SMART: MLLM-guided Temporal Alignment for Unifying Sign Language Recognition and Spotting
Continuous sign language recognition (CSLR) aims to recognize gloss sequences from unsegmented sign videos under weak sequence-level supervision. However, existing methods rely on sentence-level gloss annotations, providing limited temporal and semantic guidance for fine-grained representation learning. Conventional video-text alignment also requires large batch sizes, making it inefficient for memory-intensive sign language video training. In this work, we propose SMART, an MLLM-guided temporal alignment framework for joint sign recognition and spotting. SMART uses MLLMgenerated motion descriptions as auxiliary semantic cues and performs stable videotext alignment under small-batch train...
| Authors | Eunjee Choi, JungHoon Sung, Seongwhan Cho et al. |
|---|---|
| Primary category | cs.CV |
| Published | 26 August 2026 |
| Last updated at source | 31 August 2026 |
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