GenAI4ELab/papercli-papers huggingface dataset:
Video Individual Counting for Moving Drones
Authors: Yaowu Fan; Jia Wan; Tao Han; Antoni B. Chan; Andy J. Ma
Venue: ICCV 2025 (main)

Abstract: Video Individual Counting (VIC) has received increasing attention for its importance in intelligent video surveillance. Existing works are limited in two aspects, i.e., dataset and method. Previous datasets are captured with fixed or rarely moving cameras with relatively sparse individuals, restricting evaluation for a highly varying view and time in crowded scenes. Existing methods rely on localization followed by association or classification, which struggle under dense and dynamic conditions due to inaccurate localization of small targets. To address these issues, we introduce the MovingDroneCrowd Dataset, featuring videos captured by fast-moving drones in crowded scenes under diverse illuminations, shooting heights and angles. We further propose a Shared Density map-guided Network (SDNet) using a Depth-wise Cross-Frame Attention (DCFA) module to directly estimate shared density maps between consecutive frames, from which the inflow and outflow density maps are derived by subtracting the shared density maps from the global density maps. The inflow density maps across frames are summed up to obtain the number of unique pedestrians in a video. Experiments on our datasets and publicly available ones show the superiority of our method over the state of the arts in highly dynamic and complex crowded scenes. Our dataset and codes have been released publicly.
BridgeDepth: Bridging Monocular and Stereo Reasoning with Latent Alignment
Authors: Tongfan Guan; Jiaxin Guo; Chen Wang; Yun-Hui Liu
Venue: ICCV 2025 (main)

Abstract: Monocular and stereo depth estimation offer complementary strengths: monocular methods capture rich contextual priors but lack geometric precision, while stereo approaches leverage epipolar geometry yet struggle with ambiguities such as reflective or textureless surfaces. Despite post-hoc synergies, these paradigms remain largely disjoint in practice. We introduce OmniDepth, a unified framework that bridges both through iterative bidirectional alignment of their latent representations. At its core, a novel cross-attentive alignment mechanism dynamically synchronizes monocular contextual cues with stereo hypothesis representations during stereo reasoning. This mutual alignment resolves stereo ambiguities (e.g., specular surfaces) by injecting monocular structure priors while refining monocular depth with stereo geometry within a single network.Extensive experiments demonstrate state-of-the-art results: OmniDepth reduces zero-shot generalization error by \!>\!40% on Middlebury and ETH3D, while addressing longstanding failures on transparent and reflective surfaces. By harmonizing multi-view geometry with monocular context, OmniDepth enables robust 3D perception that transcends modality-specific limitations. Codes available at https://github.com/aeolusguan/OmniDepth.
SuperEvent: Cross-Modal Learning of Event-based Keypoint Detection for SLAM
Authors: Yannick Burkhardt; Simon Schaefer; Stefan Leutenegger
Venue: ICCV 2025 (main)

Abstract: Event-based keypoint detection and matching holds significant potential, enabling the integration of event sensors into highly optimized Visual SLAM systems developed for frame cameras over decades of research. Unfortunately, existing approaches struggle with the motion-dependent appearance of keypoints and the complex noise prevalent in event streams, resulting in severely limited feature matching capabilities and poor performance on downstream tasks. To mitigate this problem, we propose SuperEvent, a data-driven approach to predict stable keypoints with expressive descriptors. Due to the absence of event datasets with ground truth keypoint labels, we leverage existing frame-based keypoint detectors on readily available event-aligned and synchronized gray-scale frames for self-supervision: we generate temporally sparse keypoint pseudo-labels considering that events are a product of both scene appearance and camera motion. Combined with our novel, information-rich event representation, we enable SuperEvent to effectively learn robust keypoint detection and description in event streams. Finally, we demonstrate the usefulness of SuperEvent by its integration into a modern sparse keypoint and descriptor-based SLAM framework originally developed for traditional cameras, surpassing the state-of-the-art in event-based SLAM by a wide margin. Source code is available at https://ethz-mrl.github.io/SuperEvent/.
ViLLa: Video Reasoning Segmentation with Large Language Model
Authors: Rongkun Zheng; Lu Qi; Xi Chen; Yi Wang; Kun Wang; Hengshuang Zhao
Venue: ICCV 2025 (main)

