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Found 10 papers, 5 papers with code
Correlation-Embedded Transformer Tracking: A Single-Branch Framework
Thus, we reformulate the two-branch Siamese tracking as a conceptually simple, fully transformer-based Single-Branch Tracking pipeline, dubbed SBT.
Towards Category Unification of 3D Single Object Tracking on Point Clouds
Based on this observation, we design a novel point set representation learning network inheriting transformer architecture, termed AdaFormer, which adaptively encodes the dynamically varying shape and size information from cross-category data in a unified manner.
VideoTrack: Learning To Track Objects via Video Transformer
Existing Siamese tracking methods, which are built on pair-wise matching between two single frames, heavily rely on additional sophisticated mechanism to exploit temporal information among successive video frames, hindering them from high efficiency and industrial deployments.
VideoPipe 2022 Challenge: Real-World Video Understanding for Urban Pipe Inspection
For this reason, we propose to advance research areas of video understanding, with a shift from traditional action recognition to industrial anomaly analysis.
Collaborative Attention Memory Network for Video Object Segmentation
In order to overcome the shortcomings , we propose Collaborative Attention Memory Network with an enhanced segmentation head.
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.
Learning Tracking Representations via Dual-Branch Fully Transformer Networks
We present a Siamese-like Dual-branch network based on solely Transformers for tracking.
Symbolic Techniques for Deep Learning: Challenges and Opportunities
The goal of this survey is to study how symbolic techniques are utilized in deep learning.
Learning Spatio-Appearance Memory Network for High-Performance Visual Tracking
Existing visual object tracking usually learns a bounding-box based template to match the targets across frames, which cannot accurately learn a pixel-wise representation, thereby being limited in handling severe appearance variations.
Deep Stock Representation Learning: From Candlestick Charts to Investment Decisions
We propose a novel investment decision strategy (IDS) based on deep learning.
Computational Finance
