Interpretable Visual Reasoning via Probabilistic Formulation Under Natural Supervision

By Xinzhe Han, Shuhui Wang, Chi Su, Weigang Zhang, Qingming Huang, Qi Tian. 2020

11 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Chuang Gan
2. Ping Luo
3. Luc Van Gool
4. Josh Tenenbaum
5. Xiaogang Wang
6. Dacheng Tao
7. Andrea Vedaldi
8. Jitendra Malik

Citations in the corpus

• Class-Incremental Instance Segmentation via Multi-Teacher Networks (2021, neutral): “Thanks to the development of deep learning, like other visual tasks (Wei et al. 2019; Yang et al. 2019; Han et al. 2020; Feng et al. 2020), current instance segmentation methods (He et al. 2017; Bolya et al. 2019; Chen et al. 2020) based on convolutional neural networks (CNNs) have achieved…”
• Dynamic Visual Reasoning by Learning Differentiable Physics Models from Video and Language (2021, background): “Existing works can be generally categorized into two streams as end-to-end approaches [38, 81, 72, 39, 5] and neurosymbolic approaches [78, 27, 28, 22, 4, 57, 43, 56, 34, 3].”