Overcoming Semantic Dilution in Transformer-Based Next Frame Prediction

• URL: http://arxiv.org/abs/2501.16753v1
• Date: Tue, 28 Jan 2025 07:12:29 GMT
• Title: Overcoming Semantic Dilution in Transformer-Based Next Frame Prediction
• Authors: Hy Nguyen, Srikanth Thudumu, Hung Du, Rajesh Vasa, Kon Mouzakis,
• Abstract summary: Next-frame prediction in videos is crucial for applications such as autonomous driving, object tracking, and motion prediction. transformer-based next-frame prediction models face notable issues. We propose a Semantic Concentration Multi-Head Self-Attention architecture, which effectively mitigates semantic dilution.
• Score: 0.9776703963093367
• License:
• Abstract: Next-frame prediction in videos is crucial for applications such as autonomous driving, object tracking, and motion prediction. The primary challenge in next-frame prediction lies in effectively capturing and processing both spatial and temporal information from previous video sequences. The transformer architecture, known for its prowess in handling sequence data, has made remarkable progress in this domain. However, transformer-based next-frame prediction models face notable issues: (a) The multi-head self-attention (MHSA) mechanism requires the input embedding to be split into $N$ chunks, where $N$ is the number of heads. Each segment captures only a fraction of the original embeddings information, which distorts the representation of the embedding in the latent space, resulting in a semantic dilution problem; (b) These models predict the embeddings of the next frames rather than the frames themselves, but the loss function based on the errors of the reconstructed frames, not the predicted embeddings -- this creates a discrepancy between the training objective and the model output. We propose a Semantic Concentration Multi-Head Self-Attention (SCMHSA) architecture, which effectively mitigates semantic dilution in transformer-based next-frame prediction. Additionally, we introduce a loss function that optimizes SCMHSA in the latent space, aligning the training objective more closely with the model output. Our method demonstrates superior performance compared to the original transformer-based predictors.

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