MambaLCT: Boosting Tracking via Long-term Context State Space Model

Bineng Zhong; Guorong Li; Qihua Liang; Shuxiang Song; Xiaohai Li; Zhiyi Mo

REVIEW 3 major objections 5 minor 1 cited by

MambaLCT: Boosting Tracking via Long-term Context State Space Model

T0 review · 3 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read Visual tracking usually forgets the target's past; MambaLCT argues that a state space model can remember every frame from the first to the present, and that this long-term memory is what pushes performance to new highs on six benchmarks.

desk verdict A well-ablated Mamba-based tracker whose long-term memory claim is undercut by a 2-frame training clip; the gains are real but the mechanism isn't proven. read the letter →

arxiv 2412.13615 v1 pith:43ERK33L submitted 2024-12-18 cs.CV

Xiaohai Li , Bineng Zhong , Qihua Liang , Guorong Li , Zhiyi Mo , Shuxiang Song This is my paper · ORCID

classification cs.CV

keywords visualobjecttrackinglong-termcontextstatespacemodelMambaselectivescanningtransformerencodertokenbenchmarkevaluation

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

Existing trackers build target context from adjacent frames or short video clips, so they lose sight of how the target has changed over a long sequence. MambaLCT instead scans the features of every search frame from the first frame to the current frame with a unidirectional Mamba state space model, compressing target-related cues into a single evolving context token. That token is injected into the attention of a transformer encoder that jointly models template, search frame, and context, so appearance learning and long-term memory are unified. The paper reports that this design reaches new state-of-the-art scores on LaSOT, LaSOText, GOT-10K, TrackingNet, TNL2K and UAV123 while running in real time, and that the context token's attention indeed focuses on the target as the sequence grows.

What carries the argument

The Context Mamba module is a unidirectional state space model that scans frame features in temporal order. Each frame's features $f_i$ update the hidden state $H_i^t = A H_i^{t-1} + B f_i^t$, and an empty token $T_i$ carries the aggregated history between frames via $H_i^T = A H_i^L + B T_i$, producing $Y_i^T = C H_i^T$ that updates the context token $c_p$. The context token has length 1, so the entire history is compressed into one vector that is injected into the attention of the ucaEncoder, a hierarchical ViT that jointly models template, search frame, and context. This separation of labor — transformer for spatial appearance, Mamba for temporal memory — is what lets the context length grow to the whole sequence at linear cost.

What would settle it

Run inference while resetting the Context Mamba hidden state and context token every $K$ frames (for example every 50 frames), and compare AUC on LaSOT with the full-history version. If performance does not drop, the benchmark gains come from module capacity rather than from long-term memory; alternatively, if a fixed two-frame context window matches full-history performance, the long-term mechanism is not load-bearing.

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Extended reading notes

Core claim

The central claim is that long-term context — not just the initial template or a short window — is what a tracker needs to stay accurate through deformation, full occlusion, and fast motion. MambaLCT proposes to build that context by feeding the autoregressive appearance features of each search frame, along with a learned context token, into a Context Mamba module; the selective scanning mechanism accumulates target-related information in a hidden state that carries across frames. The updated context token is then concatenated into the next encoder step, so the template-search relationship is modeled with knowledge of the target's entire history. Experiments on six benchmarks, with ablations on LaSOT, are used to support the claim that this long-term context is responsible for the gains, not the extra parameters or the video sampling alone.

Load-bearing premise

The hidden state of the Context Mamba, trained only on two-frame clips, is assumed to keep accumulating useful target cues when unrolled over the full test sequence, even though the paper states that training and testing phases cannot be unified.

