arxiv: 2604.13426 · v1 · submitted 2026-04-15 · 💻 cs.CV · cs.AI

Recognition: unknown

Event-Adaptive State Transition and Gated Fusion for RGB-Event Object Tracking

Jinlin You, Muyu Li, Xudong Zhao

Pith reviewed 2026-05-10 13:06 UTC · model grok-4.3

classification 💻 cs.CV cs.AI

keywords RGB-Event trackingDynamic State Space ModelEvent-adaptive transitionGated Projection FusionMultimodal object trackingVision MambaCross-modal fusion

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The pith

MambaTrack adapts state transition matrices to event density and uses gated fusion for robust RGB-Event object tracking.

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

The paper addresses how static state transition matrices in Vision Mamba models for RGB-Event tracking fail to handle changes in event sparsity, causing underfitting on sparse streams and overfitting on dense ones. It introduces MambaTrack, which builds a Dynamic State Space Model with an event-adaptive state transition that modulates the matrix using event density and a learnable scalar, plus a Gated Projection Fusion module that projects RGB features and applies gates derived from density and confidence scores. This setup aims for balanced cross-modal integration that suppresses noise while keeping useful signals. A sympathetic reader would care because event data excels in high-speed or low-light scenes where RGB alone struggles, and better fusion could improve tracking reliability without heavy computation.

Core claim

MambaTrack is a multimodal and efficient tracking framework built upon a Dynamic State Space Model whose first contribution is an event-adaptive state transition mechanism that dynamically modulates the state transition matrix based on event stream density, with a learnable scalar governing the state evolution rate to enable differentiated modeling of sparse and dense event flows. Its second contribution is a Gated Projection Fusion module that projects RGB features into the event feature space and generates adaptive gates from event density and RGB confidence scores to precisely control fusion intensity.

What carries the argument

Event-adaptive state transition mechanism in a Dynamic State Space Model, which modulates the transition matrix according to event density with a learnable scalar, paired with the Gated Projection Fusion module that uses density and confidence to control cross-modal integration.

If this is right

Better balance in modeling sparse versus dense event streams, reducing underfitting and overfitting.
More precise suppression of noise during fusion while retaining complementary RGB and event information.
State-of-the-art tracking accuracy on the FE108 and FELT datasets.
Lightweight design that supports potential real-time embedded deployment.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The density-based adaptation could transfer to other state-space models handling variable-rate sensor inputs, such as in audio-visual or radar-vision fusion.
If the learnable scalar proves sensitive to initialization, adding constraints derived from event statistics might stabilize training across domains.
Extending the gated projection idea to multi-frame temporal fusion could further improve tracking in long sequences with changing conditions.

Load-bearing premise

That modulating the state transition matrix dynamically via event density and a learnable scalar, together with gates based on density and RGB confidence, will deliver stable robust fusion without introducing instability or needing heavy per-dataset tuning.

What would settle it

An ablation or comparison on a dataset with controlled extreme variations in event density where removing the adaptive modulation or the gates causes no performance drop or even improvement over the full MambaTrack model.

Figures

Figures reproduced from arXiv: 2604.13426 by Jinlin You, Muyu Li, Xudong Zhao.

**Figure 2.** Figure 2: Overall architecture of our proposed MambaTrack. The framework consists of two main [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Tracking results of our MambaTrack under 4 challenging conditions on FELT SOT dataset. [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗

read the original abstract

Existing Vision Mamba-based RGB-Event(RGBE) tracking methods suffer from using static state transition matrices, which fail to adapt to variations in event sparsity. This rigidity leads to imbalanced modeling-underfitting sparse event streams and overfitting dense ones-thus degrading cross-modal fusion robustness. To address these limitations, we propose MambaTrack, a multimodal and efficient tracking framework built upon a Dynamic State Space Model(DSSM). Our contributions are twofold. First, we introduce an event-adaptive state transition mechanism that dynamically modulates the state transition matrix based on event stream density. A learnable scalar governs the state evolution rate, enabling differentiated modeling of sparse and dense event flows. Second, we develop a Gated Projection Fusion(GPF) module for robust cross-modal integration. This module projects RGB features into the event feature space and generates adaptive gates from event density and RGB confidence scores. These gates precisely control the fusion intensity, suppressing noise while preserving complementary information. Experiments show that MambaTrack achieves state-of-the-art performance on the FE108 and FELT datasets. Its lightweight design suggests potential for real-time embedded deployment.

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

MambaTrack adds event-density modulation to the state transition and a gated projection fusion for RGB-event Mamba tracking, but the abstract supplies no ablations or baseline runs to show these drive the claimed SOTA gains.

read the letter

The paper's main move is to replace the fixed state transition matrix in a Vision Mamba tracker with one that scales according to event stream density, controlled by a learnable scalar, and to add a Gated Projection Fusion module that projects RGB features into event space and uses density plus confidence scores to set the mixing weights. The stated goal is to avoid underfitting sparse events and overfitting dense ones while keeping fusion selective against noise. That target is reasonable given how event cameras behave in practice. The description of the two modules is clear enough at the conceptual level to show what the authors are trying to change from prior static Mamba tracking work. The lightweight framing also suggests they kept efficiency in mind for possible embedded use. The soft spot is straightforward: the abstract states SOTA results on FE108 and FELT but gives no equations, no ablation tables, no direct comparison against a non-adaptive Mamba baseline, and no check on how much the learnable scalar or the gates actually move the numbers. Without those controls it is impossible to know whether the reported gains come from the proposed adaptations or from other implementation choices. The concern about missing verification is not minor here because the central claim attributes performance to exactly these mechanisms. Readers working on multimodal or event-based tracking could still pull the high-level ideas for their own experiments, especially if they already use Mamba backbones. Anyone who needs reproducible gains or wants to cite the specific improvements will have to wait for the full paper, code, and detailed results before deciding. I would send it to peer review once the experiments are in place; the direction is concrete enough to be worth referee time even if the current version leaves the attribution untested.

