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SKU-Patch: Towards Efficient Instance Segmentation for Unseen Objects in
  Auto-Store

SKU-Patch: Towards Efficient Instance Segmentation for Unseen Objects in Auto-Store

8 November 2023
Biqi Yang
Weiliang Tang
Xiaojie Gao
Xianzhi Li
Yunhui Liu
Chi-Wing Fu
Pheng-Ann Heng
ArXivPDFHTML

Papers citing "SKU-Patch: Towards Efficient Instance Segmentation for Unseen Objects in Auto-Store"

6 / 6 papers shown
Title
ViDT: An Efficient and Effective Fully Transformer-based Object Detector
ViDT: An Efficient and Effective Fully Transformer-based Object Detector
Hwanjun Song
Deqing Sun
Sanghyuk Chun
Varun Jampani
Dongyoon Han
Byeongho Heo
Wonjae Kim
Ming-Hsuan Yang
78
75
0
08 Oct 2021
Unseen Object Amodal Instance Segmentation via Hierarchical Occlusion
  Modeling
Unseen Object Amodal Instance Segmentation via Hierarchical Occlusion Modeling
S. Back
Joosoon Lee
Taewon Kim
Sangjun Noh
Raeyoung Kang
Seongho Bak
Kyoobin Lee
29
67
0
23 Sep 2021
Universal-Prototype Enhancing for Few-Shot Object Detection
Universal-Prototype Enhancing for Few-Shot Object Detection
Aming Wu
Yahong Han
Linchao Zhu
Yi Yang
ObjD
26
83
0
01 Mar 2021
CrossTransformers: spatially-aware few-shot transfer
CrossTransformers: spatially-aware few-shot transfer
Carl Doersch
Ankush Gupta
Andrew Zisserman
ViT
201
276
0
22 Jul 2020
Rethinking Object Detection in Retail Stores
Yuanqiang Cai
Longyin Wen
Libo Zhang
Dawei Du
Weiqiang Wang
11
23
0
18 Mar 2020
Meta R-CNN : Towards General Solver for Instance-level Few-shot Learning
Meta R-CNN : Towards General Solver for Instance-level Few-shot Learning
Xiaopeng Yan
Ziliang Chen
Anni Xu
Xiaoxi Wang
Xiaodan Liang
Liang Lin
ObjD
151
440
0
28 Sep 2019
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