Editorial extensions

If this is right

MambaLCT-384 reaches 73.6 AUC on LaSOT, 53.3 AUC on LaSOText, 85.2 AUC on TrackingNet, and 58.5 AUC on TNL2K, ahead of the short-context trackers it is compared with.
The same model runs at 45.3 fps on a Tesla V100, so long-term context can be added without sacrificing real-time speed.
Attribute-level results on LaSOT show the largest gains in motion blur, full occlusion, and deformation, the situations where remembering the target's history should matter most.
Ablations indicate that sequence sampling by itself does not help; the improvement appears only when the sampled video is fed through the Context Mamba module, locating the effect in the long-term context mechanism.
A single context token of length 1 outperforms longer context token sequences, consistent with Mamba's autoregressive accumulation of redundant information.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the mechanism is doing what the paper claims, then training on longer clips or with a curriculum that gradually extends sequence length should close the train-test gap and push all six benchmarks higher still.
The unidirectional scan means the context token only knows the past; a bidirectional or chunked scan might help after a target disappears and reappears, since the tracker could then also use evidence from frames after the occlusion.
Because the context token is a single compact vector, the method should compose naturally with online template update or memory replay: store the token and update it cheaply, without re-encoding the full history.
The same context-injection idea could transfer to other single-object tracking frameworks or to video object segmentation, where target appearance also drifts over long clips.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Short version: MambaLCT is a clearly written, well-ablated tracker that adds a unidirectional Mamba module to accumulate target cues across frames. The gains over its HiViT baseline are real (about +1.3 AUC on LaSOT), and the paper is honest about its main weakness: training on 2-frame clips while testing on full sequences. That weakness is significant enough that the "long-term context" story is not directly proven.

The genuinely new piece is using a state-space model for video-level context in single-object tracking, as opposed to the fixed-window Transformers of ODTrack and AQATrack. The ablation table is clean: sequence sampling alone doesn't help, adding Mamba does. The speed numbers are competitive. That's a solid contribution to a mature field.

The soft spots: (1) The abstract says "new SOTA on six benchmarks," but Table 2 shows ODTrack beats MambaLCT-384 on GOT-10K (77.0 vs 76.2 AO). That's an overclaim. (2) The train/test mismatch is not a minor detail. With clip length 2, the Mamba parameters are only ever trained to integrate two frames; at inference it unrolls over hundreds. The ablation shows the module adds capacity, but it doesn't show that capacity is being used for long-term memory. The visualization in Fig. 6 is suggestive but not controlled. (3) No variance or multiple-seed reporting, which is common but would help given the small margins.

The limitations paragraph acknowledges the train/test gap and calls it future work. That's honest, but it also means the central claim of the paper—that long-term context is what helps—remains unverified. The +1.3 AUC could come from the extra Vim parameters or from two-frame information. Still, the paper is worth engaging: the design is sensible, the experiments are thorough enough to be informative, and the train/test mismatch is a testable hypothesis for follow-up. I'd send it to review. The referee should ask for a version with longer training clips (even 4 or 8) and per-resolution SOTA claims.

Referee Report

3 major / 5 minor

Summary. The paper proposes MambaLCT, a single-object tracker that augments a HiViT-based encoder with a unidirectional Context Mamba module intended to aggregate target-related cues from the first frame to the current frame. The context tokens are updated by a recurrent state-space update (Eqs. 7-8) and injected into the encoder's attention. Training uses clips of length 2 sampled from GOT-10K, LaSOT, COCO, and TrackingNet, while inference unrolls the recurrence over entire test sequences. The authors report results on LaSOT, LaSOText, GOT-10K, TrackingNet, TNL2K and UAV123, plus ablations on LaSOT, and claim new SOTA on six benchmarks.

Significance. If the long-term context mechanism were validated, the paper would provide a practically interesting combination of Mamba's linear-time recurrence with transformer appearance modeling for visual tracking, and the reported real-time speed (45.3 fps at 384 resolution) and public code and models are useful assets. The empirical coverage is broad, and the ablation in Table 4(a) isolates the Mamba addition from the HiViT backbone. However, as detailed below, the paper's central attribution of gains to long-term memory is not directly supported by the training setup, and the SOTA claim is contradicted by its own GOT-10K numbers.

major comments (3)

