Accurate traffic forecasting requires both understanding scene dynamics and synthesizing realistic future observations. Recent diffusion-based video generation models produce visually plausible predictions but require expensive end-to-end training and often entangle scene understanding with image synthesis. In this wor...
T. Nguyen, Thanh-Nhan Vo, Bui Hoai Thuong Nguyen et al.· 1 citation
A decoupled semantic understanding framework that first resolves predefined traffic questions into structured semantic facts and subsequently uses these facts to guide caption generation, leading to more accurate and reliable traffic understanding, leading to higher-quality lan guage generation.
Bui Hoai Thuong Nguyen, Thanh-Nhan Vo, T. Nguyen et al.· 1 citation
This work proposes a unified three-stage coarse-to-fine framework that combines action-aligned retrieval with pairwise multimodal reranking to bridge the pose-semantic gap, and proposes an efficient off-the-shelf reranking module that leverages a pivot-promote algorithm to perform direct pairwise visual comparisons.
Thanh-Khoi Nguyen, Thanh-Nhan Vo, Trong-Thuan Nguyen et al.· 0 citations
Each task and its evaluation protocol is described, the challenge leaderboards are presented, and the leading submissions are summarized, with the aim of documenting the current state of each task as measured on held-out challenge data.
A. Cioppa, Silvio Giancola, Haakan Ardo et al.· 1 citation
SAGA integrates an acceleration domain spectral guidance objective based on finite-window Slepian projections with a structured autoregressive noise initialization strategy that suppresses short-range temporal correlations while preserving long-range motion structure and consistently improves temporal quality across mu...
Thanh-Nhan Vo, Trong-Thuan Nguyen, T. Le et al.· 0 citations
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