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20112025
most citedOpenOOD: Benchmarking Generalized Out-of-Distribution Detection

87 citations · 502 across the 49 of their papers we have counts for

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Showing 2020Show all

7 papers · 1 filter

cs.CV2020★ 11 cited

Webly Supervised Image Classification with Metadata: Automatic Noisy Label Correction via Visual-Semantic Graph

Jingkang Yang, Weirong Chen, Litong Feng +3

Webly supervised learning becomes attractive recently for its efficiency in data expansion without expensive human labeling. However, adopting search queries or hashtags as web lab…

cs.CV2020★ 1 cited

Webly Supervised Image Classification with Self-Contained Confidence

Jingkang Yang, Litong Feng, Weirong Chen +4

This paper focuses on webly supervised learning (WSL), where datasets are built by crawling samples from the Internet and directly using search queries as web labels. Although WSL…

cs.CV2020★ 4 cited

Context-Aware RCNN: A Baseline for Action Detection in Videos

Jianchao Wu, Zhanghui Kuang, Limin Wang +2

Video action detection approaches usually conduct actor-centric action recognition over RoI-pooled features following the standard pipeline of Faster-RCNN. In this work, we first e…

cs.CV2020★ 13 cited

RobustScanner: Dynamically Enhancing Positional Clues for Robust Text Recognition

Xiaoyu Yue, Zhanghui Kuang, Chenhao Lin +2

The attention-based encoder-decoder framework has recently achieved impressive results for scene text recognition, and many variants have emerged with improvements in recognition q…

cs.LG2020

Maximum-and-Concatenation Networks

Xingyu Xie, Hao Kong, Jianlong Wu +3

While successful in many fields, deep neural networks (DNNs) still suffer from some open problems such as bad local minima and unsatisfactory generalization performance. In this wo…

cs.CV2020★ 7 cited

Scale-Equalizing Pyramid Convolution for Object Detection

Xinjiang Wang, Shilong Zhang, Zhuoran Yu +2

Feature pyramid has been an efficient method to extract features at different scales. Development over this method mainly focuses on aggregating contextual information at different…