Computer Vision

[TOC] Title: Segment Anything Author: Alexander Kirillov et. al. Publish Year: 5 Apr 2023 Review Date: Sun, May 21, 2023 url: https://arxiv.org/pdf/2304.02643.pdf Summary of paper Motivation we introduce the segment anything project: a new task, model and dataset for image segmentation. Using the model in a data collection loop, we built the largest segmentation dataset to date. Contribution the model is designed and trained to be promptable, so it can transfer zero-shot to new images distributions and tasks....

Title: Attention Over Learned Object Embeddings Enables Complex Visual Reasoning Author: David Ding et. al. Publish Year: 2021 NeurIPS Review Date: Dec 2021 Background info for this paper: Their paper propose a all-in-one transformer model that is able to answer CLEVRER counterfactual questions with higher accuracy (75.6% vs 46.5%) and less training data (- 40%) They believe that their model relies on three key aspects: self-attention soft-discretization self-supervised learning What is self-attention...