Abstract: Recent efforts in video reasoning segmentation (VRS) integrate large language models (LLMs) with perception models to localize and track objects via textual instructions, achieving barely satisfactory results in simple scenarios. However, they struggled to discriminate and deduce the objects from user queries in more real-world scenes featured by long durations, multiple objects, rapid motion, and heavy occlusions. In this work, we analyze the underlying causes of these limitations, and present **ViLLa**: **Vi**deo reasoning segmentation with **L**arge **La**nguage Model. Remarkably, our ViLLa manages to tackle these challenges through multiple core innovations: (1) a context synthesizer that dynamically encodes the user intent with video contexts for accurate reasoning, resolving ambiguities in complex queries, and (2) a hierarchical temporal synchronizer that disentangles multi-object interactions across complex temporal scenarios by modelling multi-object interactions at local and global temporal scales. To enable efficient processing of long videos, ViLLa incorporates (3) a key segment sampler that adaptively partitions long videos into shorter but semantically dense segments for less redundancy. What's more, to promote research in this unexplored area, we construct a VRS benchmark, **VideoReasonSeg**, featuring different complex scenarios. Our model also exhibits impressive state-of-the-art results on VideoReasonSeg, Ref-YouTube-VOS, Ref-DAVIS17, MeViS, and ReVOS. Both quantitative and qualitative experiments demonstrate that our method effectively enhances video reasoning segmentation capabilities for multimodal LLMs.
Bridging Class Imbalance and Partial Labeling via Spectral-Balanced Energy Propagation for Skeleton-based Action Recognition
Authors: Yandan Wang; Chenqi Guo; Yinglong Ma; Jiangyan Chen; Yuan Gao; Weiming Dong
Venue: ICCV 2025 (main)

Abstract: Skeleton-based action recognition faces class imbalance and insufficient labeling problems in real-world applications. Existing methods typically address these issues separately, lacking a unified framework that can effectively handle both issues simultaneously while considering their inherent relationships. Our theoretical analysis reveals two fundamental connections between these problems. First, class imbalance systematically shifts the eigenvalue spectrum of normalized affinity matrices, compromising both convergence and accuracy of label propagation. Second, boundary samples are critical for model training under imbalanced conditions but are often mistakenly excluded by conventional reliability metrics, which focus on relative class differences rather than holistic connectivity patterns. Built upon these theoretical findings, we propose SpeLER (Spectral-balanced Label Propagation with Energy-based Tightened Reliability), which introduces a spectral balancing technique that explicitly counteracts spectral shifts by incorporating class distribution information. Meanwhile, a propagation energy-based tightened reliability measure is proposed to better preserve crucial boundary samples by evaluating holistic connectivity patterns. Extensive experiments on six public datasets demonstrate that SpeLER consistently outperforms state-of-the-art methods, validating both our theoretical findings and practical effectiveness.
PRVQL: Progressive Knowledge-guided Refinement for Robust Egocentric Visual Query Localization
Authors: Bing Fan; Yunhe Feng; Yapeng Tian; James Chenhao Liang; Yuewei Lin; Yan Huang; Heng Fan
Venue: ICCV 2025 (main)

Abstract: Egocentric visual query localization (EgoVQL) focuses on localizing the target of interest in space and time from first-person videos, given a visual query. Despite recent progressive, existing methods often struggle to handle severe object appearance changes and cluttering background in the video due to lacking sufficient target cues, leading to degradation. Addressing this, we introduce PRVQL, a novel Progressive knowledge-guided Refinement framework for EgoVQL. The core is to continuously exploit target-relevant knowledge directly from videos and utilize it as guidance to refine both query and video features for improving target localization. Our PRVQL contains multiple processing stages. The target knowledge from one stage, comprising appearance and spatial knowledge extracted via two specially designed knowledge learning modules, are utilized as guidance to refine the query and videos features for the next stage, which are used to generate more accurate knowledge for further feature refinement. With such a progressive process, target knowledge in PRVQL can be gradually improved, which, in turn, leads to better refined query and video features for localization in the final stage. Compared to previous methods, our PRVQL, besides the given object cues, enjoys additional crucial target information from a video as guidance to refine features, and hence enhances EgoVQL in complicated scenes. In our experiments on challenging Ego4D, PRVQL achieves state-of-the-art result and largely surpasses other methods, showing its efficacy. Our code, model, and results will be released at https://github.com/fb-reps/PRVQL.
ARIG: Autoregressive Interactive Head Generation for Real-time Conversations
Authors: Ying Guo; Xi Liu; Cheng Zhen; Pengfei Yan; Xiaoming Wei
Venue: ICCV 2025 (main)

