GenAI4ELab/papercli-papers huggingface dataset:
Accelerate 3D Object Detection Models via Zero-Shot Attention Key Pruning
Authors: Lizhen Xu; Xiuxiu Bai; Xiaojun Jia; Jianwu Fang; Shanmin Pang
Venue: ICCV 2025 (main)

Abstract: Query-based methods with dense features have demonstrated remarkable success in 3D object detection tasks. However, the computational demands of these models, particularly with large image sizes and multiple transformer layers, pose significant challenges for efficient running on edge devices. Existing pruning and distillation methods either need retraining or are designed for ViT models, which are hard to migrate to 3D detectors. To address this issue, we propose a zero-shot runtime pruning method for transformer decoders in 3D object detection models. The method, termed tgGBC (trim keys gradually Guided By Classification scores), systematically trims keys in transformer modules based on their importance. We expand the classification score to multiply it with the attention map to get the importance score of each key and then prune certain keys after each transformer layer according to their importance scores. Our method achieves a 1.99x speedup in the transformer decoder of the latest ToC3D model, with only a minimal performance loss of less than 1%. Interestingly, for certain models, our method even enhances their performance. Moreover, we deploy 3D detectors with tgGBC on an edge device, further validating the effectiveness of our method. The code can be found at https://github.com/iseri27/tg_gbc.
Adaptive Learning of High-Value Regions for Semi-Supervised Medical Image Segmentation
Authors: Tao Lei; Ziyao Yang; Xingwu Wang; Yi Wang; Xuan Wang; Feiman Sun; Asoke K. Nandi
Venue: ICCV 2025 (main)

Abstract: Existing semi-supervised learning methods typically mitigate the impact of unreliable predictions by suppressing low-confidence regions. However, these methods fail to explore which regions hold higher learning value and how to design adaptive learning strategies for these regions. To address these issues, we propose a novel adaptive learning of high-value regions (ALHVR) framework. By exploiting the diversity of predictions from dual-branch networks, the prediction regions are classified into three groups: reliable stable region, reliable unstable region, and unreliable stable region. For high-value regions (reliable unstable region and unreliable stable region), different training strategies are designed. Specifically, for reliable unstable region, we propose a confidence-guided cross-prototype consistency learning (CG-CPCL) module, which enforces prototype consistency constraints in the feature space. By leveraging confidence information, the high-confidence predictions from one network selectively supervise the low-confidence predictions from the other, thus helping the model learn inter-class discrimination more stably. Additionally, for unreliable stable region, we design a dynamic teacher competition teaching (DTCT) module, which dynamically selects the most reliable pixels as teachers by evaluating the unperturbed predictions from both networks. These selected pixels are then used to supervise perturbed predictions, thereby enhancing the model's learning capability in unreliable region. Experimental results show that our method outperforms state-of-the-art approaches on three public datasets. Code is available at https://github.com/ziziyao/ALHVR.
X-Dancer: Expressive Music to Human Dance Video Generation
Authors: Zeyuan Chen; Hongyi Xu; Guoxian Song; You Xie; Chenxu Zhang; Xin Chen; Chao Wang; Di Chang; Linjie Luo
Venue: ICCV 2025 (main)

Abstract: We present X-Dancer, a novel zero-shot music-driven image animation pipeline that creates diverse and long-range lifelike human dance videos from a single static image. As its core, we introduce a unified transformer-diffusion framework, featuring an autoregressive transformer model that synthesize extended and music-synchronized token sequences for 2D body, head and hands poses, which then guide a diffusion model to produce coherent and realistic dance video frames. Unlike traditional methods that primarily generate human motion in 3D, X-Dancer addresses data limitations and enhances scalability by modeling a wide spectrum of 2D dance motions, capturing their nuanced alignment with musical beats through readily available monocular videos. To achieve this, we first build a spatially compositional token representation from 2D human pose labels associated with keypoint confidences, encoding both large articulated body movements (e.g., upper and lower body) and fine-grained motions (e.g., head and hands). We then design a music-to-motion transformer model that autoregressively generates music-aligned dance pose token sequences, incorporating global attention to both musical style and prior motion context. Finally we leverage a diffusion backbone to animate the reference image with these synthesized pose tokens through AdaIN, forming a fully differentiable end-to-end framework. Experimental results demonstrate that X-Dancer is able to produce both diverse and characterized dance videos, substantially outperforming state-of-the-art methods in term of diversity, expressiveness and realism. See our project page for more results: https://zeyuan-chen.com/X-Dancer/.
IDF: Iterative Dynamic Filtering Networks for Generalizable Image Denoising
Authors: Dongjin Kim; Jaekyun Ko; Muhammad Kashif Ali; Tae Hyun Kim
Venue: ICCV 2025 (main)

