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arxiv: 2604.10527 · v1 · submitted 2026-04-12 · 💻 cs.CV · cs.AI

Recognition: unknown

STORM: End-to-End Referring Multi-Object Tracking in Videos

Davide Modolo, Jingru Yi, Jue Wang, Junwen Chen, Xinyu Li, Yuxiao Chen, Zijia Lu

Authors on Pith no claims yet

Pith reviewed 2026-05-10 16:20 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords referring multi-object trackingend-to-end multimodal modeltask-composition learningvideo object groundingspatial-temporal reasoningRMOT benchmarkmulti-object trackinglanguage-guided video analysis
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The pith

STORM unifies referring grounding and multi-object tracking in one end-to-end multimodal model for videos.

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

The paper introduces STORM as an end-to-end multimodal large language model that performs referring multi-object tracking by jointly handling language-based grounding and object association in video. Earlier approaches split these steps into separate modules, which limited performance because of scarce annotated videos, ambiguous labels, and narrow domains. STORM instead uses a task-composition learning strategy that breaks the full task into image grounding and single-object tracking sub-tasks, letting the model draw on larger, cleaner datasets to build coherent spatial and temporal reasoning. It also releases STORM-Bench, a new dataset with precise trajectories and unambiguous referring expressions created through bottom-up annotation. If the unified approach holds, it removes the need for external detectors and supports more reliable tracking when objects are described in natural language across complex scenes.

Core claim

STORM is an end-to-end MLLM that jointly performs grounding and tracking within a unified framework, eliminating external detectors and enabling coherent reasoning over appearance, motion, and language. To improve data efficiency, it proposes a task-composition learning strategy that decomposes RMOT into image grounding and object tracking, allowing the model to leverage data-rich sub-tasks and learn structured spatial-temporal reasoning. The authors further construct STORM-Bench, a new RMOT dataset with accurate trajectories and diverse, unambiguous referring expressions generated through a bottom-up annotation pipeline. Extensive experiments demonstrate state-of-the-art results on image-gr

What carries the argument

The task-composition learning (TCL) strategy that decomposes the joint RMOT task into image grounding and object tracking sub-tasks so the model can acquire spatial-temporal reasoning from abundant separate data sources.

If this is right

  • The model reasons jointly over language, appearance, and motion instead of passing outputs between disconnected modules.
  • Performance improves on image grounding, single-object tracking, and full RMOT benchmarks simultaneously.
  • No external object detector is required, reducing error accumulation from separate components.
  • The same architecture generalizes across complex real-world video scenarios with diverse referring expressions.

Where Pith is reading between the lines

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

  • The TCL decomposition could be applied to other language-guided video tasks such as action recognition or event localization that currently suffer from limited joint annotations.
  • Removing separate detectors may lower overall system latency in real-time applications like surveillance or robotics.
  • STORM-Bench's bottom-up annotation method offers a template for creating cleaner benchmarks in related grounding and tracking domains.
  • Future extensions might test whether the unified reasoning also improves robustness when queries contain temporal references such as 'the car that just turned left'.

Load-bearing premise

Decomposing RMOT into separate image-grounding and tracking sub-tasks transfers structured spatial-temporal reasoning to the full joint task without loss of performance.

What would settle it

Run STORM trained only on end-to-end RMOT annotations without the TCL sub-task decomposition and compare its accuracy and generalization directly against the published TCL version on the same RMOT benchmarks.

Figures

Figures reproduced from arXiv: 2604.10527 by Davide Modolo, Jingru Yi, Jue Wang, Junwen Chen, Xinyu Li, Yuxiao Chen, Zijia Lu.

Figure 1
Figure 1. Figure 1: Overview of STORM method. The STORM adopts a LLaVA-style MLLM architecture [ [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Visualization of videos from our STORM-Bench dataset. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Visualization of complex referring expressions involv [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: Word cloud of the referring expressions in the STORM [PITH_FULL_IMAGE:figures/full_fig_p014_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Distribution of object classes in STORM-Bench. [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Model results with the referring expression [PITH_FULL_IMAGE:figures/full_fig_p015_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Model results with the referring expression [PITH_FULL_IMAGE:figures/full_fig_p016_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Model results with the referring expression [PITH_FULL_IMAGE:figures/full_fig_p016_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Model results with the referring expression [PITH_FULL_IMAGE:figures/full_fig_p017_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Model results with the referring expression [PITH_FULL_IMAGE:figures/full_fig_p017_11.png] view at source ↗
read the original abstract

Referring multi-object tracking (RMOT) is a task of associating all the objects in a video that semantically match with given textual queries or referring expressions. Existing RMOT approaches decompose object grounding and tracking into separated modules and exhibit limited performance due to the scarcity of training videos, ambiguous annotations, and restricted domains. In this work, we introduce STORM, an end-to-end MLLM that jointly performs grounding and tracking within a unified framework, eliminating external detectors and enabling coherent reasoning over appearance, motion, and language. To improve data efficiency, we propose a task-composition learning (TCL) strategy that decomposes RMOT into image grounding and object tracking, allowing STORM to leverage data-rich sub-tasks and learn structured spatial--temporal reasoning. We further construct STORM-Bench, a new RMOT dataset with accurate trajectories and diverse, unambiguous referring expressions generated through a bottom-up annotation pipeline. Extensive experiments show that STORM achieves state-of-the-art performance on image grounding, single-object tracking, and RMOT benchmarks, demonstrating strong generalization and robust spatial--temporal grounding in complex real-world scenarios. STORM-Bench is released at https://github.com/amazon-science/storm-referring-multi-object-grounding.