Abstract: Face-to-face communication, as a common human activity, motivates the research on interactive head generation. A virtual agent can generate motion responses with both listening and speaking capabilities based on the audio or motion signals of the other user and itself. However, previous clip-wise generation paradigm or explicit listener/speaker generator-switching methods have limitations in future signal acquisition, contextual behavioral understanding, and switching smoothness, making it challenging to be real-time and realistic.In this paper, we propose an autoregressive (AR) based frame-wise framework called ARIG to realize the real-time generation with better interaction realism. To achieve real-time generation, we model motion prediction as a non-vector-quantized AR process. Unlike discrete codebook-index prediction, we represent motion distribution using diffusion procedure, achieving more accurate predictions in continuous space. To improve interaction realism, we emphasize interactive behavior understanding (IBU) and detailed conversational state understanding (CSU). In IBU, based on dual-track dual-modal signals, we summarize short-range behaviors through bidirectional-integrated learning and perform contextual understanding over long ranges. In CSU, we use voice activity signals and context features of IBU to understand the various states (interruption, feedback, pause, etc.) that exist in actual conversations. These serve as conditions for the final progressive motion prediction. Extensive experiments have verified the effectiveness of our model.
RARE: Refine Any Registration of Pairwise Point Clouds via Zero-Shot Learning
Authors: Chengyu Zheng; Jin Huang; Honghua Chen; Mingqiang Wei
Venue: ICCV 2025 (main)

Abstract: Recent research leveraging large-scale pretrained diffusion models has demonstrated the potential of using diffusion features to establish semantic correspondences in images. Inspired by advancements in diffusion-based techniques, we propose a novel zero-shot method for refining point cloud registration algorithms. Our approach leverages correspondences derived from depth images to enhance point feature representations, eliminating the need for a dedicated training dataset. Specifically, we first project the point cloud into depth maps from multiple perspectives and extract implicit knowledge from a pretrained diffusion network as depth diffusion features. These features are then integrated with geometric features obtained from existing methods to establish more accurate correspondences between point clouds. By leveraging these refined correspondences, our approach achieves significantly improved registration accuracy. Extensive experiments demonstrate that our method not only enhances the performance of existing point cloud registration techniques but also exhibits robust generalization capabilities across diverse datasets. Codes are available a https://github.com/zhengcy-lambo/RARE.git.
Bayesian-Inspired Space-Time Superpixels
Authors: Kent Gauen; Stanley Chan
Venue: ICCV 2025 (main)

Abstract: This paper presents Bayesian-inspired Space-Time Superpixels (BIST): a fast, state-of-the-art method to compute space-time superpixels. BIST is a novel extension of a single-image Bayesian method named BASS, and it is inspired by hill-climbing to a local mode of a Dirichlet-Process Gaussian Mixture Model (DP-GMM). The method is only Bayesian-inspired, rather than actually Bayesian, because it includes heuristic modifications to the theoretically correct sampler. Similar to existing methods, BIST can adapt the number of superpixels to an individual frame using split-merge steps. A key novelty is a new temporal coherence term in the split step, which reduces the chance of splitting propagated superpixels. This term enforces temporal coherence in propagated regions, but allows for unconstrained adaptation in disoccluded regions. A hyperparameter determines the strength of this new term, which does not require special tuning to return consistent results across a dataset of videos. The wall-clock runtime of BIST is over twice as fast as BASS and over 30 times faster than the next fastest space-time superpixel method with open-source code.
FEVER-OOD: Free Energy Vulnerability Elimination for Robust Out-of-Distribution Detection
Authors: Brian K.S. Isaac-Medina; Mauricio Che; Yona Falinie A. Gaus; Samet Akcay; Toby P. Breckon
Venue: ICCV 2025 (main)