Abstract: Image denoising is a fundamental challenge in computer vision, with applications in photography and medical imaging. While deep learning-based methods have shown remarkable success, their reliance on specific noise distributions limits generalization to unseen noise types and levels. Existing approaches attempt to address this with extensive training data and high computational resources but they still suffer from overfitting. To address these issues, we conduct image denoising by utilizing dynamically generated kernels via efficient operations. This approach helps prevent overfitting and improves resilience to unseen noise. Specifically, our method leverages a Feature Extraction Module for robust noise-invariant features, Global Statistics and Local Correlation Modules to capture comprehensive noise characteristics and structural correlations. The Kernel Prediction Module then employs these cues to produce pixel-wise varying kernels adapted to local structures, which are then applied iteratively for denoising. This ensures both efficiency and superior restoration quality. Despite being trained on single-level Gaussian noise, our compact model (  0.04 M) excels across diverse noise types and levels, demonstrating the promise of iterative dynamic filtering for practical image denoising.
LayerLock: Non-collapsing Representation Learning with Progressive Freezing
Authors: Goker Erdogan; Nikhil Parthasarathy; Catalin Ionescu; Drew A. Hudson; Alexander Lerchner; Andrew Zisserman; Mehdi S. M. Sajjadi; Joao Carreira
Venue: ICCV 2025 (main)

Abstract: We introduce LayerLock, a simple yet effective approach for self-supervised visual representation learning, that gradually transitions throughout training from predicting shallow features to deeper ones through progressive layer freezing. First, we make the observation that during training of video masked-autoencoding (MAE) models, ViT layers converge in the order of their depth: shallower layers converge early, deeper layers converge late. We then show that this observation can be exploited to accelerate standard MAE by progressively freezing the model according to an explicit schedule, throughout training. Furthermore, this same schedule can be used in a simple and scalable approach to latent prediction that does not suffer from "representation collapse". We apply our proposed approach, LayerLock, to both pixel and latent prediction approaches with large models of up to 4B parameters and show improvements on both semantic (action classification) and low level (depth estimation) vision tasks.
Augmenting Moment Retrieval: Zero-Dependency Two-Stage Learning
Authors: Zhengxuan Wei; Jiajin Tang; Sibei Yang
Venue: ICCV 2025 (main)

Abstract: Existing Moment Retrieval methods face three critical bottlenecks: (1) data scarcity forces models into shallow keyword-feature associations; (2) boundary ambiguity in transition regions between adjacent events; (3) insufficient discrimination of fine-grained semantics (e.g., distinguishing "kicking" vs. "throwing" a ball). In this paper, we propose a zero-external-dependency Augmented Moment Retrieval framework, AMR, designed to overcome local optima caused by insufficient data annotations and the lack of robust boundary and semantic discrimination capabilities. AMR is built upon two key insights: (1) it resolves ambiguous boundary information and semantic confusion in existing annotations without additional data (avoiding costly manual labeling), and (2) it preserves boundary and semantic discriminative capabilities enhanced by training while generalizing to real-world scenarios, significantly improving performance. Furthermore, we propose a two-stage training framework with cold-start and distillation adaptation. The cold-start stage employs curriculum learning on augmented data to build foundational boundary/semantic awareness. The distillation stage introduces dual query sets: Original Queries maintain DETR-based localization using frozen Base Queries from the cold-start model, while Active Queries dynamically adapt to real-data distributions. A cross-stage distillation loss enforces consistency between Original and Base Queries, preventing knowledge forgetting while enabling real-world generalization. Experiments on multiple benchmarks show that AMR achieves improved performance over prior state-of-the-art approaches.
Derm1M: A Million-scale Vision-Language Dataset Aligned with Clinical Ontology Knowledge for Dermatology
Authors: Siyuan Yan; Ming Hu; Yiwen Jiang; Xieji Li; Hao Fei; Philipp Tschandl; Harald Kittler; Zongyuan Ge
Venue: ICCV 2025 (main)

