GenAI4ELab/papercli-papers huggingface dataset:
Fusion Meets Diverse Conditions: A High-diversity Benchmark and Baseline for UAV-based Multimodal Object Detection with Condition Cues
Authors: Chen Chen; Kangcheng Bin; Ting Hu; Jiahao Qi; Xingyue Liu; Tianpeng Liu; Zhen Liu; Yongxiang Liu; Ping Zhong
Venue: ICCV 2025 (main)

Abstract: Unmanned aerial vehicles (UAV)-based object detection with visible (RGB) and infrared (IR) images facilitates robust around-the-clock detection, driven by advancements in deep learning techniques and the availability of high-quality dataset. However, the existing dataset struggles to fully capture real-world complexity for limited imaging conditions. To this end, we introduce a high-diversity dataset ATR-UMOD covering varying scenarios, spanning altitudes from 80m to 300m, angles from 0deg to 75deg, and all-day, all-year time variations in rich weather and illumination conditions. Moreover, each RGB-IR image pair is annotated with 6 condition attributes, offering valuable high-level contextual information. To meet the challenge raised by such diverse conditions, we propose a novel prompt-guided condition-aware dynamic fusion (PCDF) to adaptively reassign multimodal contributions by leveraging annotated condition cues. By encoding imaging conditions as text prompts, PCDF effectively models the relationship between conditions and multimodal contributions through a task-specific soft-gating transformation. A prompt-guided condition-decoupling module further ensures the availability in practice without condition annotations. Experiments on ATR-UMOD dataset reveal the effectiveness of PCDF.
Stealthy Backdoor Attack in Federated Learning via Adaptive Layer-wise Gradient Alignment
Authors: Qingqian Yang; Peishen Yan; Xiaoyu Wu; Jiaru Zhang; Tao Song; Yang Hua; Hao Wang; Liangliang Wang; Haibing Guan
Venue: ICCV 2025 (main)

Abstract: The distributed nature of federated learning exposes it to significant security threats, among which backdoor attacks are one of the most prevalent. However, existing backdoor attacks face a trade-off between attack strength and stealthiness: attacks maximizing the attack strength are often detectable, while stealthier approaches significantly reduce the effectiveness of the attack itself. Both of them result in ineffective backdoor injection. In this paper, we propose an adaptive layer-wise gradient alignment strategy to effectively evade various robust defense mechanisms while preserving attack strength. Without requiring additional knowledge, we leverage the previous global update as a reference for alignment to ensure stealthiness during dynamic FL training. This fine-grained alignment strategy applies appropriate constraints to each layer, which helps significantly maintain attack strength. To demonstrate the effectiveness of our method, we conduct extensive evaluations across a wide range of datasets and networks. Our experimental results show that the proposed attack effectively bypasses eight state-of-the-art defenses and achieves high backdoor accuracy, outperforming existing attacks by up to 54.76%. Additionally, it significantly preserves attack strength and maintains robust performance across diverse scenarios, highlighting its adaptability and generalizability. Code implementation is available at https://github.com/yqqhyqq/LGA.
Hybrid Layout Control for Diffusion Transformer: Fewer Annotations, Superior Aesthetics
Authors: Keming Wu; Junwen Chen; Zhanhao Liang; Yinuo Wang; Ji Li; Chao Zhang; Bin Wang; Yuhui Yuan
Venue: ICCV 2025 (main)

