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首次观测 · 记录 #1CVF · CVPR 2026:cvf:240cc7d8e7390da9845bce59de103f5eCVF · CVPR 2026

D2FANet: Enhancing Video Object Detection with Dual-Domain Feature Aggregation Network

Qi, Qiang, Shang, Wenqi, Wang, Meifang, Wang, Xiao

原文摘要

Accurately capturing and aggregating spatiotemporal information has become crucial for video object detection. Previous methods mainly perform feature aggregation in the spatiotemporal domain, treating all regions indiscriminately and overlooking both their relative importance and the frequency characteristics that capture periodic motion patterns. This limits the capability of these methods to capture dynamic interactions and adapt to complex scene variations. In this paper, we propose a novel Dual-Domain Feature Aggregation Network (D2FANet) for video object detection, which, to the best of our knowledge, is the first work to introduce the frequency-domain feature aggregation into the video object detection task. By collaboratively modeling spatiotemporal and frequency information, our D2FANet enhances motion awareness and temporal consistency, thereby improving detection accuracy. First, we develop a frequency-domain feature aggregation module that decomposes frame features into high- and low-frequency distributions and reinforces object query representations through aggregating multi-scale frequency features. Second, we design a spatiotemporal-domain feature aggregation module that leverages an importance guidance mechanism to dynamically emphasize regions of different importance and reinforces object query representations via guiding the aggregation of spatiotemporal features. Experiments on the ImageNet VID and EPIC-KITCHENS datasets demonstrate that D2FANet achieves state-of-the-art performance.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:7b9a405ca1d26f00aa35c1d0a2b0c455CVF · CVPR 2026

When Transformers Meet Mamba: A Hybrid Transformer-Mamba Network for Video Object Detection

Qi, Qiang, Wang, Xiao, Du, Zongyuan, Zhang, Yu

原文摘要

Video object detection has gained notable progress with the advent of transformers. While transformers excel at modeling long-range contextual dependencies, the quadratic complexity limits their efficiency in long-sequence processing. In contrast, Mamba offers greater efficiency in modeling long sequences but tends to exhibit relatively limited contextual learning capability compared with transformers, and its application to video object detection remains unexplored. To harness the complementary strengths of transformers and Mamba, we propose a hybrid Transformer-Mamba network for video object detection (TMambaDet), a pioneering framework in this domain that combines the long-range modeling power of transformers with the efficient long-sequence processing capability of Mamba. Our TMambaDet is characterized by three core components: 1) a spatial adaptive deformable transformer encoder to effectively model the long-range dependencies within each frame, enabling intra-frame feature aggregation that substantially improves the spatial feature representations of objects; 2) a temporal cascaded bidirectional Mamba encoder to efficiently capture the long-range dependencies across frames in video sequences with linear complexity, enabling inter-frame feature aggregation that effectively enhances the temporal feature representations of objects; 3) a Mamba entangled transformer decoder to fully explore the interactions between object queries and spatial-temporal features, enabling fine-grained query-feature alignment that effectively enriches the instance-level representations of object queries. We conduct experiments on the ImageNet VID and EPIC-KITCHENS-55 datasets, showing that TMambaDet achieves state-of-the-art results.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:a6cb44696f0cc2406ff758ff07256246CVF · CVPR 2026

A Temporal and Content Co-Awareness Latent Diffusion for Controllable Hand Image Generation

Hao, Shuang, Ren, Pengfei, Sun, Haifeng, Pan, Ting, Qi, Qi, Zhang, Lei, Liu, Cong, Liao, Jianxin 等 9 位作者

原文摘要

Controllable hand image generation aims to synthesize geometrically accurate images with consistent appearance. Recently, diffusion models have been widely applied for hand image synthesis. However, through input-level fusion or feature-level modulation, existing methods inject control signals with fixed strength across all timesteps, ignoring the progressive nature of the denoising process. In this paper, we reveal that the modulation of control signals depends on the denoising state and condition complexity. Due to distinct semantic distributions and information densities, achieving effective interaction among these heterogeneous representations remains challenging. To address this, we propose a Temporal and Content Co-Awareness Latent Diffusion method that introduces a dual-driven modulation strategy. Specifically, we design a query-based interaction mechanism to mitigate information redundancy and align semantic distributions. Leveraging cross-domain interaction, the model infers required control information to dynamically adjust pose and appearance injection strengths. Furthermore, we design a Pose-Invariant Appearance Encoder that captures both global appearance consistency and local texture details. Extensive experiments validate our superiority over state-of-the-art.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:d26748209a913c6a937db2c7ebf1bef4CVF · CVPR 2026

