Prof. Jie Zhang Profile

Prof. Jie Zhang

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SPIE Journal Paper | 28 December 2024

Skeleton-weighted and multi-scale temporal-driven network for video action recognition

Ziqi Xu, Jie Zhang, Peng Zhang, Pengfei Ding

JEI, Vol. 33, Issue 06, 063056, (December 2024) https://doi.org/10.1117/12.10.1117/1.JEI.33.6.063056

KEYWORDS: Video, RGB color model, Action recognition, Modeling, Data modeling, Motion models, Visualization, Feature extraction, Convolution, Transformers

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Sequential and causal relationships among actions are critical for accurate video interpretation. Therefore, capturing both short-term and long-term temporal information is essential for effective action recognition. Current research, however, primarily focuses on fusing spatial features from diverse modalities for short-term action recognition, inadequately modeling the complex temporal dependencies in videos, leading to suboptimal performance. To address this limitation, we propose a skeleton-weighted and multi-scale temporal-driven action recognition network that integrates RGB and skeleton modalities to effectively capture both short-term and long-term temporal information. First, we propose a temporal-enhanced adaptive graph convolutional network. This network derives motion attention masks from the skeletal joints and transfers them to RGB videos to generate visually salient regions, thereby achieving a concise and effective input representation. Subsequently, we develop a multi-scale local–global temporal modeling network driven by a self-attention mechanism, which effectively captures fine-grained local details of individual actions along with global temporal relationships among actions across multiple temporal resolutions. Moreover, we design a multi-level adaptive temporal scale mixer module that efficiently integrates multi-scale features, creating a unified temporal feature representation to ensure temporal consistency. Finally, we conducted extensive experiments on the NTU-RGBD-60, NTU-RGBD-120, NW-UCLA, and Kinetics datasets to validate the effectiveness of the proposed method.

Proceedings Article | 10 November 2022 Paper

NDANN: efficient SSD-based approximate nearest neighbor search through navigation

Kaixiang Yang, Hongya Wang, Zongyuan Tan, Jie Zhang

Proceedings Volume 12331, 123313K (2022) https://doi.org/10.1117/12.2652299

KEYWORDS: Quantization, Multimedia, Computer science

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Graph-based Approximate Nearest Neighbor Search (ANNS) algorithms have attracted more attention due to their better performance. Numerous ANNS optimization methods have been proposed by researchers. However, Current graph-based ANNS algorithms still cannot index billion-scale datasets on a single server with 256GB RAM. Although several researchers have investigated on this problem, we believe there is still an improvement in reducing disk accesses. In this paper, we provide a fast navigation layer to decrease the number of disk accesses by assisting query points in swiftly reaching the range of strongly connected components. Compared with the state-of-the-art ANNS algorithms, NDANN reduces the mean latency by about 20% under the same recall.

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