GenAI4ELab/papercli-papers huggingface dataset:
Contact-Aware Amodal Completion for Human-Object Interaction via Multi-Regional Inpainting
Authors: Seunggeun Chi; Enna Sachdeva; Pin-Hao Huang; Kwonjoon Lee
Venue: ICCV 2025 (main)

Abstract: Amodal completion, the task of inferring the complete appearance of objects despite partial occlusions, is crucial for understanding complex human-object interactions (HOI) in computer vision and robotics. Existing methods, including pre-trained diffusion models, often struggle to generate plausible completions in dynamic scenarios due to their limited understanding of HOI. To address this challenge, we propose a novel approach that leverages physical prior knowledge alongside a specialized multi-regional inpainting technique tailored for HOI. By incorporating physical constraints derived from human topology and contact information, we define two distinct regions: the primary region, where occluded object parts are most likely to reside, and the secondary region, where occlusions are less probable. Our multi-regional inpainting method employs customized denoising strategies across these regions within a diffusion model, thereby enhancing the accuracy and realism of generated completions in both shape and visual detail. Experimental results demonstrate that our approach substantially outperforms existing methods in HOI scenarios, advancing machine perception toward a more human-like understanding of dynamic environments. Furthermore, we show that our pipeline remains robust even without ground-truth contact annotations, broadening its applicability to tasks such as 3D reconstruction and novel view/pose synthesis.
SeqGrowGraph: Learning Lane Topology as a Chain of Graph Expansions
Authors: Mengwei Xie; Shuang Zeng; Xinyuan Chang; Xinran Liu; Zheng Pan; Mu Xu; Xing Wei
Venue: ICCV 2025 (main)

Abstract: Accurate lane topology is essential for autonomous driving, yet traditional methods struggle to model the complex, non-linear structures--such as loops and bidirectional lanes--prevalent in real-world road structure. We present SeqGrowGraph, a novel framework that learns lane topology as a chain of graph expansions, inspired by human map-drawing processes. Representing the lane graph as a directed graph G=(V,E), with intersections (V) and centerlines (E), SeqGrowGraph incrementally constructs this graph by introducing one vertex at a time. At each step, an adjacency matrix (A) expands from nn to (n+1)(n+1) to encode connectivity, while a geometric matrix (M) captures centerline shapes as quadratic Bezier curves. The graph is serialized into sequences, enabling a transformer model to autoregressively predict the chain of expansions, guided by a depth-first search ordering. Evaluated on nuScenes and Argoverse 2 datasets, SeqGrowGraph achieves state-of-the-art performance.
Ultra-Precision 6DoF Pose Estimation Using 2-D Interpolated Discrete Fourier Transform
Authors: Guowei Shi; Zian Mao; Peisen Huang
Venue: ICCV 2025 (main)

Abstract: Ultra-precision estimation of 6DoF pose is essential in applications such as semiconductor manufacturing and nanoscale manipulation. Conventional vision-based techniques are often hampered by sensitivity to defocus and limited estimation accuracy. In this paper, we propose a novel two-dimensional interpolated Discrete Fourier Transform (2D-IpDFT) method for robust 6DoF pose estimation using periodic patterns. We further develop a mathematical framework that links image parameters--phase and frequency--to 6DoF pose, which is applicable to both orthographic and quasi-orthographic imaging systems. Extensive experiments on a low-cost setup, featuring an industrial camera and an etched checkerboard pattern, demonstrate translation estimation accuracy at the nanometer level and rotation estimation accuracy at the microradian level.
Neural Compression for 3D Geometry Sets
Authors: Siyu Ren; Junhui Hou; Weiyao Lin; Wenping Wang
Venue: ICCV 2025 (main)

