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Bin Liu
Researcher at Chinese Academy of Sciences
Publications - 209
Citations - 3615
Bin Liu is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Video tracking & Recurrent neural network. The author has an hindex of 22, co-authored 206 publications receiving 2185 citations. Previous affiliations of Bin Liu include Stony Brook University & Syracuse University.
Papers
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Proceedings ArticleDOI
Online Multi-object Tracking Using CNN-Based Single Object Tracker with Spatial-Temporal Attention Mechanism
TL;DR: Zhang et al. as mentioned in this paper proposed a spatial-temporal attention mechanism (STAM) to handle the drift caused by occlusion and interaction among targets, which can be considered as temporal attention mechanism.
Proceedings ArticleDOI
Cross-Modality Person Re-Identification With Shared-Specific Feature Transfer
TL;DR: Wang et al. as mentioned in this paper proposed a cross-modality shared-specific feature transfer algorithm (termed cm-SSFT) to explore the potential of both the modality-shared information and the modal-specific characteristics to boost the reID performance.
Book ChapterDOI
Zoom-Net: Mining Deep Feature Interactions for Visual Relationship Recognition
TL;DR: This work presents two new pooling cells to encourage feature interactions, and sheds light on how one could resolve ambiguous and noisy object and predicate annotations by Intra-Hierarchical trees (IH-tree).
Journal ArticleDOI
Medium Access Control for Wireless Body Area Networks with QoS Provisioning and Energy Efficient Design
TL;DR: This paper adopts a TDMA-based protocol and dynamically adjust the transmission order and transmission duration of the nodes based on channel status and application context of WBAN, and designs a new synchronization scheme to reduce the synchronization overhead.
Journal ArticleDOI
Correlation Particle Filter for Visual Tracking
TL;DR: Compared with existing tracking methods based on correlation filters and particle filters, the proposed CPF tracking algorithm has four major advantages: it is robust to partial and total occlusions, and can recover from lost tracks by maintaining multiple hypotheses.