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Trackformer — Multi-Object Tracking with Transformers
A lower-level explanation of the paper Multi-Object Tracking with Transformers (Meinhardt et al., 2022) including DETR (Carion et al., 2020) and Deformable DETR (Zhu et al., 2020). 🎥
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8. Transformer
⏮️ I recommend reading the RNN post first for the encoder-decoder architecture.
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7. Recurrent Neural Networks
🧱 The encoder-decoder RNN is quite useful for understanding the transformer.
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6. Convolutional Neural Networks
🔑️ Key sections about parameter sharing, inductive biases, skip connections, and cross-correlation.
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5. Feedforward Neural Networks
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4. Backpropagation
💡 Includes a nice detail about why we don't 'frontpropagate'. ;-)
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3. Optimization
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2. Supervised Learning