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. background CLIP and ALIGN use contrastive learning to train text and image encoders that align the two modalities. goal of the authors ...

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 ...