Abstract: The emergence of vision-language models has transformed medical AI, enabling unprecedented advances in diagnostic capability and clinical applications. However, progress in dermatology has lagged behind other medical domains due to the lack of standard image-text pairs. Existing dermatological datasets are limited in both scale and depth, offering only single-label annotations across a narrow range of diseases instead of rich textual descriptions, and lacking the crucial clinical context needed for real-world applications. To address these limitations, we present Derm1M, the first large-scale vision-language dataset for dermatology, comprising 1,029,761 image-text pairs. Built from diverse educational resources and structured around a standard ontology collaboratively developed by experts, Derm1M provides comprehensive coverage for over 390 skin conditions across four hierarchical levels and 130 clinical concepts with rich contextual information such as medical history, symptoms, and skin tone. To demonstrate Derm1M's potential in advancing both AI research and clinical application, we pretrained a series of CLIP-like models, collectively called DermLIP, on this dataset. The DermLIP family significantly outperforms state-of-the-art foundation models on eight diverse datasets across multiple tasks, including zero-shot skin disease classification, clinical and artifacts concept identification, few-shot/full-shot learning, and cross-modal retrieval. Our dataset and code are available at https://github.com/SiyuanYan1/Derm1M.
Expressive Talking Human from Single-Image with Imperfect Priors
Authors: Jun Xiang; Yudong Guo; Leipeng Hu; Boyang Guo; Yancheng Yuan; Juyong Zhang
Venue: ICCV 2025 (main)

Abstract: Building realistic and animatable avatars still requires minutes of multi-view or monocular self-rotating videos, and most methods lack precise control over gestures and expressions. To push this boundary, we address the challenge of constructing a whole-body talking avatar from a single image. We propose a novel pipeline that tackles two critical issues: 1) complex dynamic modeling and 2) generalization to novel gestures and expressions. To achieve seamless generalization, we leverage recent pose-guided image-to-video diffusion models to generate imperfect video frames as pseudo-labels. To overcome the dynamic modeling challenge posed by inconsistent and noisy pseudo-frames, we introduce a tightly coupled 3DGS-mesh hybrid avatar representation and apply several key regularizations to mitigate inconsistencies caused by imperfect labels. Extensive experiments on diverse subjects demonstrate that our method enables the creation of a photorealistic, precisely animatable, and expressive whole-body talking avatar from just a single image.
DADM: Dual Alignment of Domain and Modality for Face Anti-spoofing
Authors: Jingyi Yang; Xun Lin; Zitong Yu; Liepiao Zhang; Xin Liu; Hui Li; Xiaochen Yuan; Xiaochun Cao
Venue: ICCV 2025 (main)

