5 citations · 6 across the 6 of their papers we have counts for
6 papers
Environment-Invariant Curriculum Relation Learning for Fine-Grained Scene Graph Generation
Yukuan Min, Aming Wu, Cheng Deng
The scene graph generation (SGG) task is designed to identify the predicates based on the subject-object pairs.However,existing datasets generally include two imbalance cases: one…
Siamese Contrastive Embedding Network for Compositional Zero-Shot Learning
Xiangyu Li, Xu Yang, Kun Wei +2
Compositional Zero-Shot Learning (CZSL) aims to recognize unseen compositions formed from seen state and object during training. Since the same state may be various in the visual a…
MSR: Making Self-supervised learning Robust to Aggressive Augmentations
Yingbin Bai, Erkun Yang, Zhaoqing Wang +5
Most recent self-supervised learning methods learn visual representation by contrasting different augmented views of images. Compared with supervised learning, more aggressive augm…
Group Contrastive Self-Supervised Learning on Graphs
Xinyi Xu, Cheng Deng, Yaochen Xie +1
We study self-supervised learning on graphs using contrastive methods. A general scheme of prior methods is to optimize two-view representations of input graphs. In many studies, a…
Domain-Smoothing Network for Zero-Shot Sketch-Based Image Retrieval
Zhipeng Wang, Hao Wang, Jiexi Yan +2
Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR) is a novel cross-modal retrieval task, where abstract sketches are used as queries to retrieve natural images under zero-shot scena…
CerfGAN: A Compact, Effective, Robust, and Fast Model for Unsupervised Multi-Domain Image-to-Image Translation
Xiao Liu, Shengchuan Zhang, Hong Liu +3
In this paper, we aim at solving the multi-domain image-to-image translation problem with a unified model in an unsupervised manner. The most successful work in this area refers to…