Paper Info
Title
Class-Agnostic Object Detection with Multi-modal Transformer.
Abstract
What constitutes an object? This has been a long-standing question in computer vision. Towards this goal, numerous learning-free and learning-based approaches have been developed to score objectness. However, they generally do not scale well across new domains and novel objects. In this paper, we advocate that existing methods lack a top-down supervision signal governed by human-understandable semantics. For the first time in literature, we demonstrate that Multi-modal Vision Transformers (MViT) trained with aligned image-text pairs can effectively bridge this gap. Our extensive experiments across various domains and novel objects show the state-of-the-art performance of MViTs to localize generic objects in images. Based on the observation that existing MViTs do not include multi-scale feature processing and usually require longer training schedules, we develop an efficient MViT architecture using multi-scale deformable attention and late vision-language fusion. We show the significance of MViT proposals in a diverse range of applications including open-world object detection, salient and camouflage object detection, supervised and self-supervised detection tasks. Further, MViTs can adaptively generate proposals given a specific language query and thus offer enhanced interactability. Code: https://git.io/J1HPY.
Year
DOI
Venue
2022
10.1007/978-3-031-20080-9_30
European Conference on Computer Vision
Keywords
DocType
Citations 
Object detection,Class-agnostic,Vision transformers
Conference
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Muhammad Maaz100.34
Hanoona Abdul Rasheed200.34
Salman Khan338741.05
Fahad Shahbaz Khan4162269.24
Muhammad Anwer Rao512911.22
Yang Ming-Hsuan615303620.69