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cs.CV2022
Learning Semantic Correspondence with Sparse Annotations
Shuaiyi Huang, Luyu Yang, Bo He +3
Finding dense semantic correspondence is a fundamental problem in computer vision, which remains challenging in complex scenes due to background clutter, extreme intra-class variat…
cs.CV2022
Budget-aware Few-shot Learning via Graph Convolutional Network
Shipeng Yan, Songyang Zhang, Xuming He
This paper tackles the problem of few-shot learning, which aims to learn new visual concepts from a few examples. A common problem setting in few-shot classification assumes random…