activity
20152023
most citedThiNet: A Filter Level Pruning Method for Deep Neural Network Compression

105 citations · 400 across the 29 of their papers we have counts for

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Showing 2020Show all

10 papers · 1 filter

cs.CV2020

Delving into the Cyclic Mechanism in Semi-supervised Video Object Segmentation

Yuxi Li, Ning Xu, Jinlong Peng +2

In this paper, we address several inadequacies of current video object segmentation pipelines. Firstly, a cyclic mechanism is incorporated to the standard semi-supervised process t…

cs.CV2020

Discriminative Sounding Objects Localization via Self-supervised Audiovisual Matching

Di Hu, Rui Qian, Minyue Jiang +5

Discriminatively localizing sounding objects in cocktail-party, i.e., mixed sound scenes, is commonplace for humans, but still challenging for machines. In this paper, we propose a…

cs.MM2020★ 26 cited

Key-Point Sequence Lossless Compression for Intelligent Video Analysis

Weiyao Lin, Xiaoyi He, Wenrui Dai +4

Feature coding has been recently considered to facilitate intelligent video analysis for urban computing. Instead of raw videos, extracted features in the front-end are encoded and…

cs.CV2020

Finding Action Tubes with a Sparse-to-Dense Framework

Yuxi Li, Weiyao Lin, Tao Wang +5

The task of spatial-temporal action detection has attracted increasing attention among researchers. Existing dominant methods solve this problem by relying on short-term informatio…

cs.CV2020

CFAD: Coarse-to-Fine Action Detector for Spatiotemporal Action Localization

Yuxi Li, Weiyao Lin, John See +4

Most current pipelines for spatio-temporal action localization connect frame-wise or clip-wise detection results to generate action proposals, where only local information is explo…

cs.CV2020

AP-Loss for Accurate One-Stage Object Detection

Kean Chen, Weiyao Lin, Jianguo Li +3

One-stage object detectors are trained by optimizing classification-loss and localization-loss simultaneously, with the former suffering much from extreme foreground-background cla…