227 citations · 341 across the 26 of their papers we have counts for
5 papers · 1 filter
Deconfounded Visual Grounding
Jianqiang Huang, Yu Qin, Jiaxin Qi +2
We focus on the confounding bias between language and location in the visual grounding pipeline, where we find that the bias is the major visual reasoning bottleneck. For example,…
Self-Supervised Learning Disentangled Group Representation as Feature
Tan Wang, Zhongqi Yue, Jianqiang Huang +2
A good visual representation is an inference map from observations (images) to features (vectors) that faithfully reflects the hidden modularized generative factors (semantics). In…
Causal Attention for Unbiased Visual Recognition
Tan Wang, Chang Zhou, Qianru Sun +1
Attention module does not always help deep models learn causal features that are robust in any confounding context, e.g., a foreground object feature is invariant to different back…
Transporting Causal Mechanisms for Unsupervised Domain Adaptation
Zhongqi Yue, Qianru Sun, Xian-Sheng Hua +1
Existing Unsupervised Domain Adaptation (UDA) literature adopts the covariate shift and conditional shift assumptions, which essentially encourage models to learn common features a…
Revisiting Local Descriptor for Improved Few-Shot Classification
Jun He, Richang Hong, Xueliang Liu +2
Few-shot classification studies the problem of quickly adapting a deep learner to understanding novel classes based on few support images. In this context, recent research efforts…