1.8k citations · 2.8k across the 32 of their papers we have counts for
13 papers · 1 filter
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…
Learning Point-Language Hierarchical Alignment for 3D Visual Grounding
Jiaming Chen, Weixin Luo, Ran Song +3
This paper presents a novel hierarchical alignment model (HAM) that learns multi-granularity visual and linguistic representations in an end-to-end manner. We extract key points an…
SegViT: Semantic Segmentation with Plain Vision Transformers
Bowen Zhang, Zhi Tian, Quan Tang +4
We explore the capability of plain Vision Transformers (ViTs) for semantic segmentation and propose the SegVit. Previous ViT-based segmentation networks usually learn a pixel-level…
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…