Fine-VAD: Towards Fine-Grained Video Anomaly Detection via Progressive Cross-Granularity Learning

Zhang, Menghao, Zhu, Yiyan, Ren, Pengfei, Sun, Haifeng, Qi, Qi, Zhuang, Zirui, Wang, Huazheng, Zhang, Lei 等 10 位作者

原文摘要

In this paper, we explore video anomaly detection (VAD) from a fine-grained perspective, which aims not only to detect anomalous events but also to identify their specific categories. Due to the limited number of examples per category, existing methods either fail to handle intra-class variation across diverse contexts or struggle with inter-class confusion caused by shared visual primitives. To address these challenges, we propose a progressive cross-granularity learning paradigm that leverages coarse- and fine-grained labels in a complementary manner to progressively refine representations from generic anomaly patterns to category-specific semantics. Building on this paradigm, we develop Fine-VAD, a progressive alignment framework that aligns video features with supervision signals at multiple granularities. Extensive experiments on two benchmark datasets demonstrate that Fine-VAD achieves up to 47.7% relative improvement in fine-grained anomaly classification. Notably, our paradigm generalizes well across diverse model architectures, offering an adaptable and effective solution for real-world fine-grained VAD.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:d26cab6c67397e1710609e63aaa26590CVF · CVPR 2026

Alert-CLIP: Abnormality-aware Latent-Enhanced Representation Tuning of CLIP for Video Anomaly Detection

Zhu, Yiyan, Zhang, Menghao, Sun, Haifeng, Ren, Pengfei, Chu, Xianao, Xu, Chenye, Tan, Hong, Wang, Jinghan 等 10 位作者

原文摘要

With the rise of pre-trained vision-language models such as CLIP, performing video anomaly detection (VAD) through cross-modal reasoning has become an emerging trend. However, we observe that CLIP still suffers from weak abnormality awareness: normal and abnormal descriptions are highly entangled in the text embedding space, causing video features to assign nearly indistinguishable similarity scores to both types of prompts. To address this issue, we propose Alert-CLIP, an abnormality-aware latent-enhanced tuning framework that tailors CLIP for VAD. Alert-CLIP introduces a multi-level alignment strategy: (1) video-label alignment, which reshapes the semantic space to establish a coarse-level foundation for abnormality awareness; (2) region-text alignment, which explicitly associates anomaly-related regions with their detailed descriptions to strengthen fine-grained perception; and (3) region-semantic alignment, which further contrasts anomalous regions against multiple hard negative samples, enhancing abnormality-aware discrimination.Extensive experiments on four benchmarks demonstrate that Alert-CLIP consistently surpasses vanilla CLIP across supervised, zero-shot, and open-vocabulary settings, providing a solid foundation for future CLIP-based VAD research.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:ef0c8423bd94f1e7b7a570eac230aaa5CVF · CVPR 2026

Clay-to-Stone: Phase-wise 3D Gaussian Splatting for Monocular Articulated Hand-Object Manipulation Modeling

Liu, Xingyu, Ren, Pengfei, Qi, Qi, Sun, Haifeng, Zhuang, Zirui, Liao, Jianxin, Wang, Jingyu

原文摘要

Understanding interacting hand-object from monocular videos is crucial for immersive and dexterous interactions in AR/VR and robotic applications. However, existing monocular reconstruction methods primarily assume rigid grasping and static object geometry. When applied to articulated manipulations, the continuous joint rotations and frequent component deformations introduce a strong coupling between shape and motion, leading to severe ambiguity and instability in articulation optimization under monocular observation. To address this challenge, we propose a Clay-to-Stone dual-phase framework, modeling the articulated manipulation at hierarchical granularities, enabling a progression from flexible semantic exploration to structured articulation recovery. In the CLAY phase, our method performs fine-grained control over geometric deformation, guided by inter-part semantic correlation learning. As semantic and motion priors emerge, the STONE phase enforces rigid constraints to consolidate articulated structures and explicitly estimates motion parameters. Experiments on a real-world manipulation dataset show that our method achieves state-of-the-art reconstruction quality and plausible articulation modeling from monocular videos. Code is available at https://github.com/ru1ven/ARGS.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