arxiv: 2604.09164 · v1 · submitted 2026-04-10 · 💻 cs.CV

Recognition: unknown

Efficient Spatial-Temporal Focal Adapter with SSM for Temporal Action Detection

Yicheng Qiu , Keiji Yanai

Authors on Pith no claims yet

Pith reviewed 2026-05-10 17:26 UTC · model grok-4.3

classification 💻 cs.CV

keywords temporal action detectionstate space modelsSSMaction localizationvideo understandingspatial-temporal adapterefficient modeling

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The pith

A focal adapter embedding boundary-aware state space modeling improves action localization in long untrimmed videos.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper seeks to overcome feature redundancy and weak global dependency capture that limit CNN and Transformer models on lengthy video sequences for temporal human action detection. It turns to State Space Models for their linear scaling and strong long-range temporal reasoning, then builds a new framework around an Efficient Spatial-Temporal Focal Adapter. This adapter is inserted into pre-trained layers and combines a Temporal Boundary-aware SSM for temporal features with efficient spatial processing. A sympathetic reader would care because success would make accurate detection of actions in real-world, hours-long videos computationally practical rather than prohibitive.

Core claim

The research constructs a novel framework for video human action detection by introducing the Efficient Spatial-Temporal Focal (ESTF) Adapter into the pre-trained layers. This module integrates the advantages of the proposed Temporal Boundary-aware SSM (TB-SSM) for temporal feature modeling with efficient processing of spatial features. Comprehensive experiments across multiple benchmarks show that this improved strategy enhances both localization performance and robustness compared to previous SSM-based and other structural methods.

What carries the argument

The Efficient Spatial-Temporal Focal (ESTF) Adapter, which incorporates a Temporal Boundary-aware State Space Model (TB-SSM) to model temporal features with linear complexity while handling spatial features efficiently inside pre-trained layers.

Load-bearing premise

That inserting the TB-SSM and ESTF Adapter into pre-trained layers will deliver consistent localization and robustness gains across varied real-world video distributions without needing heavy hyperparameter tuning or encountering domain shift problems.

What would settle it

Experiments on a new, diverse collection of long untrimmed videos that show no improvement or a drop in mean average precision for action localization relative to strong Transformer baselines would disprove the central effectiveness claim.

Figures

Figures reproduced from arXiv: 2604.09164 by Keiji Yanai, Yicheng Qiu.

**Figure 1.** Figure 1: The architecture of the proposed TAD framework. We integrate ESTF Adapters into the frozen pre-trained backbone layers to adapt representations for temporal action [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗

**Figure 2.** Figure 2: Qualitative results of our proposed method and previous method on THUMOS14 [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

read the original abstract

Temporal human action detection aims to identify and localize action segments within untrimmed videos, serving as a pivotal task in video understanding. Despite the progress achieved by prior architectures like CNN and Transformer models, these continue to struggle with feature redundancy and degraded global dependency modeling capabilities when applied to long video sequences. These limitations severely constrain their scalability in real-world video analysis. State Space Models (SSMs) offer a promising alternative with linear long-term modeling and robust global temporal reasoning capabilities. Rethinking the application of SSMs in temporal modeling, this research constructs a novel framework for video human action detection. Specifically, we introduce the Efficient Spatial-Temporal Focal (ESTF) Adapter into the pre-trained layers. This module integrates the advantages of our proposed Temporal Boundary-aware SSM(TB-SSM) for temporal feature modeling with efficient processing of spatial features. We perform comprehensive and quantitative analyses across multiple benchmarks, comparing our proposed method against previous SSM-based and other structural methods. Extensive experiments demonstrate that our improved strategy significantly enhances both localization performance and robustness, validating the effectiveness of our proposed method.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper proposes an SSM-based adapter for temporal action detection but the abstract provides no concrete evidence for its claimed improvements.

read the letter

Your colleague should know that this paper presents the ESTF Adapter and TB-SSM as a way to handle long untrimmed videos for action detection more efficiently than standard approaches. The idea is to insert these modules into pre-trained layers to model temporal boundaries and spatial features with state space models. The work does a solid job identifying the problems with CNNs and Transformers in long sequences, like feature redundancy and poor global dependency modeling. Turning to SSMs for linear complexity is a reasonable move, and the boundary-aware variant plus focal adapter sounds like a targeted fix for action localization tasks. That said, the abstract is heavy on assertions and light on substance. It says the method significantly enhances localization and robustness, but there are no numbers, no description of the actual implementation, no ablation studies, and no discussion of experimental controls. Without those, it's impossible to tell if the gains are meaningful or if they come from careful tuning on specific benchmarks. The stress test note about potential failure under domain shift or without per-dataset tuning seems worth checking in the full paper. Many adapter methods show sensitivity there, and nothing in the abstract suggests they tested cross-dataset transfer. This paper is mainly for researchers focused on efficient temporal modeling in computer vision, particularly those already exploring SSMs for video. A reader interested in practical improvements to action detection pipelines might get some value if the experiments are thorough. I would recommend sending it for peer review because the underlying idea engages honestly with current limitations in the field, even if the current writeup needs more detail to stand on its own.

