Search Results for author: Jiarui Cai

Found 6 papers, 0 papers with code

Hyperbolic Learning with Synthetic Captions for Open-World Detection

no code implementations7 Apr 2024 Fanjie Kong, Yanbei Chen, Jiarui Cai, Davide Modolo

Specifically, we bootstrap dense synthetic captions using pre-trained VLMs to provide rich descriptions on different regions in images, and incorporate these captions to train a novel detector that generalizes to novel concepts.

Hallucination Novel Concepts +3

MeMOT: Multi-Object Tracking with Memory

no code implementations CVPR 2022 Jiarui Cai, Mingze Xu, Wei Li, Yuanjun Xiong, Wei Xia, Zhuowen Tu, Stefano Soatto

We propose an online tracking algorithm that performs the object detection and data association under a common framework, capable of linking objects after a long time span.

Multi-Object Tracking Object +2

ACE: Ally Complementary Experts for Solving Long-Tailed Recognition in One-Shot

no code implementations ICCV 2021 Jiarui Cai, Yizhou Wang, Jenq-Neng Hwang

One-stage long-tailed recognition methods improve the overall performance in a "seesaw" manner, i. e., either sacrifice the head's accuracy for better tail classification or elevate the head's accuracy even higher but ignore the tail.

Long-tail Learning

Multi-Target Multi-Camera Tracking of Vehicles using Metadata-Aided Re-ID and Trajectory-Based Camera Link Model

no code implementations3 May 2021 Hung-Min Hsu, Jiarui Cai, Yizhou Wang, Jenq-Neng Hwang, Kwang-Ju Kim

In this paper, we propose a novel framework for multi-target multi-camera tracking (MTMCT) of vehicles based on metadata-aided re-identification (MA-ReID) and the trajectory-based camera link model (TCLM).

Clustering

IA-MOT: Instance-Aware Multi-Object Tracking with Motion Consistency

no code implementations24 Jun 2020 Jiarui Cai, Yizhou Wang, Haotian Zhang, Hung-Min Hsu, Chengqian Ma, Jenq-Neng Hwang

Meanwhile, the spatial attention, which focuses on the foreground within the bounding boxes, is generated from the given instance masks and applied to the extracted embedding features.

Multi-Object Tracking Multiple Object Tracking +1

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