23 citations · 57 across the 11 of their papers we have counts for
13 papers
On the Importance of Distractors for Few-Shot Classification
Rajshekhar Das, Yu-Xiong Wang, JoséM. F. Moura
Few-shot classification aims at classifying categories of a novel task by learning from just a few (typically, 1 to 5) labelled examples. An effective approach to few-shot classifi…
Pixel Contrastive-Consistent Semi-Supervised Semantic Segmentation
Yuanyi Zhong, Bodi Yuan, Hong Wu +3
We present a novel semi-supervised semantic segmentation method which jointly achieves two desiderata of segmentation model regularities: the label-space consistency property betwe…
Generative Modeling for Multi-task Visual Learning
Zhipeng Bao, Martial Hebert, Yu-Xiong Wang
Generative modeling has recently shown great promise in computer vision, but it has mostly focused on synthesizing visually realistic images. In this paper, motivated by multi-task…
Hallucination Improves Few-Shot Object Detection
Weilin Zhang, Yu-Xiong Wang
Learning to detect novel objects from few annotated examples is of great practical importance. A particularly challenging yet common regime occurs when there are extremely limited…
Image-Level or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed Detection
Nadine Chang, Zhiding Yu, Yu-Xiong Wang +3
Training on datasets with long-tailed distributions has been challenging for major recognition tasks such as classification and detection. To deal with this challenge, image resamp…
DAP: Detection-Aware Pre-training with Weak Supervision
Yuanyi Zhong, Jianfeng Wang, Lijuan Wang +3
This paper presents a detection-aware pre-training (DAP) approach, which leverages only weakly-labeled classification-style datasets (e.g., ImageNet) for pre-training, but is speci…