Referee Report

2 major / 1 minor

Summary. The paper proposes a framework for temporal action detection in untrimmed videos that addresses limitations of CNN and Transformer models in handling long sequences by introducing an Efficient Spatial-Temporal Focal (ESTF) Adapter. This adapter integrates a novel Temporal Boundary-aware SSM (TB-SSM) for temporal feature modeling with efficient spatial processing, inserted into pre-trained layers. The authors perform quantitative comparisons against prior SSM-based and structural methods on multiple benchmarks and claim that the approach significantly improves localization performance and robustness.

Significance. If the empirical gains are robustly demonstrated, the work could advance efficient long-range temporal modeling in video understanding by exploiting SSMs' linear complexity as an alternative to Transformers, with the boundary-aware adaptation potentially aiding precise action localization. However, the current lack of detailed experimental validation limits immediate impact.

major comments (2)

[Abstract] Abstract: The headline claim that 'extensive experiments demonstrate that our improved strategy significantly enhances both localization performance and robustness' is unsupported by any reported metrics, baselines, error bars, statistical tests, or ablation results in the provided text, which is load-bearing for the central empirical assertion.
[Experiments] Experiments section: No details are given on benchmark-specific results (e.g., mAP on THUMOS14 or ActivityNet), hyperparameter sensitivity, cross-dataset transfer, or controls for domain shift, despite the skeptic concern that adapter modules often require per-dataset tuning; this undermines the robustness and generalizability claims.

minor comments (1)

[Method] The notation and integration details for TB-SSM and ESTF Adapter would benefit from explicit equations or pseudocode to clarify how they are inserted into pre-trained layers.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We acknowledge that the current manuscript version would benefit from more explicit quantitative details to support the claims. We will revise accordingly.

read point-by-point responses

Referee: [Abstract] Abstract: The headline claim that 'extensive experiments demonstrate that our improved strategy significantly enhances both localization performance and robustness' is unsupported by any reported metrics, baselines, error bars, statistical tests, or ablation results in the provided text, which is load-bearing for the central empirical assertion.

Authors: The abstract is intended as a concise summary of findings detailed in the Experiments section. To directly address this point, we will revise the abstract to incorporate specific quantitative metrics, such as mAP improvements on the benchmarks, along with brief baseline comparisons. This will make the claim evidence-based while preserving its summary nature. revision: yes
Referee: [Experiments] Experiments section: No details are given on benchmark-specific results (e.g., mAP on THUMOS14 or ActivityNet), hyperparameter sensitivity, cross-dataset transfer, or controls for domain shift, despite the skeptic concern that adapter modules often require per-dataset tuning; this undermines the robustness and generalizability claims.

Authors: We agree that expanded reporting is needed for full transparency. In the revised manuscript, we will add detailed benchmark-specific results including mAP on THUMOS14 and ActivityNet, hyperparameter sensitivity analyses, cross-dataset transfer experiments, and controls for domain shift. These additions will directly substantiate the robustness and generalizability claims and address potential concerns about per-dataset tuning. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical claims rest on benchmark comparisons, not self-referential definitions or fitted inputs.

full rationale

The paper introduces TB-SSM and ESTF Adapter modules for temporal action detection and validates them via experiments on standard benchmarks. No derivation chain, equations, or parameter-fitting steps are described that reduce predictions to the inputs by construction. Claims of enhanced localization and robustness are presented as outcomes of quantitative comparisons against prior methods, not as logical necessities derived from the method's own definitions. Self-citations (if any in the full text) do not bear the load of the central empirical assertions, which remain falsifiable through external benchmarks. This is a standard empirical contribution with no detectable circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 2 invented entities

The central claim depends on the unproven assumption that the new adapter will generalize beyond the tested benchmarks and that SSMs retain their linear scaling advantages when inserted as adapters into pre-trained vision models.

axioms (1)

domain assumption State Space Models provide linear-complexity long-range temporal modeling superior to Transformers for long sequences
Invoked in the abstract to justify replacing or augmenting prior architectures.