Abstract: Text-to-image generation models often struggle to interpret spatially aware text prompts effectively. To overcome this, existing approaches typically require millions of high-quality semantic layout annotations consisting of bounding boxes and regional prompts. This paper shows that the large amounts of regional prompts are non-necessary for the latest diffusion transformers like SD3 or FLUX.In this paper, we propose an efficient hybrid layout framework for diffusion transformers. Our approach drastically reduces need for extensive layout annotations and minimizes reliance on regional prompt annotations--incurring only minimal additional computational cost during inference--while maintaining high-quality layout adherence. Our key insight is to break the layout-control task into two sequential stages: first, generating the target objects within the designated regions specified by an anonymous layout, and second, refining these outputs to ensure they strictly adhere to the regional prompts in the semantic layout. Building on this insight, we propose a hybrid layout control scheme that first fine-tunes the DiTs (e.g., SD3) to follow an anonymous layout, then continues fine-tuning the DiTs to follow the semantic layout, and finally includes a quality-tuning stage to enhance visual aesthetics. We show that this hybrid design is highly data-efficient, as we find only using a small amount of semantic layout annotations is sufficient, thereby significantly reducing dependency on regional prompts. In addition, we propose an efficient regional diffusion transformer to encode the spatial layout information using just a set of lower-resolution regional tokens instead of various carefully designed layout tokens. The region-wise diffusion loss over these regional tokens can guide the diffusion transformer learn to follow the given layout implicitly. We empirically validate the effectiveness of our approach by comparing it with the latest version of SiamLayout and show that our method achieves better results while being more than 10 times more data efficient and ensuring superior aesthetics.
BUFFER-X: Towards Zero-Shot Point Cloud Registration in Diverse Scenes
Authors: Minkyun Seo; Hyungtae Lim; Kanghee Lee; Luca Carlone; Jaesik Park
Venue: ICCV 2025 (main)

Abstract: Recent advances in deep learning-based point cloud registration have improved generalization, yet most methods still require retraining or manual parameter tuning for each new environment. In this paper, we identify three key factors limiting generalization: (a) reliance on environment-specific voxel size and search radius, (b) poor out-of-domain robustness of learning-based keypoint detectors,and (c) raw coordinate usage, which exacerbates scale discrepancies. To address these issues, we present a zero-shot registration pipeline called BUFFER-X by (a) adaptively determining voxel size/search radii, (b) using farthest point sampling to bypass learned detectors, and (c) leveraging patch-wise scale normalization for consistent coordinate bounds. In particular, we present a multi-scale patch-based descriptor generation and a hierarchical inlier search across scales to improve robustness in diverse scenes. We also propose a novel generalizability benchmark using 11 datasets that cover various indoor/outdoor scenarios and sensor modalities, demonstrating that BUFFER-X achieves substantial generalization without prior information or manual parameter tuning for the test datasets. Our code is available at https://github.com/MIT-SPARK/BUFFER-X.
VITAL: More Understandable Feature Visualization through Distribution Alignment and Relevant Information Flow
Authors: Ada Görgün; Bernt Schiele; Jonas Fischer
Venue: ICCV 2025 (main)

Abstract: Neural networks are widely adopted to solve complex and challenging tasks. Especially in high-stakes decision-making, understanding their reasoning process is crucial, yet proves challenging for modern deep networks. Feature visualization (FV) is a powerful tool to decode what information neurons are responding to and hence to better understand the reasoning behind such networks. In particular, in FV we generate human-understandable images that reflect the information detected by neurons of interest. However, current methods often yield unrecognizable visualizations, exhibiting repetitive patterns and visual artifacts that are hard to understand for a human. To address these problems, we propose to guide FV through **statistics of real image features** combined with measures of **relevant network flow** to generate prototypical images. Our approach yields human-understandable visualizations that both qualitatively and quantitatively improve over state-of-the-art FVs across various architectures. As such, it can be used to decode **which** information the network uses, complementing mechanistic circuits that identify **where** it is encoded.
DisCoPatch: Taming Adversarially-driven Batch Statistics for Improved Out-of-Distribution Detection
Authors: Francisco Caetano; Christiaan Viviers; Luis A. Zavala-Mondragón; Peter H.N. De With; Fons van der Sommen
Venue: ICCV 2025 (main)