Abstract: We present NeCGS, the first neural compression paradigm, which can compress a geometry set encompassing thousands of detailed and diverse 3D mesh models by up to 900 times with high accuracy and preservation of detailed geometric structures. Specifically, we first propose TSDF-Def, a new implicit representation that is capable of accurately representing irregular 3D mesh models with various structures into regular 4D tensors of uniform and compact size, where 3D surfaces can be extracted through the deformable marching cubes. Then we construct a quantization-aware auto-decoder network architecture to regress these 4D tensors to explore the local geometric similarity within each shape and across different shapes for redundancy removal, resulting in more compact representations, including an embedded feature of a smaller size associated with each 3D model and a network parameter shared by all models. We finally encode the resulting features and network parameters into bitstreams through entropy coding. Besides, our NeCGS can handle the dynamic scenario well, where new 3D models are constantly added to a compressed set. Extensive experiments and ablation studies demonstrate the significant advantages of our NeCGS over state-of-the-art methods both quantitatively and qualitatively. The source code is publicly available at https://github.com/rsy6318/NeCGS.
Lifting the Structural Morphing for Wide-Angle Images Rectification: Unified Content and Boundary Modeling
Authors: Wenting Luan; Siqi Lu; Yongbin Zheng; Wanying Xu; Lang Nie; Zongtan Zhou; Kang Liao
Venue: ICCV 2025 (main)

Abstract: The mainstream approach for correcting distortions in wide-angle images typically involves a cascading process of rectification followed by rectangling. These tasks address distorted image content and irregular boundaries separately, using two distinct pipelines. However, this independent optimization prevents the two stages from benefiting each other. It also increases susceptibility to error accumulation and misaligned optimization, ultimately degrading the quality of the rectified image and the performance of downstream vision tasks. In this work, we observe and verify that transformations based on motion representations ((e.g.,, Thin-Plate Spline) exhibit structural continuity in both rectification and rectangling tasks. This continuity enables us to establish their relationships through the perspective of structural morphing, allowing for an optimal solution within a single end-to-end framework. To this end, we propose ConBo-Net, a unified Content and Boundary modeling approach for one-stage wide-angle image correction. Our method jointly addresses distortion rectification and boundary rectangling in an end-to-end manner. To further enhance the model's structural recovery capability, we incorporate physical priors based on the wide-angle camera model during training and introduce an ordinal geometric loss to enforce curvature monotonicity. Extensive experiments demonstrate that ConBo-Net outperforms state-of-the-art two-stage solutions. The code and dataset are available at https://github.com/lwttttt/ConBo-Net
ASGS: Single-Domain Generalizable Open-Set Object Detection via Adaptive Subgraph Searching
Authors: Yuxuan Yuan; Luyao Tang; Yixin Chen; Chaoqi Chen; Yue Huang; Xinghao Ding
Venue: ICCV 2025 (main)

Abstract: Albeit existing Single-Domain Generalized Object Detection (Single-DGOD) methods enable models to generalize to unseen domains, most assume that the training and testing data share the same label space. In real-world scenarios, unseen domains often introduce previously unknown objects, a challenge that has been largely overlooked. In this paper, we tackle the practical problem of Single-domain Generalizable Open-Set Object Detection (SG-OSOD), which addresses both unseen domains and unknown classes. We identify two key challenges: (1) detecting unknown classes with only known-class data, and (2) learning robust features to mitigate domain shift. To address these challenges, we propose the framework termed \texttt ASGS , which leverages adaptive subgraph structures to enhance the understanding of unknown scenes and classes. \texttt ASGS  consists of Subgraph-wise Unknown-class Learning (SUL) and Class-wise Embedding Compaction (CEC). SUL employs non-parametric methods to detect unknown samples and performs Adaptive Subgraph Searching (ASS) for high-order structural feature extraction, enabling domain-robust unknown class learning. Moreover, the CEC module enhances class discrimination robustness through contrastive learning, which results in more compact class clusters in unknown scenarios. Experimental results demonstrate the effectiveness of the proposed \texttt ASGS .
Dual-Temporal Exemplar Representation Network for Video Semantic Segmentation
Authors: Xiaolong Xu; Lei Zhang; Jiayi Li; Lituan Wang; Yifan Guan; Yu Yan; Leyi Zhang; Hao Song
Venue: ICCV 2025 (main)