invented entities (2)

Efficient Spatial-Temporal Focal (ESTF) Adapter no independent evidence
purpose: Integrate temporal boundary-aware SSM processing with efficient spatial feature handling inside pre-trained layers
New module introduced by the authors; no independent evidence provided beyond the paper's own experiments.
Temporal Boundary-aware SSM (TB-SSM) no independent evidence
purpose: Model temporal features while explicitly attending to action boundaries
New variant of SSM proposed in this work; no external validation or theoretical proof supplied.

pith-pipeline@v0.9.0 · 5481 in / 1190 out tokens · 47809 ms · 2026-05-10T17:26:30.152450+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

43 extracted references · 14 canonical work pages · 4 internal anchors

[1]

Video process detection for space electrostatic suspension material experiment in china’s space station,

J. Yang, K. Liu, M. Zhao, and S. Li, “Video process detection for space electrostatic suspension material experiment in china’s space station,” Engineering Applications of Artificial Intelligence, vol. 131, p. 107804, 2024

2024
[2]

Low-power continuous remote behavioral localization with event cameras,

F. Hamann, S. Ghosh, I. J. Martinez, T. Hart, A. Kacelnik, and G. Gal- lego, “Low-power continuous remote behavioral localization with event cameras,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 18 612–18 621

2024
[3]

Uniav: Unified audio-visual perception for multi-task video localization,

T. Geng, T. Wang, Y . Zhang, J. Duan, W. Guan, and F. Zheng, “Uniav: Unified audio-visual perception for multi-task video localization,”arXiv preprint arXiv:2404.03179, 2024

work page arXiv 2024
[4]

Fire anomaly detection based on low-rank adaption fine-tuning and localization using gradient filtering,

Y . Qiu, F. Sha, L. Niu, and G. Zhang, “Fire anomaly detection based on low-rank adaption fine-tuning and localization using gradient filtering,” Applied Soft Computing, p. 112782, 2025

2025
[5]

Astra: An action spotting transformer for soccer videos,

A. Xarles, S. Escalera, T. B. Moeslund, and A. Clap ´es, “Astra: An action spotting transformer for soccer videos,” inProceedings of the 6th International Workshop on Multimedia Content Analysis in Sports, 2023, pp. 93–102

2023
[6]

Multipath 3d-conv encoder and temporal- sequence decision for repetitive-action counting,

Y . Qiu, L. Niu, and F. Sha, “Multipath 3d-conv encoder and temporal- sequence decision for repetitive-action counting,”Expert Systems with Applications, vol. 249, p. 123760, 2024

2024
[7]

Efficient temporal attention with state space model for temporal action localization,

Y . Qiu, F. Sha, and L. Niu, “Efficient temporal attention with state space model for temporal action localization,” inInternational Conference on Neural Information Processing. Springer, 2024, pp. 183–197

2024
[8]

Videgothink: Assessing egocentric video understanding capabilities for embodied ai,

S. Cheng, K. Fang, Y . Yu, S. Zhou, B. Li, Y . Tian, T. Li, L. Han, and Y . Liu, “Videgothink: Assessing egocentric video understanding capabilities for embodied ai,”arXiv preprint arXiv:2410.11623, 2024

work page arXiv 2024
[9]

Urbanvideo-bench: Benchmarking vision-language models on embodied intelligence with video data in urban spaces,

B. Zhao, J. Fang, Z. Dai, Z. Wang, J. Zha, W. Zhang, C. Gao, Y . Wang, J. Cui, X. Chenet al., “Urbanvideo-bench: Benchmarking vision-language models on embodied intelligence with video data in urban spaces,”arXiv preprint arXiv:2503.06157, 2025

work page arXiv 2025
[10]

Alanavlm: A multimodal embodied ai foundation model for egocentric video understanding,

A. Suglia, C. Greco, K. Baker, J. L. Part, I. Papaioannou, A. Eshghi, I. Konstas, and O. Lemon, “Alanavlm: A multimodal embodied ai foundation model for egocentric video understanding,”arXiv preprint arXiv:2406.13807, 2024

work page arXiv 2024
[11]

Tridet: Temporal action detection with relative boundary modeling,

D. Shi, Y . Zhong, Q. Cao, L. Ma, J. Li, and D. Tao, “Tridet: Temporal action detection with relative boundary modeling,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 18 857–18 866

2023
[12]

G-tad: Sub- graph localization for temporal action detection,

M. Xu, C. Zhao, D. S. Rojas, A. Thabet, and B. Ghanem, “G-tad: Sub- graph localization for temporal action detection,” inProceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 10 156–10 165

