic Motion Synthesis in the Wild
Authors: Yiyi Ma; Yuanzhi Liang; Xiu Li; Chi Zhang; Xuelong Li
Venue: ICCV 2025 (main)

Abstract: We present Interleaved Learning for Motion Synthesis (InterSyn), a novel framework that targets the generation of realistic interaction motions by learning from integrated motions that consider both solo and multi-person dynamics. Unlike previous methods that treat these components separately, InterSyn employs an interleaved learning strategy to capture the natural, dynamic interactions and nuanced coordination inherent in real-world scenarios. Our framework comprises two key modules: the Interleaved Interaction Synthesis (INS) module, which jointly models solo and interactive behaviors in a unified paradigm from a first-person perspective to support multiple character interactions, and the Relative Coordination Refinement (REC) module, which refines mutual dynamics and ensures synchronized motions among characters. Experimental results show that the motion sequences generated by InterSyn exhibit higher text-to-motion alignment and improved diversity compared with recent methods, setting a new benchmark for robust and natural motion synthesis.