Abstract: Video semantic segmentation aims to assign a class label for each pixel in every video frame. Existing methods predominantly follow the reference-target interaction paradigm, focusing on extracting local temporal contexts while neglecting the integration of global temporal information. Moreover, complex dynamics and varying lighting conditions introduce inter-frame intra-class discrepancies in feature representations, leading to unstable predictions. In this paper, we propose a novel framework, the Dual-Temporal Exemplar Representation Network (DTERN), which utilizes the strong representational capability of cluster centers, i.e., exemplars, to effectively model both local and global temporal information. DTERN consists of two core modules: 1) the Local Temporal Exemplar Module (LTEM), which constructs local exemplars to capture local temporal contexts, ensuring stable and reliable predictions. 2) the Global Temporal Exemplar Module (GTEM), which introduces learnable global exemplars to dynamically model global temporal information, thereby improving the effective consistency of segmentation. Furthermore, we observe that the existing Video Consistency (VC) metric fails to evaluate segmentation accuracy and lacks sensitivity to small-object segmentation. To this end, we propose Video Effective Consistency (VEC) to comprehensively evaluate temporal consistency and segmentation effectiveness. Experiments on VSPW and Cityscape demonstrate that DTERN outperforms state-of-the-art methods. The code is available at https://github.com/zlxilo/DTERN.
UniConvNet: Expanding Effective Receptive Field while Maintaining Asymptotically Gaussian Distribution for ConvNets of Any Scale
Authors: Yuhao Wang; Wei Xi
Venue: ICCV 2025 (main)

Abstract: Convolutional neural networks (ConvNets) with large effective receptive field (ERF), still in their early stages, have demonstrated promising effectiveness while constrained by high parameters and FLOPs costs and disrupted asymptotically Gaussian distribution (AGD) of ERF. This paper proposes an alternative paradigm: rather than merely employing extremely large ERF, it is more effective and effcient to expand the ERF while maintaining AGD of ERF by proper combination of smaller kernels, such as 7x 7 , 9x 9 , 11x 11 . This paper introduces a Three-layer Receptive Field Aggregator and designs a Layer Operator as the fundamental operator from the perspective of receptive field. The ERF can be expanded to the level of existing large-kernel ConvNets through the stack of proposed modules while maintaining AGD of ERF. Using these designs, we propose a universal ConvNet, termed UniConvNet. Extensive experiments on ImageNet-1K, COCO2017, and ADE20K demonstrate that UniConvNet outperforms state-of-the-art CNNs and ViTs across various vision recognition tasks for both lightweight and large-scale models with comparable throughput. Surprisingly, UniConvNet-T achieves 84.2% ImageNet top-1 accuracy with 30M parameters and 5.1G FLOPs. UniConvNet-XL also shows competitive scalability to big data and large models, acquiring 88.4% top-1 accuracy on ImageNet.
Denoising Token Prediction in Masked Autoregressive Models
Authors: Ting Yao; Yehao Li; Yingwei Pan; Zhaofan Qiu; Tao Mei
Venue: ICCV 2025 (main)

Abstract: Autoregressive models are just at a tipping point where they could really take off for visual generation. In this paper, we propose to model token prediction using diffusion procedure particularly in masked autoregressive models for image generation. We look into the problem from two critical perspectives: progressively refining the unmasked tokens prediction via a denoising head with the autoregressive model, and representing masked tokens probability distribution by capitalizing on the interdependency across masked and unmasked tokens through a diffusion head. Our proposal harbors an innate agency that remains advantageous in the speed of sequence prediction, and strongly favors high capability in generating quality samples by leveraging the principles of denoising diffusion process. Extensive experiments on both class-conditional and text-to-image tasks demonstrate its superiority, achieving the state-of-the-art FID score of 1.47 and 5.27 on ImageNet and MSCOCO datasets, respectively. More remarkably, our approach leads to 45% speedup in the inference time of image generation against the diffusion models such as DiT-XL/2.
Uncertainty-Aware Gradient Stabilization for Small Object Detection
Authors: Huixin Sun; Yanjing Li; Linlin Yang; Xianbin Cao; Baochang Zhang
Venue: ICCV 2025 (main)

