What is the impact of domain shift in image complexity (e.g., from synthetic to natural scenes) on the accurac

Abstract

Abstract: In Transformer architectures, tokenstextemdash discrete units derived from raw datatextemdash are formed by segmenting inputs into fixed-length chunks. Each token is then mapped to an embedding, enabling parallel attention computations while preserving the input's essential information. Due to the quadratic computational complexity of transformer self-attention mechanisms, token reduction has primarily been used as an efficiency strategy. This is especially true in single vision and language domains, where it helps balance computational costs, memory usage, and inference latency. Despite the

Research Question

What is the impact of domain shift in image complexity (e.g., from synthetic to natural scenes) on the accuracy-latency trade-off of CAT-based token reduction versus fixed patch tokenization in multimodal video generation models, evaluated using FID and CLIP scores?

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