1 citations · 2 across the 7 of their papers we have counts for
4 papers · 1 filter
Do Vision Transformers Need All-to-All Attention? Global Communication Through Elastic Learned Cores
Alan Z. Song, Yinjie Chen, Mu Nan +8
Vision Transformers (ViTs) learn rich visual-semantic representations through all-to-all self-attention among patch tokens. However, this design implicitly assumes that direct pair…
Grounded Reinforcement Learning for Visual Reasoning
Gabriel Sarch, Snigdha Saha, Naitik Khandelwal +4
While reinforcement learning (RL) over chains of thought has significantly advanced language models in tasks such as mathematics and coding, visual reasoning introduces added compl…
VLM Agents Generate Their Own Memories: Distilling Experience into Embodied Programs of Thought
Gabriel Sarch, Lawrence Jang, Michael J. Tarr +3
Large-scale generative language and vision-language models (LLMs and VLMs) excel in few-shot learning but require high-quality demonstrations. We propose In-Context Abstraction Lea…
3D View Prediction Models of the Dorsal Visual Stream
Gabriel Sarch, Hsiao-Yu Fish Tung, Aria Wang +2
Deep neural network representations align well with brain activity in the ventral visual stream. However, the primate visual system has a distinct dorsal processing stream with dif…