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Found 7 papers, 4 papers with code
SimPLR: A Simple and Plain Transformer for Scaling-Efficient Object Detection and Segmentation
The ability to detect objects in images at varying scales has played a pivotal role in the design of modern object detectors.
R-MAE: Regions Meet Masked Autoencoders
In this work, we explore regions as a potential visual analogue of words for self-supervised image representation learning.
BoxeR: Box-Attention for 2D and 3D Transformers
Specifically, we present BoxeR, short for Box Transformer, which attends to a set of boxes by predicting their transformation from a reference window on an input feature map.
MoVie: Revisiting Modulated Convolutions for Visual Counting and Beyond
This paper focuses on visual counting, which aims to predict the number of occurrences given a natural image and a query (e. g. a question or a category).
Ranked #1 on
Object Counting
on HowMany-QA
UR2KiD: Unifying Retrieval, Keypoint Detection, and Keypoint Description without Local Correspondence Supervision
In this paper, we explore how three related tasks, namely keypoint detection, description, and image retrieval can be jointly tackled using a single unified framework, which is trained without the need of training data with point to point correspondences.
Multi-task Learning of Hierarchical Vision-Language Representation
The representation is hierarchical, and prediction for each task is computed from the representation at its corresponding level of the hierarchy.
Improved Fusion of Visual and Language Representations by Dense Symmetric Co-Attention for Visual Question Answering
A key solution to visual question answering (VQA) exists in how to fuse visual and language features extracted from an input image and question.