Abstract: Despite advances in generic object detection, there remains a performance gap in detecting small objects compared to normal-scale objects. We reveal that conventional object localization methods suffer from gradient instability in small objects due to sharper loss curvature, leading to a convergence challenge. To address the issue, we propose Uncertainty-Aware Gradient Stabilization (UGS), a framework that reformulates object localization as a classification task to stabilize gradients. UGS quantizes continuous labels into interval non-uniform discrete representations. Under a classification-based objective, the localization branch generates bounded and confidence-driven gradients, mitigating instability. Furthermore, UGS integrates an uncertainty minimization (UM) loss that reduces prediction variance and an uncertainty-guided refinement (UR) module that identifies and refines high-uncertainty regions via perturbations. Evaluated on four benchmarks, UGS consistently improves anchor-based, anchor-free, and leading small object detectors. Notably, UGS enhances DINO-5scale by 2.6 AP on VisDrone, surpassing prior state-of-the-art performance.
Radiant Foam: Real-Time Differentiable Ray Tracing
Authors: Shrisudhan Govindarajan; Daniel Rebain; Kwang Moo Yi; Andrea Tagliasacchi
Venue: ICCV 2025 (main)

Abstract: Research on differentiable scene representations is consistently moving towards more efficient, real-time models. Recently, this has led to the popularization of splatting methods, which eschew the traditional ray-based rendering of radiance fields in favor of rasterization. This has yielded a significant improvement in rendering speeds due to the efficiency of rasterization algorithms and hardware, but has come at a cost: the approximations that make rasterization efficient also make implementation of light transport phenomena like reflection and refraction much more difficult. We propose a novel scene representation which avoids these approximations, but keeps the efficiency and reconstruction quality of splatting by leveraging a decades-old efficient volumetric mesh ray tracing algorithm which has been largely overlooked in recent computer vision research. The resulting model, which we name Radiant Foam, achieves rendering speed and quality comparable to Gaussian Splatting, without the constraints of rasterization. Unlike ray traced Gaussian models that use hardware ray tracing acceleration, our method requires no special hardware or APIs beyond the standard features of a programmable GPU.
Attention to the Burstiness in Visual Prompt Tuning!
Authors: Yuzhu Wang; Manni Duan; Shu Kong
Venue: ICCV 2025 (main)

Abstract: Visual Prompt Tuning (VPT) is a parameter-efficient finetuning technique that adapts a pre-trained vision Transformer (ViT) by learning a small set of parameters in the input space, known as prompts. In VPT, we uncover "burstiness" in the values arising from the interaction of image patch embeddings, and the key and query projectors within Transformer's self-attention module. Interestingly, the values of patch embeddings and the key and query projectors exhibit Laplacian and hyper-Laplacian distribution, respectively. Intuitively, these non-Gaussian distributions pose challenges for learning prompts. To address this, we propose whitening these data, de-correlating them and equalizing their variance towards more Gaussian before learning prompts. We derive the whitening matrix over random image patch embeddings and ViT's key and query projectors, and multiply it with the prompt to be learned in a bilinear manner.Surprisingly, this method significantly accelerates prompt tuning and boosts accuracy, e.g., >25 points on the CUB dataset; interestingly, it learns "bursty prompts".As bilinear models are known to introduce burstiness, we present a compact method by learning two small sets of parameters whose multiplication yields the final prompts. We call the proposed methods Bilinear Prompt Tuning (BPT). Extensive experiments demonstrate that BPT methods not only outperform various VPT methods across multiple benchmark datasets but also reduce parameter count and computation overhead.
BadVideo: Stealthy Backdoor Attack against Text-to-Video Generation
Authors: Ruotong Wang; Mingli Zhu; Jiarong Ou; Rui Chen; Xin Tao; Pengfei Wan; Baoyuan Wu
Venue: ICCV 2025 (main)

