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arXiv · 2511.17929

MambaTAD: When State-Space Models Meet Long-Range Temporal Action Detection

Hui Lu·Yi Yu·Shijian Lu·Deepu Rajan·Boon Poh Ng·Alex C. Kot·Xudong Jiang
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Abstract

Temporal Action Detection (TAD) aims to identify and localize actions by determining their starting and ending frames within untrimmed videos. Recent Structured State-Space Models such as Mamba have demonstrated potential in TAD due to their long-range modeling capability and linear computational complexity. On the other hand, structured state-space models often face two key challenges in TAD, namely, decay of temporal context due to recursive processing and self-element conflict during global visual context modeling, which become more severe while handling long-span action instances. Additionally, traditional methods for TAD struggle with detecting long-span action instances due to a lack of global awareness and inefficient detection heads. This paper presents MambaTAD, a new state-space TAD model that introduces long-range modeling and global feature detection capabilities for accurate temporal action detection. MambaTAD comprises two novel designs that complement each other with superior TAD performance. First, it introduces a Diagonal-Masked Bidirectional State-Space (DMBSS) module which effectively facilitates global feature fusion and temporal action detection. Second, it introduces a global feature fusion head that refines the detection progressively with multi-granularity features and global awareness. In addition, MambaTAD tackles TAD in an end-to-end one-stage manner using a new state-space temporal adapter(SSTA) which reduces network parameters and computation cost with linear complexity. Extensive experiments show that MambaTAD achieves superior TAD performance consistently across multiple public benchmarks.

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Hui Lu, Yi Yu, Shijian Lu, Deepu Rajan, Boon Poh Ng, Alex C. Kot, Xudong Jiang. 2025-11-22. MambaTAD: When State-Space Models Meet Long-Range Temporal Action Detection. https://arxiv.org/abs/2511.17929

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