Abstract: Modern machine learning models, that excel on computer vision tasks such as classification and object detection, are often overconfident in their predictions for Out-of-Distribution (OOD) examples, resulting in unpredictable behaviour for open-set environments. Recent works have demonstrated that the free energy score is an effective measure of uncertainty for OOD detection given its close relationship to the data distribution. However, despite that free energy-based methods representing a significant empirical advance in OOD detection, our theoretical analysis reveals previously unexplored and inherent vulnerabilities within the free energy score formulation such that in-distribution and OOD instances can have distinct feature representations yet identical free energy scores. This phenomenon occurs when the vector representing the feature space difference between the in-distribution and OOD sample belongs to the null space of the last layer of a neural-based classifier. To mitigate these issues, we explore lower-dimensional feature spaces to reduce the null space footprint and introduce novel regularisation to maximize the least singular value of the final linear layer, hence enhancing inter-sample free energy separation. We refer to these techniques as Free Energy Vulnerability Elimination for Robust Out-of-Distribution Detection (FEVER-OOD). Extensive experimentation shows that FEVER-OOD improves OOD detection in CIFAR-10 with 28.22% FPR95 and 94.78 AUROC vs 33.66% FPR95 and 92.15 AUROC of the baseline model, CIFAR-100 with 42.77% FPR95 and 89.98 AUROC vs 50.96% FPR95 and 88.06 AUROC of the baseline model, and achieves state of the art OOD detection in Imagenet-100, with average OOD FPR95 of 36.50% and an AUROC of 92.74 when used with the Dream-OOD model, compared with a 39.33% and 91.84 AUROC without FEVER-OOD.
TRNAS: A Training-Free Robust Neural Architecture Search
Authors: Yeming Yang; Qingling Zhu; Jianping Luo; Ka-Chun Wong; Qiuzhen Lin; Jianqiang Li
Venue: ICCV 2025 (main)

Abstract: Deep Neural Networks (DNNs) have been successfully applied in various computer tasks. However, they remain vulnerable to adversarial attacks, which could lead to severe security risks. In recent years, robust neural architecture search (NAS) has gradually become an emerging direction for designing adversarially robust architectures. However, existing robust NAS methods rely on repeatedly training numerous DNNs to evaluate robustness, which makes the search process extremely expensive. In this paper, we propose a training-free robust NAS method (TRNAS) that significantly reduces search costs. First, we design a zero-cost proxy model (R-Score) that formalizes adversarial robustness evaluation by exploring the theory of DNN's linear activation capability and feature consistency. This proxy only requires initialized weights for evaluation, thereby avoiding expensive adversarial training costs. Secondly, we introduce a multi-objective selection (MOS) strategy to save candidate architectures with robustness and compactness. Experimental results show that TRNAS only requires 0.02 GPU days to find a promising robust architecture in a vast search space including approximately 10^ 20  networks. TRNAS surpasses other state-of-the-art robust NAS methods under both white-box and black-box attacks. Finally, we summarize a few meaningful conclusions for designing the robust architecture and promoting the development of robust NAS field.
Hierarchical Divide-and-Conquer Grouping for Classification Adaptation of Pre-Trained Models
Authors: Ziqian Lu; Yunlong Yu; Qinyue Tong; Jun Liu
Venue: ICCV 2025 (main)

Abstract: Existing adaptation methods of pre-trained vision-language models like CLIP often rely on base-class samples during fine-tuning, introducing systematic biases that distort decision boundaries and degrade performance on novel classes. In this work, we break new ground by proposing a hierarchical divide-and-conquer framework that addresses classification bias at its root. Our method first segregates the label space into base and novel subspaces, ensuring domain separation. Subsequently, it employs text-embedding clustering within each subspace to decompose ambiguous intra-domain classes into disentangled, fine-grained clusters. This two-stage grouping strategy not only alleviates class confusion but also enables domain-specific model training in isolated subspaces, fostering specialized learning without overfitting base categories. Experiments on three classification benchmarks reveal that our approach achieves state-of-the-art performance, surpassing the second-best competitor by 10% average accuracy.
Text-IRSTD: Leveraging Semantic Text to Promote Infrared Small Target Detection in Complex Scenes
Authors: Feng Huang; Shuyuan Zheng; Zhaobing Qiu; Huanxian Liu; Huanxin Bai; Liqiong Chen
Venue: ICCV 2025 (main)