2020
[13]

Actionformer: Localizing moments of actions with transformers,

C.-L. Zhang, J. Wu, and Y . Li, “Actionformer: Localizing moments of actions with transformers,” inEuropean Conference on Computer Vision. Springer, 2022, pp. 492–510

2022
[14]

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

A. Gu and T. Dao, “Mamba: Linear-time sequence modeling with selective state spaces,”arXiv preprint arXiv:2312.00752, 2023

work page internal anchor Pith review Pith/arXiv arXiv 2023
[15]

Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

T. Dao and A. Gu, “Transformers are ssms: Generalized models and ef- ficient algorithms through structured state space duality,”arXiv preprint arXiv:2405.21060, 2024

work page internal anchor Pith review arXiv 2024
[16]

Graph mamba: Towards learning on graphs with state space models,

A. Behrouz and F. Hashemi, “Graph mamba: Towards learning on graphs with state space models,” inProceedings of the 30th ACM SIGKDD conference on knowledge discovery and data mining, 2024, pp. 119– 130

2024
[17]

Parameter-efficient transfer learning for nlp,

N. Houlsby, A. Giurgiu, S. Jastrzebski, B. Morrone, Q. De Laroussilhe, A. Gesmundo, M. Attariyan, and S. Gelly, “Parameter-efficient transfer learning for nlp,” inInternational conference on machine learning. PMLR, 2019, pp. 2790–2799

2019
[18]

Temporalmaxer: Maximize temporal context with only max pooling for temporal action localization,

T. N. Tang, K. Kim, and K. Sohn, “Temporalmaxer: Maximize temporal context with only max pooling for temporal action localization,”arXiv preprint arXiv:2303.09055, 2023

work page arXiv 2023
[19]

et al.: Video mamba suite: State space model as a versatile alternative for video understanding

G. Chen, Y . Huang, J. Xu, B. Pei, Z. Chen, Z. Li, J. Wang, K. Li, T. Lu, and L. Wang, “Video mamba suite: State space model as a versatile alternative for video understanding,”arXiv preprint arXiv:2403.09626, 2024

work page arXiv 2024
[20]

Bmn: Boundary-matching network for temporal action proposal generation,

T. Lin, X. Liu, X. Li, E. Ding, and S. Wen, “Bmn: Boundary-matching network for temporal action proposal generation,” inProceedings of the IEEE/CVF international conference on computer vision, 2019, pp. 3889–3898

2019
[21]

Learning salient boundary feature for anchor-free temporal action localization,

C. Lin, C. Xu, D. Luo, Y . Wang, Y . Tai, C. Wang, J. Li, F. Huang, and Y . Fu, “Learning salient boundary feature for anchor-free temporal action localization,” inProceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021, pp. 3320–3329

2021
[22]

End- to-end temporal action detection with transformer,

X. Liu, Q. Wang, Y . Hu, X. Tang, S. Zhang, S. Bai, and X. Bai, “End- to-end temporal action detection with transformer,”IEEE Transactions on Image Processing, vol. 31, pp. 5427–5441, 2022

2022
[23]

Etad: Training action detection end to end on a laptop,

S. Liu, M. Xu, C. Zhao, X. Zhao, and B. Ghanem, “Etad: Training action detection end to end on a laptop,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 4524–4533

2023
[24]

Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

L. Zhu, B. Liao, Q. Zhang, X. Wang, W. Liu, and X. Wang, “Vision mamba: Efficient visual representation learning with bidirectional state space model,”arXiv preprint arXiv:2401.09417, 2024

work page internal anchor Pith review arXiv 2024
[25]

Videomamba: State space model for efficient video understanding,

K. Li, X. Li, Y . Wang, Y . He, Y . Wang, L. Wang, and Y . Qiao, “Videomamba: State space model for efficient video understanding,” in European Conference on Computer Vision. Springer, 2025, pp. 237– 255

2025
[26]

Ms-temba: Multi- scale temporal mamba for efficient temporal action detection,

A. Sinha, M. S. Raj, P. Wang, A. Helmy, and S. Das, “Ms-temba: Multi- scale temporal mamba for efficient temporal action detection,”arXiv preprint arXiv:2501.06138, 2025

work page arXiv 2025
[27]

Soft-nms–improving object detection with one line of code,

N. Bodla, B. Singh, R. Chellappa, and L. S. Davis, “Soft-nms–improving object detection with one line of code,” inProceedings of the IEEE international conference on computer vision, 2017, pp. 5561–5569

2017
[28]

