Visual commonsense representation learning via causal inference
We present a novel unsupervised feature representation learning method, Visual Commonsense Region-based Convolutional Neural Network (VC R-CNN), to serve as an improved visual region encoder for high-level tasks such as captioning and VQA. Given a set of detected object regions in an image (e.g., us...
Saved in:
Main Authors: | WANG, Tan, HUANG, Jianqiang, ZHANG, Hanwang, SUN, Qianru |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2020
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/5598 https://ink.library.smu.edu.sg/context/sis_research/article/6601/viewcontent/Wang_Visual_Commonsense_Representation_Learning_via_Causal_Inference_CVPRW_2020_paper.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
Similar Items
-
Visual Commonsense R-CNN
by: WANG, Tan, et al.
Published: (2020) -
Self-supervised learning disentangled group representation as feature
by: WANG, Tan, et al.
Published: (2021) -
Causal attention for unbiased visual recognition
by: WANG, Tan, et al.
Published: (2021) -
Transporting causal mechanisms for unsupervised domain adaptation
by: YUE, Zhongqi, et al.
Published: (2021) -
Exploring diffusion time-steps for unsupervised representation learning
by: YUE, Zhongqi, et al.
Published: (2024)