# MambaTAD: When State-Space Models Meet Long-Range Temporal Action Detection arXiv: https://arxiv.org/abs/2511.17929 ยท HTML: https://mlsanity.com/paper/2511.17929/ ## Key facts - Cited by 4 papers in the map (data through 14 Oct 2026). - Placed in Video, motion and generative Mamba models, one of 10 directions in State-space sequence models (S4, Mamba). - Builds on 72 of its 101 references that are in the map. - First posted on arXiv 22 Nov 2025. ## Placed in - State-space sequence models (S4, Mamba) / [Video, motion and generative Mamba models](https://mlsanity.com/ssm/video-motion-and-generative-mamba-models/) ## Builds on (most cited first, 30 of 72) - [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016) - [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023) - [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019) - [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016) - [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017) - [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021) - [GLU Variants Improve Transformer](https://mlsanity.com/paper/2002.05202/) (2020) - [Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality](https://mlsanity.com/paper/2405.21060/) (2024) - [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://mlsanity.com/paper/2203.12602/) (2022) - [Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model](https://mlsanity.com/paper/2401.09417/) (2024) - [Path Aggregation Network for Instance Segmentation](https://mlsanity.com/paper/1803.01534/) (2018) - [Simplified State Space Layers for Sequence Modeling](https://mlsanity.com/paper/2208.04933/) (2022) - [HiPPO: Recurrent Memory with Optimal Polynomial Projections](https://mlsanity.com/paper/2008.07669/) (2020) - [Understanding the Effective Receptive Field in Deep Convolutional Neural Networks](https://mlsanity.com/paper/1701.04128/) (2017) - [VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking](https://mlsanity.com/paper/2303.16727/) (2023) - [Hungry Hungry Hippos: Towards Language Modeling with State Space Models](https://mlsanity.com/paper/2212.14052/) (2022) - [U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation](https://mlsanity.com/paper/2401.04722/) (2024) - [MambaIR: A Simple Baseline for Image Restoration with State-Space Model](https://mlsanity.com/paper/2402.15648/) (2024) - [BMN: Boundary-Matching Network for Temporal Action Proposal Generation](https://mlsanity.com/paper/1907.09702/) (2019) - [VideoMamba: State Space Model for Efficient Video Understanding](https://mlsanity.com/paper/2403.06977/) (2024) - [ActionFormer: Localizing Moments of Actions with Transformers](https://mlsanity.com/paper/2202.07925/) (2022) - [BSN: Boundary Sensitive Network for Temporal Action Proposal Generation](https://mlsanity.com/paper/1806.02964/) (2018) - [Long Range Language Modeling via Gated State Spaces](https://mlsanity.com/paper/2206.13947/) (2022) - [Graph Convolutional Networks for Temporal Action Localization](https://mlsanity.com/paper/1909.03252/) (2019) - [Learning Salient Boundary Feature for Anchor-free Temporal Action Localization](https://mlsanity.com/paper/2103.13137/) (2021) - [End-to-end Temporal Action Detection with Transformer](https://mlsanity.com/paper/2106.10271/) (2021) - [MambaOut: Do We Really Need Mamba for Vision?](https://mlsanity.com/paper/2405.07992/) (2024) - [TriDet: Temporal Action Detection with Relative Boundary Modeling](https://mlsanity.com/paper/2303.07347/) (2023) - [Gaussian Temporal Awareness Networks for Action Localization](https://mlsanity.com/paper/1909.03877/) (2019) - [Single Shot Temporal Action Detection](https://mlsanity.com/paper/1710.06236/) (2017) ## Built on by (most cited first, 4 of 4) - [ApET: Approximation-Error Guided Token Compression for Efficient VLMs](https://mlsanity.com/paper/2602.19870/) (2026) - [LiquidTAD: Efficient Temporal Action Detection via Parallel Liquid-Inspired Temporal Relaxation](https://mlsanity.com/paper/2604.18274/) (2026) - [Efficient Spatial-Temporal Focal Adapter with SSM for Temporal Action Detection](https://mlsanity.com/paper/2604.09164/) (2026) - [UniSkip-Mamba: A Frequency-Aware State Space Model for Audio-Visual Temporal Forgery Localization](https://mlsanity.com/paper/2607.04498/) (2026) ## Abstract (arXiv) 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.