Abstract: Out-of-distribution (OOD) detection holds significant importance across many applications. While semantic and domain-shift OOD problems are well-studied, this work focuses on covariate shifts - subtle variations in the data distribution that can degrade machine learning performance. We hypothesize that detecting these subtle shifts can improve our understanding of in-distribution boundaries, ultimately improving OOD detection. In adversarial discriminators trained with Batch Normalization (BN), real and adversarial samples form distinct domains with unique batch statistics - a property we exploit for OOD detection. We introduce DisCoPatch, an unsupervised Adversarial Variational Autoencoder (VAE) framework that harnesses this mechanism. During inference, batches consist of patches from the same image, ensuring a consistent data distribution that allows the model to rely on batch statistics. DisCoPatch uses the VAE's suboptimal outputs (generated and reconstructed) as negative samples to train the discriminator, thereby improving its ability to delineate the boundary between in-distribution samples and covariate shifts. By tightening this boundary, DisCoPatch achieves state-of-the-art results in public OOD detection benchmarks. The proposed model not only excels in detecting covariate shifts, achieving 95.5% AUROC on ImageNet-1K(-C), but also outperforms all prior methods on public Near-OOD (95.0%) benchmarks. With a compact model size of 25MB, it achieves high OOD detection performance at notably lower latency than existing methods, making it an efficient and practical solution for real-world OOD detection applications.
EC-Flow: Enabling Versatile Robotic Manipulation from Action-Unlabeled Videos via Embodiment-Centric Flow
Authors: Yixiang Chen; Peiyan Li; Yan Huang; Jiabing Yang; Kehan Chen; Liang Wang
Venue: ICCV 2025 (main)

Abstract: Current language-guided robotic manipulation systems often require low-level action-labeled datasets for imitation learning. While object-centric flow prediction methods mitigate this issue, they remain limited to scenarios involving rigid objects with clear displacement and minimal occlusion. In this work, we present Embodiment-Centric Flow (EC-Flow), a framework that directly learns manipulation from action-unlabeled videos by predicting embodiment-centric flow. Our key insight is that incorporating the embodiment's inherent kinematics significantly enhances generalization to versatile manipulation scenarios, including deformable object handling, occlusions, and non-object-displacement tasks. To connect the EC-Flow with language instructions and object interactions, we further introduce a goal-alignment module by jointly optimizing movement consistency and goal-image prediction. Moreover, translating EC-Flow to executable robot actions only requires a standard robot URDF (Unified Robot Description Format) file to specify kinematic constraints across joints, which makes it easy to use in practice. We validate EC-Flow on both simulation (Meta-World) and real-world tasks, demonstrating its state-of-the-art performance in occluded object handling (62% improvement), deformable object manipulation (45% improvement), and non-object-displacement tasks (80% improvement) than prior state-of-the-art object-centric flow methods. More results can be found on our project website: https://ec-flow1.github.io/
One Look is Enough: Seamless Patchwise Refinement for Zero-Shot Monocular Depth Estimation on High-Resolution Images
Authors: Byeongjun Kwon; Munchurl Kim
Venue: ICCV 2025 (main)

Abstract: Zero-shot depth estimation (DE) models exhibit strong generalization performance as they are trained on large-scale datasets. However, existing models struggle with high-resolution images due to the discrepancy in image resolutions of training (with smaller resolutions) and inference (for high resolutions). Processing them at full resolution leads to decreased estimation accuracy on depth with tremendous memory consumption, while downsampling to the training resolution results in blurred edges in the estimated depth images. Prevailing high-resolution depth estimation methods adopt a patch-based approach, which introduces depth discontinuity issues when reassembling the estimated depth patches, resulting in test-time inefficiency. Additionally, to obtain fine-grained depth details, these methods rely on synthetic datasets due to the real-world sparse ground truth depth, leading to poor generalizability. To tackle these limitations, we propose Patch Refine Once (PRO), an efficient and generalizable tile-based framework. Our PRO consists of two key components: (i) Grouped Patch Consistency Training that enhances test-time efficiency while mitigating the depth discontinuity problem by jointly processing four overlapping patches and enforcing a consistency loss on their overlapping regions within a single backpropagation step, and (ii) Bias Free Masking that prevents the DE models from overfitting to dataset-specific biases, enabling better generalization to real-world datasets even after training on synthetic data. Zero-shot evaluations on Booster, ETH3D, Middlebury 2014, and NuScenes demonstrate that our PRO can be seamlessly integrated into existing depth estimation models. It preserves the performance of original depth estimation models even under grid-based inference on high-resolution images, exhibiting minimal depth discontinuities along patch boundaries. Moreover, our PRO achieves significantly faster inference speed compared to prior patch-based methods.
Omegance: A Single Parameter for Various Granularities in Diffusion-Based Synthesis
Authors: Xinyu Hou; Zongsheng Yue; Xiaoming Li; Chen Change Loy
Venue: ICCV 2025 (main)

