Search Results for author: Le Kang

Found 7 papers, 2 papers with code

SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in Soccer Videos

no code implementations14 Apr 2022 Anthony Cioppa, Silvio Giancola, Adrien Deliege, Le Kang, Xin Zhou, Zhiyu Cheng, Bernard Ghanem, Marc Van Droogenbroeck

Tracking objects in soccer videos is extremely important to gather both player and team statistics, whether it is to estimate the total distance run, the ball possession or the team formation.

Multiple Object Tracking

ASM-Loc: Action-aware Segment Modeling for Weakly-Supervised Temporal Action Localization

1 code implementation29 Mar 2022 Bo He, Xitong Yang, Le Kang, Zhiyu Cheng, Xin Zhou, Abhinav Shrivastava

Without the boundary information of action segments, existing methods mostly rely on multiple instance learning (MIL), where the predictions of unlabeled instances (i. e., video snippets) are supervised by classifying labeled bags (i. e., untrimmed videos).

14 Weakly Supervised Temporal Action Localization

Feature Combination Meets Attention: Baidu Soccer Embeddings and Transformer based Temporal Detection

1 code implementation28 Jun 2021 Xin Zhou, Le Kang, Zhiyu Cheng, Bo He, Jingyu Xin

With rapidly evolving internet technologies and emerging tools, sports related videos generated online are increasing at an unprecedentedly fast pace.

Action Recognition Action Spotting +2

Sketch-based 3D Shape Retrieval using Convolutional Neural Networks

no code implementations CVPR 2015 Fang Wang, Le Kang, Yi Li

Almost always in state of the art approaches a large amount of "best views" are computed for 3D models, with the hope that the query sketch matches one of these 2D projections of 3D models using predefined features.

3D Shape Classification 3D Shape Retrieval +1

Orientation Robust Text Line Detection in Natural Images

no code implementations CVPR 2014 Le Kang, Yi Li, David Doermann

In this paper, higher-order correlation clustering (HOCC) is used for text line detection in natural images.

graph partitioning Line Detection

Convolutional Neural Networks for No-Reference Image Quality Assessment

no code implementations CVPR 2014 Le Kang, Peng Ye, Yi Li, David Doermann

In this work we describe a Convolutional Neural Network (CNN) to accurately predict image quality without a reference image.

No-Reference Image Quality Assessment

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