1.8k citations · 1.9k across the 14 of their papers we have counts for
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Meta-Ensemble Parameter Learning
Zhengcong Fei, Shuman Tian, Junshi Huang +2
Ensemble of machine learning models yields improved performance as well as robustness. However, their memory requirements and inference costs can be prohibitively high. Knowledge d…
Expansion and Shrinkage of Localization for Weakly-Supervised Semantic Segmentation
Jinlong Li, Zequn Jie, Xu Wang +2
Generating precise class-aware pseudo ground-truths, a.k.a, class activation maps (CAMs), is essential for weakly-supervised semantic segmentation. The original CAM method usually…
Weakly Supervised Semantic Segmentation via Progressive Patch Learning
Jinlong Li, Zequn Jie, Xu Wang +3
Most of the existing semantic segmentation approaches with image-level class labels as supervision, highly rely on the initial class activation map (CAM) generated from the standar…
YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications
Chuyi Li, Lulu Li, Hongliang Jiang +15
For years, the YOLO series has been the de facto industry-level standard for efficient object detection. The YOLO community has prospered overwhelmingly to enrich its use in a mult…
Learn to Cluster Faces via Pairwise Classification
Junfu Liu, Di Qiu, Pengfei Yan +1
Face clustering plays an essential role in exploiting massive unlabeled face data. Recently, graph-based face clustering methods are getting popular for their satisfying performanc…
InsCon:Instance Consistency Feature Representation via Self-Supervised Learning
Junwei Yang, Ke Zhang, Zhaolin Cui +3
Feature representation via self-supervised learning has reached remarkable success in image-level contrastive learning, which brings impressive performances on image classification…