[Training Details; Inference Details; Limitation; Eqs. (7)-(8); Table 4(a)] Training Details state that the video clip sampling length is 2, and the Limitation section concedes that 'the training and testing phases cannot be unified.' The Context Mamba recurrence in Eqs. (7)-(8) is therefore trained only through two frame-to-frame transitions, while at inference it is unrolled over sequences of hundreds of frames. Under this mismatch, the gain of the Mamba module in Table 4(a) (71.8 vs 70.1 for #4 vs #3) cannot be attributed specifically to long-term context; it could come from the added Vim-Small capacity or from two-frame context alone. Please provide evidence that the hidden-state dynamics are stable for lengths far beyond the training clip length (e.g., train with clip lengths 4 or 8, or ablate with variable context at inference), or soften the long-term-context claim accordingly.
[Abstract; Table 2] The abstract claims 'new SOTA performance on six benchmarks', but Table 2 shows that on GOT-10K, ODTrack achieves AO 77.0, SR0.5 87.9 and SR0.75 75.1, all higher than MambaLCT-384's 76.2, 86.7 and 74.3. Since GOT-10K is one of the six benchmarks listed, the SOTA claim is not supported by the reported numbers. Please correct the claim (e.g., state the benchmarks and resolutions on which the method is actually SOTA).
[Training Details; Table 2 footnote] The training data are stated as GOT-10K, LaSOT, COCO and TrackingNet, while Table 2's footnote says the star on GOT-10K denotes trackers trained only on GOT-10K. If the GOT-10K protocol requires training only on GOT-10K, the MambaLCT GOT-10K result is not protocol-compliant; if it does not, the footnote is misleading. Please clarify this discrepancy and, if necessary, report a model trained only on GOT-10K for that column.

minor comments (5)

[Table 4(b)] The header says 'Sampling Length' while the surrounding text discusses the length of the context token cp; please make the label consistent with the variable being varied.
[Eqs. (7)-(8)] The notation uses t for the token index within a frame and T for the cross-frame transition token, which is easy to confuse; please define both superscripts explicitly.
[Table 1] The header contains the typo 'Spees' for 'Speed'.
[Figure 6] The qualitative attention maps are suggestive but not quantitative; consider reporting an average attention-target overlap metric over a larger set of sequences to substantiate the claim that attention becomes more focused with sequence length.
[Related Work] The statement that ODTrack and AQATrack construct fixed-length context of length 4 should be verified against the original papers, since ODTrack's temporal token is also described as propagating across the whole sequence.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: benchmark claims are empirical comparisons, not derived from fitted parameters or self-citation chains.

full rationale

MambaLCT's central claims—that long-term context aggregation improves robustness and that it achieves new SOTA on six benchmarks—are empirical measurements against published baselines. The Context Mamba recurrence (Eqs. 6–8) is a network architecture trained with the loss in Eq. (10); no predicted quantity is defined in terms of the benchmark target, and no parameter is fitted to a benchmark and then renamed as a prediction. The comparisons to ODTrack and AQATrack are against published numbers from prior work, including same-group papers, but those numbers are external constraints, not inputs to the derivation, so this is self-citation without load-bearing circularity. The stated Limitation—'the training and testing phases cannot be unified'—is a genuine generalization and validation risk for the long-term-context mechanism, but it concerns distribution shift between 2-frame training clips and full-sequence inference, not definitional circularity. Similarly, the observation that ODTrack's GOT-10K AO (77.0) exceeds MambaLCT-384 (76.2) in Tab. 2 is a correctness inconsistency with the 'new SOTA' claim, not a circularity. The paper is self-contained against external benchmarks for its empirical claims, so no circular step is present.

Assumptions & free parameters 9 free parameters · 4 assumptions · 0 invented entities

The central claim rests on standard deep learning hyperparameters and pretrained checkpoints, plus an architectural assumption that Mamba can compress target history into a fixed-size state. No new physical entities are introduced.

free parameters (9)

loss weight lambda1 = 5
Manual weight for L1 regression loss in Eq. (10).
loss weight lambda2 = 2
Manual weight for GIoU loss in Eq. (10).
context token length Nc = 1
Chosen by ablation; longer context tokens reduce performance on LaSOT.
Mamba layer insertion indices = (3, 6, 9)
Selected from ablation comparing first, last, and uniform insertion layers.
training clip sampling length = 2
Set in training; inconsistent with inference-time full-sequence context.
sampling quantity = 30000
Number of video clips sampled per epoch during training.
backbone learning rate = 2e-4
AdamW learning rate for the HiViT backbone.
other learning rate = 2e-3
Ten times the backbone learning rate for the rest of the network.
batch size = 16
Batch size used during training.

assumptions (4)

domain assumption Pretrained HiViT-Base (MAE) and Vim-Small checkpoints provide useful initialization.
The method's performance depends on transfer from these pretrained weights.
domain assumption Mamba's selective scan compresses target-relevant features into a fixed-size hidden state without losing critical cues.
Equations (7) and (8) assume the hidden state H retains enough target information to improve later frames.
domain assumption The Transformer encoder can effectively combine context, template, and search tokens through attention.
The ucaEncoder design assumes that injecting cp into attention improves template-search relation modeling.
domain assumption Standard classification, L1, and GIoU losses are sufficient to train the tracking framework.
No auxiliary losses or recurrent supervision are used to enforce long-term retention.

