activity
20182022
most citedVariational Context: Exploiting Visual and Textual Context for Grounding Referring Expressions

33 citations · 74 across the 3 of their papers we have counts for

collaborators

8 papers

cs.CV20228 cited

Respecting Transfer Gap in Knowledge Distillation

Yulei Niu, Long Chen, Chang Zhou +1

Knowledge distillation (KD) is essentially a process of transferring a teacher model's behavior, e.g., network response, to a student model. The network response serves as addition…

cs.CV202133 cited

Introspective Distillation for Robust Question Answering

Yulei Niu, Hanwang Zhang

Question answering (QA) models are well-known to exploit data bias, e.g., the language prior in visual QA and the position bias in reading comprehension. Recent debiasing methods a…

cs.CL2020

Counterfactual Variable Control for Robust and Interpretable Question Answering

Sicheng Yu, Yulei Niu, Shuohang Wang +2

Deep neural network based question answering (QA) models are neither robust nor explainable in many cases. For example, a multiple-choice QA model, tested without any input of ques…

cs.CV2020

Counterfactual VQA: A Cause-Effect Look at Language Bias

Yulei Niu, Kaihua Tang, Hanwang Zhang +3

VQA models may tend to rely on language bias as a shortcut and thus fail to sufficiently learn the multi-modal knowledge from both vision and language. Recent debiasing methods pro…

cs.CV2020

Domain-Adaptive Few-Shot Learning

An Zhao, Mingyu Ding, Zhiwu Lu +5

Existing few-shot learning (FSL) methods make the implicit assumption that the few target class samples are from the same domain as the source class samples. However, in practice t…

cs.CV2019

Mobile Video Action Recognition

Yuqi Huo, Xiaoli Xu, Yao Lu +3

Video action recognition, which is topical in computer vision and video analysis, aims to allocate a short video clip to a pre-defined category such as brushing hair or climbing st…