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Transformers for Computer Vision Applications

Spatio-Temporal Transformers

Explore how spatio-temporal transformers extend self-attention mechanisms to analyze video data by incorporating temporal dimensions. Learn the Video Transformer Network architecture, its approach to video classification, and its application to action recognition. Gain insights into the role of CNN feature extraction, positional embeddings, and the classification token. Understand other use cases like object tracking and video instance segmentation to apply transformer models effectively in video analysis.

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Let's explore the integration of transformers in tasks involving temporal relations, such as video processing and multiple frames of images. Transformers, initially designed for natural language processing (NLP), naturally extend to model temporal sequences, making them suitable for video analysis applications.

Spatial and temporal relations in video analysis

Building on spatial relations using self-attention mechanisms, transformers now address temporal aspects in video analysis. The dimensions shift from (C,W,H)(C, W, H)(representing height, width, and channels) to (T.H.W.C)(T . H . W . C), introducing a time dimension. This enables modeling both spatial and temporal relations crucial for applications like moving object detection. ...