Language-Augmented Video Action Anticipation: Design Fundamentals, Benchmarks, and Open Challenges
Action anticipation predicts future human actions from partial video under incomplete context and temporal uncertainty. Recent systems introduce large language models (LLMs), vision-language models (VLMs), or language-derived semantics at different stages, but reported gains are difficult to interpret when task formulation, visual pretraining, supervision, decoder design, and evaluation code change simultaneously. The central contribution of this review is an evidence-aware design map that crosses task regime with the point at which language-derived information intervenes. We characterise task regimes along six axes. These axes organise the literature into five broad task families: single...
| Authors | Mahsa Mohammadi, Zeyu Fu, Sareh Rowlands |
|---|---|
| Primary category | cs.CV |
| Published | 15 September 2026 |
| Last updated at source | 30 September 2026 |
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