Abstract: In this work, we show that we only need a single parameter \omega to effectively control granularity in diffusion-based synthesis. This parameter is incorporated during the denoising steps of the diffusion model's reverse process. This simple approach does not require model retraining or architectural modifications and incurs negligible computational overhead, yet enables precise control over the level of details in the generated outputs. Moreover, spatial masks or denoising schedules with varying \omega values can be applied to achieve region-specific or timestep-specific granularity control. External control signals or reference images can guide the creation of precise \omega masks, allowing targeted granularity adjustments. Despite its simplicity, the method demonstrates impressive performance across various image and video synthesis tasks and is adaptable to advanced diffusion models. The code is available at https://github.com/itsmag11/Omegance.
Frequency-Semantic Enhanced Variational Autoencoder for Zero-Shot Skeleton-based Action Recognition
Authors: Wenhan Wu; Zhishuai Guo; Chen Chen; Hongfei Xue; Aidong Lu
Venue: ICCV 2025 (main)

Abstract: Zero-shot skeleton-based action recognition aims to develop models capable of identifying actions beyond the categories encountered during training. Previous approaches have primarily focused on aligning visual and semantic representations but often overlooked the importance of fine-grained action patterns in the semantic space (e.g., the hand movements in drinking water and brushing teeth). To address these limitations, we propose a Frequency-Semantic Enhanced Variational Autoencoder (FS-VAE) to explore the skeleton semantic representation learning with frequency decomposition. FS-VAE consists of three key components: 1) a frequency-based enhancement module with high- and low-frequency adjustments to enrich the skeletal semantics learning and improve the robustness of zero-shot action recognition; 2) a semantic-based action description with multilevel alignment to capture both local details and global correspondence, effectively bridging the semantic gap and compensating for the inherent loss of information in skeleton sequences; 3) a calibrated cross-alignment loss that enables valid skeleton-text pairs to counterbalance ambiguous ones, mitigating discrepancies and ambiguities in skeleton and text features, thereby ensuring robust alignment. Evaluations on the benchmarks demonstrate the effectiveness of our approach, validating that frequency-enhanced semantic features enable robust differentiation of visually and semantically similar action clusters, thereby improving zero-shot action recognition.
CleanPose: Category-Level Object Pose Estimation via Causal Learning and Knowledge Distillation
Authors: Xiao Lin; Yun Peng; Liuyi Wang; Xianyou Zhong; Minghao Zhu; Yi Feng; Jingwei Yang; Chengju Liu; Qijun Chen
Venue: ICCV 2025 (main)

Abstract: In the effort to achieve robust and generalizable category-level object pose estimation, recent methods primarily focus on learning fundamental representations from data. However, the inherent biases within the data are often overlooked: the repeated training samples and similar environments may mislead the models to over-rely on specific patterns, hindering models' performance on novel instances. In this paper, we present CleanPose, a novel method that mitigates the data biases to enhance category-level pose estimation by integrating causal learning and knowledge distillation. By incorporating key causal variables (structural information and hidden confounders) into causal modeling, we propose the causal inference module based on front-door adjustment, which promotes unbiased estimation by reducing potential spurious correlations. Additionally, to further confront the data bias at the feature level, we devise a residual-based knowledge distillation approach to transfer unbiased semantic knowledge from 3D foundation model, providing comprehensive causal supervision. Extensive experiments across multiple benchmarks (REAL275, CAMERA25 and HouseCat6D) hightlight the superiority of proposed CleanPose over state-of-the-art methods. Code will be available at https://github.com/chrislin0621/CleanPose.
Lark: Low-Rank Updates After Knowledge Localization for Few-shot Class-Incremental Learning
Authors: Jinxin Shi; Jiabao Zhao; Yifan Yang; Xingjiao Wu; Jiawen Li; Liang He
Venue: ICCV 2025 (main)

