2 citations · 3 across the 6 of their papers we have counts for
6 papers
Artifacts and Attention Sinks: Structured Approximations for Efficient Vision Transformers
Andrew Lu, Wentinn Liao, Liuhui Wang +2
Vision transformers have emerged as a powerful tool across a wide range of applications, yet their inner workings remain only partially understood. In this work, we examine the phe…
AlignedCut: Visual Concepts Discovery on Brain-Guided Universal Feature Space
Huzheng Yang, James Gee, Jianbo Shi
We study the intriguing connection between visual data, deep networks, and the brain. Our method creates a universal channel alignment by using brain voxel fMRI response prediction…
Brain Decodes Deep Nets
Huzheng Yang, James Gee, Jianbo Shi
We developed a tool for visualizing and analyzing large pre-trained vision models by mapping them onto the brain, thus exposing their hidden inside. Our innovation arises from a su…
Memory Encoding Model
Huzheng Yang, James Gee, Jianbo Shi
We explore a new class of brain encoding model by adding memory-related information as input. Memory is an essential brain mechanism that works alongside visual stimuli. During a v…
Retinotopy Inspired Brain Encoding Model and the All-for-One Training Recipe
Huzheng Yang, Jianbo Shi, James Gee
Brain encoding models aim to predict brain voxel-wise responses to stimuli images, replicating brain signals captured by neuroimaging techniques. There is a large volume of publicl…
Beyond mAP: Towards better evaluation of instance segmentation
Rohit Jena, Lukas Zhornyak, Nehal Doiphode +4
Correctness of instance segmentation constitutes counting the number of objects, correctly localizing all predictions and classifying each localized prediction. Average Precision i…