Referee Report

3 major / 1 minor

Summary. The manuscript introduces MambaTrack, a multimodal RGB-Event tracking framework built on a Dynamic State Space Model (DSSM). It proposes an event-adaptive state transition mechanism that modulates the state transition matrix according to event stream density using a learnable scalar, and a Gated Projection Fusion (GPF) module that projects RGB features into event space and applies adaptive gates derived from event density and RGB confidence scores. The paper claims that these components enable robust cross-modal fusion and achieve state-of-the-art performance on the FE108 and FELT datasets.

Significance. If the proposed mechanisms can be shown to drive measurable robustness gains over static-transition baselines, the work would address a practical limitation in handling variable event sparsity for efficient multimodal tracking. The emphasis on a lightweight design is a strength that could support real-time deployment, but the current lack of verifiable experimental support limits the assessed impact.

major comments (3)

Abstract: The central claim of SOTA performance on FE108 and FELT is presented without any quantitative metrics, baseline comparisons, or ablation results, leaving the attribution of gains specifically to the event-adaptive state transition and GPF unsupported.
Method section (DSSM and event-adaptive transition): No equations or explicit formulations are supplied for the density-based modulation of the state transition matrix or the learnable scalar's role in governing evolution rate, preventing verification that the mechanism avoids the stated underfitting/overfitting problems.
Experiments section: Absence of ablation studies comparing the full model against a static-transition Mamba variant or non-gated fusion baseline means it is impossible to confirm that the proposed adaptations, rather than backbone choice or optimization details, produce the reported improvements.

minor comments (1)

Abstract: The phrase 'Experiments show that MambaTrack achieves state-of-the-art performance' should be accompanied by at least the primary metrics (e.g., success rate, precision) and the number of compared methods for immediate context.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We agree that the current presentation can be strengthened by adding quantitative support in the abstract, explicit formulations in the method, and targeted ablations in the experiments. We will revise the manuscript accordingly to better substantiate the contributions of the event-adaptive state transition and Gated Projection Fusion modules.

read point-by-point responses

Referee: Abstract: The central claim of SOTA performance on FE108 and FELT is presented without any quantitative metrics, baseline comparisons, or ablation results, leaving the attribution of gains specifically to the event-adaptive state transition and GPF unsupported.

Authors: We acknowledge that the abstract would be more informative with key quantitative results. In the revised version we will update the abstract to report specific metrics (e.g., success rate and precision on FE108 and FELT) together with the main baseline comparisons, thereby directly linking the reported gains to the proposed components. revision: yes
Referee: Method section (DSSM and event-adaptive transition): No equations or explicit formulations are supplied for the density-based modulation of the state transition matrix or the learnable scalar's role in governing evolution rate, preventing verification that the mechanism avoids the stated underfitting/overfitting problems.

Authors: We agree that explicit mathematical formulations are necessary for verification. Although the manuscript describes the mechanism at a conceptual level, we will insert the full set of equations for the density-modulated state transition matrix and the learnable scalar in the revised Method section, together with a short derivation showing how the adaptation mitigates under- and overfitting across event densities. revision: yes
Referee: Experiments section: Absence of ablation studies comparing the full model against a static-transition Mamba variant or non-gated fusion baseline means it is impossible to confirm that the proposed adaptations, rather than backbone choice or optimization details, produce the reported improvements.

Authors: We recognize the value of isolating the contribution of each proposed module. The revised Experiments section will include new ablation tables that compare the full MambaTrack model against (i) a static-transition DSSM variant and (ii) a non-gated fusion baseline, while keeping the backbone and training protocol fixed. These results will quantify the incremental gains attributable to the event-adaptive transition and GPF. revision: yes

Circularity Check

0 steps flagged

No circularity in proposed mechanisms or claims

full rationale

The paper introduces MambaTrack via two new modules—an event-adaptive state transition that modulates the matrix using event density plus a learnable scalar, and a Gated Projection Fusion module with density-and-confidence gates—without any equations, derivations, or predictions that reduce by construction to fitted inputs, self-referential terms, or prior self-citations. No load-bearing uniqueness theorems, ansatzes smuggled via citation, or renamings of known results appear; the SOTA claims rest on empirical results on FE108 and FELT rather than tautological definitions. The architecture is presented as independent additions addressing static-matrix limitations, making the derivation chain self-contained.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 2 invented entities

The central claim depends on the effectiveness of two newly introduced modules whose behavior is governed by a learnable scalar and density-based gating, plus the domain assumption that event sparsity is the dominant source of prior method failure.

free parameters (1)

learnable scalar governing state evolution rate
Introduced to control differentiated modeling of sparse versus dense event streams; its value is learned from data.

axioms (1)

domain assumption Static state transition matrices in Vision Mamba fail to adapt to variations in event sparsity, causing underfitting or overfitting
This premise motivates the entire adaptive mechanism and is stated as the core limitation of existing methods.

invented entities (2)

Event-adaptive state transition mechanism no independent evidence
purpose: Dynamically modulates the state transition matrix according to event stream density
New component proposed to replace static matrices
Gated Projection Fusion module no independent evidence
purpose: Projects RGB features into event space and generates adaptive gates from density and confidence
New fusion component for cross-modal integration

pith-pipeline@v0.9.0 · 5496 in / 1346 out tokens · 33038 ms · 2026-05-10T13:06:56.742041+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

9 extracted references · 9 canonical work pages · 5 internal anchors

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