Abstract: Text-to-video (T2V) generative models have rapidly advanced and found widespread applications across fields like entertainment, education, and marketing. However, the adversarial vulnerabilities of these models remain rarely explored. We observe that in T2V generation tasks, the generated videos often contain substantial redundant information not explicitly specified in the text prompts, such as environmental elements, secondary objects, and additional details, providing opportunities for malicious attackers to embed hidden harmful content. Exploiting this inherent redundancy, we introduce BadVideo, the first backdoor attack framework tailored for T2V generation. Our attack focuses on designing target adversarial outputs through two key strategies: (1) Spatio-Temporal Composition, which combines different spatiotemporal features to encode malicious information; (2) Dynamic Element Transformation, which introduces transformations in redundant elements over time to convey malicious information. Based on these strategies, the attacker's malicious target seamlessly integrates with the user's textual instructions, providing high stealthiness. Moreover, by exploiting the temporal dimension of videos, our attack successfully evades traditional content moderation systems that primarily analyze spatial information within individual frames. Extensive experiments demonstrate that BadVideo achieves high attack success rates while preserving original semantics and maintaining excellent performance on clean inputs. Overall, our work reveals the adversarial vulnerability of T2V models, calling attention to potential risks and misuse. Our project page is at https://wrt2000.github.io/BadVideo2025/.
Cassic: Towards Content-Adaptive State-Space Models for Learned Image Compression
Authors: Shiyu Qin; Jinpeng Wang; Yimin Zhou; Bin Chen; Tianci Luo; Baoyi An; Tao Dai; Shu-Tao Xia; Yaowei Wang
Venue: ICCV 2025 (main)

Abstract: Learned image compression (LIC) demonstrates superior rate-distortion (RD) performance compared to traditional methods. Recent method MambaVC attempts to introduce Mamba, a variant of state space models, into this field aim to establish a new paradigm beyond convolutional neural networks and transformers. However, this approach relies on predefined four-directional scanning, which prioritizes spatial proximity over content and semantic relationships, resulting in suboptimal redundancy elimination. Additionally, it focuses solely on nonlinear transformations, neglecting entropy model improvements crucial for accurate probability estimation in entropy coding. To address these limitations, we propose a novel framework based on content-adaptive visual state space model, Cassic, through dual innovation.First, we design a content-adaptive selective scan based on weighted activation maps and bit allocation maps, subsequently developing a content-adaptive visual state space block. Second, we present a mamba-based channel-wise auto-regressive entropy model to fully leverage inter-slice bit allocation consistency for enhanced probability estimation. Extensive experimental results demonstrate that our method achieves state-of-the-art performance across three datasets while maintaining faster processing speeds than existing MambaVC approach.
AIGI-Holmes: Towards Explainable and Generalizable AI-Generated Image Detection via Multimodal Large Language Models
Authors: Ziyin Zhou; Yunpeng Luo; Yuanchen Wu; Ke Sun; Jiayi Ji; Ke Yan; Shouhong Ding; Xiaoshuai Sun; Yunsheng Wu; Rongrong Ji
Venue: ICCV 2025 (main)

Abstract: The rapid development of AI-generated content (AIGC) technology has led to the misuse of highly realistic AI-generated images (AIGI) in spreading misinformation, posing a threat to public information security. Although existing AIGI detection techniques are generally effective, they face two issues: 1) a lack of human-verifiable explanations, and 2) a lack of generalization in the latest generation technology. To address these issues, we introduce a large-scale and comprehensive dataset, Holmes-Set, which includes the Holmes-SFTSet, an instruction-tuning dataset with explanations on whether images are AI-generated, and the Holmes-DPOSet, a human-aligned preference dataset. Our work introduces an efficient data annotation method called the Multi-Expert Jury, enhancing data generation through structured MLLM explanations and quality control via cross-model evaluation, expert defect filtering, and human preference modification. In addition, we propose Holmes Pipeline, a meticulously designed three-stage training framework comprising visual expert pre-training, supervised fine-tuning, and direct preference optimization. Holmes Pipeline adapts multimodal large language models (MLLMs) for AIGI detection while generating human-verifiable and human-aligned explanations, ultimately yielding our model AIGI-Holmes. During the inference stage, we introduce a collaborative decoding strategy that integrates the model perception of the visual expert with the semantic reasoning of MLLMs, further enhancing the generalization capabilities. Extensive experiments on three benchmarks validate the effectiveness of our AIGI-Holmes.