Hila commited on
Commit
c3921ec
·
1 Parent(s): d16dc7c

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +1 -1
README.md CHANGED
@@ -13,7 +13,7 @@ pinned: false
13
 
14
  *Website: [all-things-vits.github.io/atv/](https://all-things-vits.github.io/atv/)*
15
 
16
- *Abstract: The attention mechanism has revolutionized deep learning research across many disciplines, starting from NLP and expanding to vision, speech, and more. Different from other mechanisms, the elegant and general attention mechanism is easily adaptable and eliminates modality-specific inductive biases. As attention become increasingly popular, it is crucial to develop tools that allow researchers to understand and explain the inner workings of the mechanism to facilitate better and more responsible use of it. This tutorial focuses on understanding and interpreting attention in the vision, and in the multi-modal settings combining text and vision. We present state-of-the-art research on representation probing, interpretability, and attention-based semantic guidance, alongside hands-on demos to facilitate interactivity. Additionally, we discuss open questions arising from recent works and future research directions.*
17
 
18
  <p align="center">
19
  <img src="https://i.imgur.com/BcqCbcC.jpg" width=750/>
 
13
 
14
  *Website: [all-things-vits.github.io/atv/](https://all-things-vits.github.io/atv/)*
15
 
16
+ *Abstract: The attention mechanism has revolutionized deep learning research across many disciplines starting from NLP and expanding to vision, speech, and more. Different from other mechanisms, the elegant and general attention mechanism is easily adaptable and eliminates modality-specific inductive biases. As attention becomes increasingly popular, it is crucial to develop tools to allow researchers to understand and explain the inner workings of the mechanism to facilitate better and more responsible use of it. This tutorial focuses on understanding and interpreting attention in the vision and the multi-modal setting. We present state-of-the-art research on representation probing, interpretability, and attention-based semantic guidance, alongside hands-on demos to facilitate interactivity. Additionally, we discuss open questions arising from recent works and future research directions.*
17
 
18
  <p align="center">
19
  <img src="https://i.imgur.com/BcqCbcC.jpg" width=750/>