64 citations · 138 across the 28 of their papers we have counts for
7 papers
CRCNet: Few-shot Segmentation with Cross-Reference and Region-Global Conditional Networks
Weide Liu, Chi Zhang, Guosheng Lin +1
Few-shot segmentation aims to learn a segmentation model that can be generalized to novel classes with only a few training images. In this paper, we propose a Cross-Reference and L…
Paired Cross-Modal Data Augmentation for Fine-Grained Image-to-Text Retrieval
Hao Wang, Guosheng Lin, Steven C. H. Hoi +1
This paper investigates an open research problem of generating text-image pairs to improve the training of fine-grained image-to-text cross-modal retrieval task, and proposes a nov…
Dual Adaptive Transformations for Weakly Supervised Point Cloud Segmentation
Zhonghua Wu, Yicheng Wu, Guosheng Lin +2
Weakly supervised point cloud segmentation, i.e. semantically segmenting a point cloud with only a few labeled points in the whole 3D scene, is highly desirable due to the heavy bu…
Few-shot Open-set Recognition Using Background as Unknowns
Nan Song, Chi Zhang, Guosheng Lin
Few-shot open-set recognition aims to classify both seen and novel images given only limited training data of seen classes. The challenge of this task is that the model is required…
Sequential Person Recognition in Photo Albums with a Recurrent Network
Yao Li, Guosheng Lin, Bohan Zhuang +3
Recognizing the identities of people in everyday photos is still a very challenging problem for machine vision, due to non-frontal faces, changes in clothing, location, lighting an…
RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
Guosheng Lin, Anton Milan, Chunhua Shen +1
Recently, very deep convolutional neural networks (CNNs) have shown outstanding performance in object recognition and have also been the first choice for dense classification probl…