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Cite this review

Pith. "Pith review of MambaLCT: Boosting Tracking via Long-term Context State Space Model." pith.science (2026). https://pith.science/paper/43ERK33L

@misc{pith2026241213615,
  author       = {Pith},
  title        = {Pith review of: MambaLCT: Boosting Tracking via Long-term Context State Space Model},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/43ERK33L}},
  note         = {Machine review of arXiv:2412.13615}
}

read the original abstract

Effectively constructing context information with long-term dependencies from video sequences is crucial for object tracking. However, the context length constructed by existing work is limited, only considering object information from adjacent frames or video clips, leading to insufficient utilization of contextual information. To address this issue, we propose MambaLCT, which constructs and utilizes target variation cues from the first frame to the current frame for robust tracking. First, a novel unidirectional Context Mamba module is designed to scan frame features along the temporal dimension, gathering target change cues throughout the entire sequence. Specifically, target-related information in frame features is compressed into a hidden state space through selective scanning mechanism. The target information across the entire video is continuously aggregated into target variation cues. Next, we inject the target change cues into the attention mechanism, providing temporal information for modeling the relationship between the template and search frames. The advantage of MambaLCT is its ability to continuously extend the length of the context, capturing complete target change cues, which enhances the stability and robustness of the tracker. Extensive experiments show that long-term context information enhances the model's ability to perceive targets in complex scenarios. MambaLCT achieves new SOTA performance on six benchmarks while maintaining real-time running speeds.

Figures

Figures reproduced from arXiv: 2412.13615 by the authors.

**Figure 2.** Overview of our framework. The input video frames are converted into tokens through patch embedding. Then, [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Illustration of the process of constructing and prop [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: Attribute-based evaluation on the LaSOT test set. [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

**Figure 6.** Figure 6: Visualization of the attention to the search frame [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

Explicit Context Reasoning with Supervision for Visual Tracking
cs.CV 2025-07 conditional novelty 6.0 of 10

RSTrack supervises a Mamba-based state reasoning module with true target states, improving visual tracking accuracy on six benchmarks.

Reference graph

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, " * write output.state after.block = add.period write newline

ENTRY address archivePrefix author booktitle chapter edition editor eid eprint howpublished institution isbn journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all...
[35]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.

[1] [1]

F.; Vedaldi, A.; and Torr, P

Bertinetto, L.; Valmadre, J.; Henriques, J. F.; Vedaldi, A.; and Torr, P. H. 2016. Fully-convolutional siamese networks for object tracking. In Computer Vision--ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part II 14, 850--865. Springer

work page 2016

[2] [2]

Cai, Y.; Liu, J.; Tang, J.; and Wu, G. 2023. Robust object modeling for visual tracking. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 9589--9600

work page 2023

[3] [3]

Chen, X.; Peng, H.; Wang, D.; Lu, H.; and Hu, H. 2023. Seqtrack: Sequence to sequence learning for visual object tracking. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 14572--14581

work page 2023

[4] [4]

Chen, X.; Yan, B.; Zhu, J.; Wang, D.; Yang, X.; and Lu, H. 2021. Transformer tracking. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 8126--8135

work page 2021

[5] [5]

Chen, Z.; Zhong, B.; Li, G.; Zhang, S.; Ji, R.; Tang, Z.; and Li, X. 2022. SiamBAN: Target-aware tracking with Siamese box adaptive network. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(4): 5158--5173

2022

[6] [6]

Cui, Y.; Jiang, C.; Wang, L.; and Wu, G. 2022. Mixformer: End-to-end tracking with iterative mixed attention. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 13608--13618

work page 2022

[7] [7]

Danelljan, M.; Bhat, G.; Shahbaz Khan, F.; and Felsberg, M. 2017. Eco: Efficient convolution operators for tracking. In Proceedings of the IEEE conference on computer vision and pattern recognition, 6638--6646

work page 2017

[8] [8]

Fan, H.; Lin, L.; Yang, F.; Chu, P.; Deng, G.; Yu, S.; Bai, H.; Xu, Y.; Liao, C.; and Ling, H. 2019. Lasot: A high-quality benchmark for large-scale single object tracking. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 5374--5383

work page 2019