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.

Referee Report

2 major / 0 minor

Summary. The paper introduces STORM, an end-to-end multimodal large language model (MLLM) for referring multi-object tracking (RMOT) in videos. It jointly performs grounding and tracking within a unified framework without external detectors, using a task-composition learning (TCL) strategy that decomposes RMOT into image grounding and object tracking sub-tasks to leverage data-rich tasks and improve data efficiency. The authors also construct and release STORM-Bench, a new RMOT dataset with accurate trajectories and diverse referring expressions via a bottom-up annotation pipeline. Extensive experiments are claimed to show state-of-the-art performance on image grounding, single-object tracking, and RMOT benchmarks, with strong generalization in complex real-world scenarios.

Significance. If the experimental claims hold, this work could advance RMOT by providing a unified end-to-end MLLM approach that eliminates modular decomposition and improves data efficiency through task composition, with potential benefits for broader video-language tasks. The release of STORM-Bench addresses documented limitations in existing datasets (scarcity, ambiguity) and supports reproducibility. Credit is given for the dataset release and the coherent high-level framing of TCL as a transfer strategy.

major comments (2)
  1. Abstract: the claim of achieving state-of-the-art performance on image grounding, single-object tracking, and RMOT benchmarks rests on 'extensive experiments' but provides no visible details on error bars, ablation studies, dataset splits, or statistical tests, preventing assessment of whether the SOTA results are robust or load-bearing for the central empirical contribution.
  2. Task-composition learning strategy (as described in abstract): the assumption that decomposing RMOT into image grounding and object tracking transfers structured spatial-temporal reasoning effectively without performance loss on the joint task is central to the data-efficiency rationale, yet no supporting analysis, ablation, or transfer-gap quantification is visible to validate this weakest assumption.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thoughtful review, recognition of the dataset release, and the high-level framing of TCL. We address the two major comments point by point below, clarifying where the requested details appear in the manuscript and offering targeted revisions where they would strengthen the presentation.

read point-by-point responses

  1. Referee: Abstract: the claim of achieving state-of-the-art performance on image grounding, single-object tracking, and RMOT benchmarks rests on 'extensive experiments' but provides no visible details on error bars, ablation studies, dataset splits, or statistical tests, preventing assessment of whether the SOTA results are robust or load-bearing for the central empirical contribution.

    Authors: The abstract is intentionally concise. The full experimental protocol, including dataset splits for STORM-Bench and the public benchmarks, ablation studies on model components and TCL, error bars or standard deviations on key metrics (MOTA, IDF1, grounding accuracy), and multi-run comparisons are reported in Section 4. We will revise the abstract to add a short clause referencing these supporting analyses so readers can immediately locate the robustness evidence. revision: partial

  2. Referee: Task-composition learning strategy (as described in abstract): the assumption that decomposing RMOT into image grounding and object tracking transfers structured spatial-temporal reasoning effectively without performance loss on the joint task is central to the data-efficiency rationale, yet no supporting analysis, ablation, or transfer-gap quantification is visible to validate this weakest assumption.

    Authors: Section 3.2 formally defines the TCL decomposition and the staged training that re-uses image-grounding and tracking corpora. Section 4.3 contains the corresponding ablations: direct RMOT training versus TCL-augmented training, with quantitative gains on the joint task and explicit measurement of the transfer benefit from each sub-task. These results show improved data efficiency and no degradation on the full RMOT objective. We will add a dedicated paragraph that isolates the transfer-gap metric if the current quantification is judged insufficient. revision: partial

Circularity Check

0 steps flagged

No significant circularity; empirical claims rest on external benchmarks and new dataset

full rationale

The paper presents an end-to-end MLLM architecture (STORM) and a task-composition learning strategy (TCL) that decomposes RMOT into image grounding and tracking sub-tasks. Performance claims are framed as empirical outcomes on external benchmarks (image grounding, single-object tracking, RMOT) and a newly constructed dataset (STORM-Bench) with bottom-up annotations. No equations, first-principles derivations, or self-defined quantities appear in the provided text. No load-bearing step reduces by construction to fitted parameters, self-citations, or renamed inputs. The architecture and TCL are presented as design choices justified by data-efficiency rationale and standard multi-task transfer patterns, not as mathematically forced results. This is a standard honest non-finding for an applied ML paper whose central claims are falsifiable via benchmark evaluation.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Based solely on the abstract, no specific free parameters, axioms, or invented entities are identifiable. The approach likely relies on standard MLLM training assumptions and hyperparameters not detailed here.

pith-pipeline@v0.9.0 · 5532 in / 1072 out tokens · 47627 ms · 2026-05-10T16:20:07.283741+00:00 · methodology

discussion (0)

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