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Found 9 papers, 6 papers with code
Correlation-Aware Deep Tracking
In contrast to the Siamese-like feature extraction, our network deeply embeds cross-image feature correlation in multiple layers of the feature network.
When Shift Operation Meets Vision Transformer: An Extremely Simple Alternative to Attention Mechanism
It can be even replaced by a zero-parameter operation.
Ranked #67 on
Object Detection
on COCO minival
(APM metric)
Learning Tracking Representations via Dual-Branch Fully Transformer Networks
We present a Siamese-like Dual-branch network based on solely Transformers for tracking.
Sparse MLP for Image Recognition: Is Self-Attention Really Necessary?
Specifically, we replace the MLP module in the token-mixing step with a novel sparse MLP (sMLP) module.
Ranked #391 on
Image Classification
on ImageNet
A Battle of Network Structures: An Empirical Study of CNN, Transformer, and MLP
Convolutional neural networks (CNN) are the dominant deep neural network (DNN) architecture for computer vision.
Self-Supervised Visual Representations Learning by Contrastive Mask Prediction
In this paper, we propose a novel contrastive mask prediction (CMP) task for visual representation learning and design a mask contrast (MaskCo) framework to implement the idea.
Unsupervised Visual Representation Learning by Tracking Patches in Video
The proxy task is to estimate the position and size of the image patch in a sequence of video frames, given only the target bounding box in the first frame.
Tracking by Instance Detection: A Meta-Learning Approach
We propose a principled three-step approach to build a high-performance tracker.
SPM-Tracker: Series-Parallel Matching for Real-Time Visual Object Tracking
The two stages are connected in series as the input proposals of the FM stage are generated by the CM stage.