Abstract: For Few-Shot Class-Incremental Learning (FSCIL), direct fine-tuning causes significant parameter shifts, resulting in catastrophic forgetting and increased resource consumption. While, freezing the pre-trained backbone exacerbates the inconsistency between the backbone and the evolving classifier. To overcome these challenges, we introduce a method called Low-Rank updates after knowledge localization (Lark). In the knowledge localization phase, the Fisher Information Matrix is calculated to measure the sensitivity of parameters in different layers to previously acquired knowledge. This phase ultimately identifies the parameters within the model that are most suitable for learning new knowledge. In the subsequent incremental editing phase, a low-rank incremental update strategy is applied. This strategy ensures that the model parameter updates adhere to a Rank-One matrix structure. By doing so, it minimizes alterations to the original parameters, thereby enabling the model to integrate new knowledge while retaining as much of the previous knowledge as possible. Extensive experimental results demonstrate that the Lark method achieves significant performance improvements on the CIFAR100, mini-ImageNet, and CUB200 datasets, surpassing current state-of-the-art methods.
Articulate3D: Holistic Understanding of 3D Scenes as Universal Scene Description
Authors: Anna-Maria Halacheva; Yang Miao; Jan-Nico Zaech; Xi Wang; Luc Van Gool; Danda Pani Paudel
Venue: ICCV 2025 (main)

Abstract: 3D scene understanding is a long-standing challenge in computer vision and a key component in enabling mixed reality, wearable computing, and embodied AI. Providing a solution to these applications requires a multifaceted approach that covers scene-centric, object-centric, as well as interaction-centric capabilities. While there exist numerous datasets approaching the former two problems, the task of understanding interactable and articulated objects is underrepresented and only partly covered in the research field. In this work, we address this shortcoming by introducing: (1) Articulate3D, an expertly curated 3D dataset featuring high-quality manual annotations on 280 indoor scenes. Articulate3D provides 8 types of annotations for articulated objects, covering parts and detailed motion information,all stored in a standardized scene representation format designed for scalable 3D content creation, exchange and seamless integration into simulation environments. (2) USDNet, a novel unified framework capable of simultaneously predicting part segmentation along with a full specification of motion attributes for articulated objects. We evaluate USDNet on Articulate3D as well as two existing datasets, demonstrating the advantage of our unified dense prediction approach. Furthermore, we highlight the value of Articulate3D through cross-dataset and cross-domain evaluations and showcase its applicability in downstream tasks such as scene editing through LLM prompting and robotic policy training for articulated object manipulation. We provide open access to our dataset, benchmark, and method's source code.
DiffPCI: Large Motion Point Cloud frame Interpolation with Diffusion Model
Authors: Tianyu Zhang; Haobo Jiang; Jian Yang; Jin Xie
Venue: ICCV 2025 (main)

Abstract: Point cloud interpolation aims to recover intermediate frames for temporally smoothing a point cloud sequence. However, real-world challenges, such as uneven or large scene motions, cause existing methods to struggle with limited interpolation precision. To address this, we introduce DiffPCI, a novel diffusion interpolation model that formulates the frame interpolation task as a progressive denoising diffusion process. Training DiffPCI involves two key stages: a forward interpolation diffusion process and a reverse interpolation denoising process. In the forward process, the clean intermediate frame is progressively transformed into a noisy one through continuous Gaussian noise injection. The reverse process then focuses on training a denoiser to gradually refine this noisy frame back to the ground-truth frame. In particular, we derive a point cloud interpolation-specific variational lower bound as our optimization objective for denoiser training. Furthermore, to alleviate the interpolation error especially in highly dynamic scenes, we develop a novel full-scale, dual-branch denoiser that enables more comprehensive front-back frame information fusion for robust bi-directional interpolation. Extensive experiments demonstrate that DiffPCI significantly outperforms current state-of-the-art frame interpolation methods (e.g. 27% and 860% reduction in the Chamfer Distance and Earth Mover's Distance on Nuscenes).