Search Results for author: Jingyu Liu

Found 10 papers, 4 papers with code

CLIP2TV: An Empirical Study on Transformer-based Methods for Video-Text Retrieval

no code implementations10 Nov 2021 Zijian Gao, Jingyu Liu, Sheng Chen, Dedan Chang, Hao Zhang, Jinwei Yuan

Modern video-text retrieval frameworks basically consist of three parts: video encoder, text encoder and the similarity head.

Ranked #2 on Video Retrieval on MSR-VTT (using extra training data)

Representation Learning Video-Text Retrieval

Coarse to Fine: Video Retrieval before Moment Localization

no code implementations14 Oct 2021 Zijian Gao, Huanyu Liu, Jingyu Liu

The current state-of-the-art methods for video corpus moment retrieval (VCMR) often use similarity-based feature alignment approach for the sake of convenience and speed.

Moment Retrieval Video Corpus Moment Retrieval +1

A Structure-Aware Relation Network for Thoracic Diseases Detection and Segmentation

1 code implementation21 Apr 2021 Jie Lian, Jingyu Liu, Shu Zhang, Kai Gao, Xiaoqing Liu, Dingwen Zhang, Yizhou Yu

Leveraging on constant structure and disease relations extracted from domain knowledge, we propose a structure-aware relation network (SAR-Net) extending Mask R-CNN.

Instance Segmentation Object Detection

ChestX-Det10: Chest X-ray Dataset on Detection of Thoracic Abnormalities

1 code implementation17 Jun 2020 Jingyu Liu, Jie Lian, Yizhou Yu

Instance level detection of thoracic diseases or abnormalities are crucial for automatic diagnosis in chest X-ray images.

Classification General Classification

Align, Attend and Locate: Chest X-Ray Diagnosis via Contrast Induced Attention Network With Limited Supervision

no code implementations ICCV 2019 Jingyu Liu, Gangming Zhao, Yu Fei, Ming Zhang, Yizhou Wang, Yizhou Yu

We show that the use of contrastive attention and alignment module allows the model to learn rich identification and localization information using only a small amount of location annotations, resulting in state-of-the-art performance in NIH chest X-ray dataset.

Contrastive Learning

Verification Code Recognition Based on Active and Deep Learning

no code implementations12 Feb 2019 Dongliang Xu, Bailing Wang, XiaoJiang Du, Xiaoyan Zhu, zhitao Guan, Xiaoyan Yu, Jingyu Liu

However, the advantages of convolutional neural networks depend on the data used by the training classifier, particularly the size of the training set.

Referring Expression Generation and Comprehension via Attributes

no code implementations ICCV 2017 Jingyu Liu, Liang Wang, Ming-Hsuan Yang

In this paper, we explore the role of attributes by incorporating them into both referring expression generation and comprehension.

Referring Expression Referring expression generation

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