Abstract: Infrared small target detection is currently a hot and challenging task in computer vision. Existing methods usually focus on mining visual features of targets, which struggles to cope with complex and diverse detection scenarios. The main reason is that infrared small targets have limited image information on their own, thus relying only on visual features fails to discriminate targets and interferences, leading to lower detection performance. To address this issue, we introduce a novel approach leveraging semantic text to guide infrared small target detection, called Text-IRSTD. It innovatively expands classical IRSTD to text-guided IRSTD, providing a new research idea. On the one hand, we devise a novel fuzzy semantic text prompt to accommodate ambiguous target categories. On the other hand, we propose a progressive cross-modal semantic interaction decoder (PCSID) to facilitate information fusion between texts and images. In addition, we construct a new benchmark consisting of 2,755 infrared images of different scenarios with fuzzy semantic textual annotations, called FZDT. Extensive experimental results demonstrate that our method achieves better detection performance and target contour recovery than the state-of-the-art methods. Moreover, proposed Text-IRSTD shows strong generalization and wide application prospects in unseen detection scenarios. The dataset and code will be publicly released after acceptance of this paper. The dataset and code are available at https://github.com/Zhengsy0407/Text-IRSTD.
IntrinsicControlNet: Cross-distribution Image Generation with Real and Unreal
Authors: Jiayuan Lu; Rengan Xie; Zixuan Xie; Zhizhen Wu; Dianbing Xi; Qi Ye; Rui Wang; Hujun Bao; Yuchi Huo
Venue: ICCV 2025 (main)

Abstract: Realistic images are usually produced by simulating light transportation results of 3D scenes using rendering engines. This framework can precisely control the output but is usually weak at producing photo-like images. Alternatively, diffusion models have seen great success in photorealistic image generation by leveraging priors from large datasets of real-world images but lack affordance controls. Promisingly, the recent ControlNet enables flexible control of the diffusion model without degrading its generation quality. In this work, we introduce IntrinsicControlNet, an intrinsically controllable image generation framework that enables easily generating photorealistic images from precise and explicit control, similar to a rendering engine, by using intrinsic images such as material properties, geometric details, and lighting as network inputs. Beyond this, we notice that there is a domain gap between the synthetic and real-world datasets, and therefore, naively blending these datasets yields domain confusion. To address this problem, we present a cross-domain control architecture that extracts control information from synthetic datasets, and control and content information from real-world datasets. This bridges the domain gap between real-world and synthetic datasets, enabling the blending or editing of 3D assets and real-world photos to support various interesting applications. Experiments and user studies demonstrate that our method can generate explicitly controllable and highly photorealistic images based on the input intrinsic images.
Hypergraph Clustering Network with Partial Attribute Imputation
Authors: Qianqian Wang; Bowen Zhao; Zhengming Ding; Wei Feng; Quanxue Gao
Venue: ICCV 2025 (main)

Abstract: Existing hypergraph clustering methods typically assume that node attributes are fully available. However, in real-world scenarios, missing node attributes are common for the sake of privacy or due to data noise. While some approaches attempt to handle missing attributes in traditional graphs, they are not designed for hypergraphs, which encode higher-order relationships and introduce additional challenges. To bridge this gap, we propose Hypergraph Clustering Network with Partial Attribute Imputation (HCN-PAI). Specifically, we first leverage higher-order neighborhood propagation to impute missing node attributes by minimizing the Dirichlet energy, ensuring smooth feature propagation across the hypergraph. Next, we introduce a hypergraph smoothing preprocessing that efficiently captures structural information, replacing the hypergraph convolution operation, and significantly reducing computational costs. Finally, we design a dual-level contrastive mechanism, which employs two independent MLPs to encode node representations into two distinct views and enforces consistency at both node and hyperedge levels. Extensive experiments on multiple benchmark datasets validate the effectiveness and superiority of our proposed method.