Abstract: With the availability of diverse sensor modalities (i.e., RGB, Depth, Infrared) and the success of multi-modal learning, multi-modal face anti-spoofing (FAS) has emerged as a prominent research focus. The intuition behind it is that leveraging multiple modalities can uncover more intrinsic spoofing traces. However, this approach presents more risk of misalignment. We identify two main types of misalignment: (1) Intra-domain modality misalignment, where the importance of each modality varies across different attacks. For instance, certain modalities (e.g., Depth) may be non-defensive against specific attacks (e.g., 3D mask), indicating that each modality has unique strengths and weaknesses in countering particular attacks. Consequently, simple fusion strategies may fall short. (2) Inter-domain modality misalignment, where the introduction of additional modalities exacerbates domain shifts, potentially overshadowing the benefits of complementary fusion. To tackle (1), we propose an alignment module between modalities based on mutual information, which adaptively enhances favorable modalities while suppressing unfavorable ones. To address (2), we employ a dual alignment optimization method that aligns both sub-domain hyperplanes and modality angle margins, thereby mitigating domain gaps. Our method, dubbed Dual Alignment of Domain and Modality (DADM), achieves state-of-the-art performance in extensive experiments across four challenging protocols, demonstrating its robustness in multi-modal domain generalization scenarios. Our code is available at \href https://github.com/yjyddq/DADM  here .
IntroStyle: Training-Free Introspective Style Attribution using Diffusion Features
Authors: Anand Kumar; Jiteng Mu; Nuno Vasconcelos
Venue: ICCV 2025 (main)

Abstract: Text-to-image (T2I) models have recently gained widespread adoption. This has spurred concerns about safeguarding intellectual property rights and an increasing demand for mechanisms that prevent the generation of specific artistic styles. Existing methods for style extraction typically necessitate the collection of custom datasets and the training of specialized models. This, however, is resource-intensive, time-consuming, and often impractical for real-time applications. We present a novel, training-free framework to solve the style attribution problem, using the features produced by a diffusion model alone, without any external modules or retraining. This is denoted as Introspective Style attribution(IntroStyle) and is shown to have superior performance to state-of-the-art models for style attribution. We also introduce a synthetic dataset of Artistic Style Split (ArtSplit) to isolate artistic style and evaluate fine-grained style attribution performance. Our experimental results on WikiArt and DomainNet datasets show that \ours is robust to the dynamic nature of artistic styles, outperforming existing methods by a wide margin.
Learning Deblurring Texture Prior from Unpaired Data with Diffusion Model
Authors: Chengxu Liu; Lu Qi; Jinshan Pan; Xueming Qian; Ming-Hsuan Yang
Venue: ICCV 2025 (main)

Abstract: Since acquiring large amounts of realistic blurry-sharp image pairs is difficult and expensive, learning blind image deblurring from unpaired data is a more practical and promising solution. Unfortunately, most existing approaches only use adversarial learning to bridge the gap from blurry domains to sharp domains, ignoring the complex and unpredictable nature of real-world blurry patterns. In this paper, we propose a novel diffusion model (DM)-based framework, dubbed TP-Diff, for image deblurring by learning spatially varying texture prior from unpaired sharp data. In particular, TP-Diff performs DM to generate the prior knowledge used to recover the texture of blurry images. To implement it, we propose a Texture Prior Encoder (TPE) that introduces a memory mechanism to encode the texture prior and thereby provide supervision for the DM training. To fully exploit the generated texture priors, we further present the Texture Transfer Transformer layer (TTformer), in which a novel Filter-Modulated Multi-head Self-Attention (FM-MSA) efficiently removes spatially varying blurring through adaptive filtering. In addition, a wavelet-based adversarial loss is used to preserve high-frequency texture details. Extensive evaluations demonstrate that TP-Diff provides a promising unsupervised deblurring solution and outperforms SOTA methods in six widely-used benchmarks.
Oasis: One Image is All You Need for Multimodal Instruction Data Synthesis
Authors: Letian Zhang; Quan Cui; Bingchen Zhao; Cheng Yang
Venue: ICCV 2025 (main)

Abstract: The success of multi-modal large language models (MLLMs) has been largely attributed to the large-scale training data. However, the training data of many MLLMs is unavailable due to privacy concerns. The expensive and labor-intensive process of collecting multi-modal data further exacerbates the problem. Is it possible to synthesize multi-modal training data automatically without compromising diversity and quality? In this paper, we propose a new method, Oasis, to synthesize high-quality multi-modal data with only images. Oasis breaks through traditional methods by prompting only images to the MLLMs, thus extending the data diversity by a large margin. Our method features a delicate quality control method which ensures the data quality. We collected over 500k data and conducted incremental experiments on LLaVA-NeXT. Extensive experiments demonstrate that our method can significantly improve the performance of MLLMs. The image-based synthesis also allows us to focus on the specific-domain ability of MLLMs. Code and data will be publicly available.
Learning Neural Scene Representation from iToF Imaging
Authors: Wenjie Chang; Hanzhi Chang; Yueyi Zhang; Wenfei Yang; Tianzhu Zhang
Venue: ICCV 2025 (main)