Bfstal: Bidirectional feature splitting with cross-layer fusion for temporal action localization,

J. Xu, Y . Zhang, W. Zhou, and H. Liu, “Bfstal: Bidirectional feature splitting with cross-layer fusion for temporal action localization,”IEEE Transactions on Circuits and Systems for Video Technology, 2025

2025
[29]

Temporal action localization with cross layer task decoupling and refinement,

Q. Li, D. Liu, J. Kong, S. Li, H. Xu, and J. Wang, “Temporal action localization with cross layer task decoupling and refinement,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 39, no. 5, 2025, pp. 4878–4886

2025
[30]

Boundary discretization and reliable classification network for temporal action detection,

Z. Fang, J. Yu, and R. Hong, “Boundary discretization and reliable classification network for temporal action detection,”IEEE Transactions on Multimedia, 2025

2025
[31]

End-to-end temporal action detection with 1b parameters across 1000 frames,

S. Liu, C.-L. Zhang, C. Zhao, and B. Ghanem, “End-to-end temporal action detection with 1b parameters across 1000 frames,”arXiv preprint arXiv:2311.17241, 2023

work page arXiv 2023
[32]

Videomae v2: Scaling video masked autoencoders with dual masking,

L. Wang, B. Huang, Z. Zhao, Z. Tong, Y . He, Y . Wang, Y . Wang, and Y . Qiao, “Videomae v2: Scaling video masked autoencoders with dual masking,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 14 549–14 560

2023
[33]

Scaling action detection: Adatad++ with transformer-enhanced temporal-spatial adap- tation,

T. Agrawal, A. Ali, A. Dantcheva, and F. Bremond, “Scaling action detection: Adatad++ with transformer-enhanced temporal-spatial adap- tation,” inProceedings of the IEEE/CVF International Conference on Computer Vision, 2025, pp. 12 222–12 231

2025
[34]

Mambatad: When state-space models meet long-range temporal action detection,

H. Lu, Y . Yu, S. Lu, D. Rajan, B. P. Ng, A. C. Kot, and X. Jiang, “Mambatad: When state-space models meet long-range temporal action detection,”arXiv preprint arXiv:2511.17929, 2025

work page arXiv 2025
[35]

Internvideo: General video foundation models via generative and discriminative learning

Y . Wang, K. Li, Y . Li, Y . He, B. Huang, Z. Zhao, H. Zhang, J. Xu, Y . Liu, Z. Wanget al., “Internvideo: General video foundation models via gen- erative and discriminative learning,”arXiv preprint arXiv:2212.03191, 2022

work page arXiv 2022
[36]

Ms- tct: Multi-scale temporal convtransformer for action detection,

R. Dai, S. Das, K. Kahatapitiya, M. S. Ryoo, and F. Br ´emond, “Ms- tct: Multi-scale temporal convtransformer for action detection,” inPro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 20 041–20 051

2022
[37]

Learning transferable visual models from natural language supervision,

A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clarket al., “Learning transferable visual models from natural language supervision,” inInternational conference on machine learning. PMLR, 2021, pp. 8748–8763

2021
[38]

Attributes-aware network for temporal action detection,

R. Dai, S. Das, M. S. Ryoo, and F. Br ´emond, “Attributes-aware network for temporal action detection,” inBMVC, 2023

2023
[39]

THUMOS challenge: Action recognition with a large number of classes,

Y .-G. Jiang, J. Liu, A. Roshan Zamir, G. Toderici, I. Laptev, M. Shah, and R. Sukthankar, “THUMOS challenge: Action recognition with a large number of classes,” 2014

2014
[40]

Activitynet: A large-scale video benchmark for human activity under- standing,

F. Caba Heilbron, V . Escorcia, B. Ghanem, and J. Carlos Niebles, “Activitynet: A large-scale video benchmark for human activity under- standing,” inProceedings of the ieee conference on computer vision and pattern recognition, 2015, pp. 961–970

2015
[41]

Hollywood in homes: Crowdsourcing data collection for activity understanding,

G. A. Sigurdsson, G. Varol, X. Wang, A. Farhadi, I. Laptev, and A. Gupta, “Hollywood in homes: Crowdsourcing data collection for activity understanding,” inEuropean conference on computer vision. Springer, 2016, pp. 510–526

2016
[42]

Decoupled Weight Decay Regularization

I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” arXiv preprint arXiv:1711.05101, 2017

work page internal anchor Pith review Pith/arXiv arXiv 2017
[43]

Attention is all you need,

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,”Advances in neural information processing systems, vol. 30, 2017

2017