361 citations · 547 across the 7 of their papers we have counts for
7 papers
Learning Object-Language Alignments for Open-Vocabulary Object Detection
Chuang Lin, Peize Sun, Yi Jiang +5
Existing object detection methods are bounded in a fixed-set vocabulary by costly labeled data. When dealing with novel categories, the model has to be retrained with more bounding…
Self-supervised Video Representation Learning with Motion-Aware Masked Autoencoders
Haosen Yang, Deng Huang, Bin Wen +5
Masked autoencoders (MAEs) have emerged recently as art self-supervised spatiotemporal representation learners. Inheriting from the image counterparts, however, existing video MAEs…
Rethinking Resolution in the Context of Efficient Video Recognition
Chuofan Ma, Qiushan Guo, Yi Jiang +3
In this paper, we empirically study how to make the most of low-resolution frames for efficient video recognition. Existing methods mainly focus on developing compact networks or a…
MetaFormer: A Unified Meta Framework for Fine-Grained Recognition
Qishuai Diao, Yi Jiang, Bin Wen +2
Fine-Grained Visual Classification(FGVC) is the task that requires recognizing the objects belonging to multiple subordinate categories of a super-category. Recent state-of-the-art…
TransTrack: Multiple Object Tracking with Transformer
Peize Sun, Jinkun Cao, Yi Jiang +5
In this work, we propose TransTrack, a simple but efficient scheme to solve the multiple object tracking problems. TransTrack leverages the transformer architecture, which is an at…
What Makes for End-to-End Object Detection?
Peize Sun, Yi Jiang, Enze Xie +4
Object detection has recently achieved a breakthrough for removing the last one non-differentiable component in the pipeline, Non-Maximum Suppression (NMS), and building up an end-…
Sparse R-CNN: End-to-End Object Detection with Learnable Proposals
Peize Sun, Rufeng Zhang, Yi Jiang +8
We present Sparse R-CNN, a purely sparse method for object detection in images. Existing works on object detection heavily rely on dense object candidates, such as anchor boxes…