Abstract: Indirect Time-of-Flight (iToF) cameras are popular for 3D perception because they are cost-effective and easy to deploy. They emit modulated infrared signals to illuminate the scene and process the received signals to generate amplitude and phase images. The depth is calculated from the phase using the modulation frequency. However, the obtained depth often suffers from noise caused by multi-path interference, low signal-to-noise ratio (SNR), and depth wrapping. Building on recent advancements in neural scene representations, which have shown great potential in 3D modeling from multi-view RGB images, we propose leveraging this approach to reconstruct 3D representations from noisy iToF data. Our method utilizes the multi-view consistency of amplitude and phase maps, fusing information from all input views to generate an accurate scene representation. Considering the impact of infrared illumination, we propose a new rendering scheme for amplitude maps based on signed distance function (SDF) and introduce a neural lighting function to model the appearance variations caused by active illumination. We also incorporate a phase-guided sampling strategy and a wrapping-aware phase-to-depth loss to utilize raw phase information and mitigate depth wrapping. Additionally, we add a noise-weight loss to prevent excessive smoothing information across noisy multi-view measurements. Experiments conducted on synthetic and real-world datasets demonstrate that the proposed method outperforms state-of-the-art techniques.
GeometryCrafter: Consistent Geometry Estimation for Open-world Videos with Diffusion Priors
Authors: Tian-Xing Xu; Xiangjun Gao; Wenbo Hu; Xiaoyu Li; Song-Hai Zhang; Ying Shan
Venue: ICCV 2025 (main)

Abstract: Despite remarkable advancements in video depth estimation, existing methods fall short in geometric fidelity due to their affine-invariant predictions, restricting their applicability in reconstruction and other metrically grounded downstream tasks. We propose a novel point map Variational Autoencoder (VAE) for encoding and decoding unbounded point maps. Notably, its latent space is agnostic to video latent distributions of video diffusion models, allowing us to leverage generation priors to model the distribution of point map sequences conditioned on the input videos. Thus, we can recover high-fidelity point map sequences with temporal coherence from open-world videos, facilitating accurate 3D/4D reconstruction, camera parameter estimation, and other depth-based applications. Extensive evaluations on diverse datasets demonstrate that our method achieves state-of-the-art 3D accuracy, temporal consistency, and generalization capability.
Multi-modal Multi-platform Person Re-Identification: Benchmark and Method
Authors: Ruiyang Ha; Songyi Jiang; Bin Li; Bikang Pan; Yihang Zhu; Junjie Zhang; Xiatian Zhu; Shaogang Gong; Jingya Wang
Venue: ICCV 2025 (main)

Abstract: Conventional person re-identification (ReID) research is often limited to single-modality sensor data from static cameras, which fails to address the complexities of real-world scenarios where multi-modal signals are increasingly prevalent. For instance, consider an urban ReID system integrating stationary RGB cameras, nighttime infrared sensors, and UAVs equipped with dynamic tracking capabilities. Such systems face significant challenges due to variations in camera perspectives, lighting conditions, and sensor modalities, hindering effective person ReID.To address these challenges, we introduce the MP-ReID benchmark, a novel dataset designed specifically for multi-modality and multi-platform ReID. This benchmark uniquely compiles data from 1,930 identities across diverse modalities, including RGB, infrared, and thermal imaging, captured by both UAVs and ground-based cameras in indoor and outdoor environments.Building on this benchmark, we introduce Uni-Prompt ReID, a framework with specific-designed prompts, tailored for cross-modality and cross-platform scenarios. Our method consistently outperforms state-of-the-art approaches, establishing a robust foundation for future research in complex and dynamic ReID environments. Additionally, our dataset will be made publicly available to support further advancements.