REVIEW 3 major objections 2 minor 1 references
Looking into the Unknown: Exploring Action Discovery for Segmentation of Known and Unknown Actions
T0 review · 3 major / 2 minor · reviewed 2026-08-05 · deepseek-v4-flash
Pith's one-line read The paper introduces Action Discovery, where only a subset of actions is annotated, and shows that unknown actions can be segmented and clustered by mimicking the granularity of the known ones.
desk verdict Abstract describes a plausible new action-discovery setup, but the full text is an unrelated paper—nothing below the title supports the claims. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The central machinery is the pairing of GGSM and UASA. GGSM is a segmentation module that identifies temporal intervals for both known and unknown actions by mimicking the granularity of the annotated actions, making the duration and transition structure of known actions a prior for all actions. UASA is an assignment step that clusters the embedding vectors of the proposed unknown intervals, using cluster membership to define discovered action classes. The load-bearing idea is that annotation granularity is a signal about the dataset's action structure, not just about the labeled subset.
What would settle it
Construct a partially labeled video dataset where known actions last about one second but unknown actions last tens of seconds, or vice versa, with hidden ground-truth labels. If GGSM splits or merges the unknown intervals to match the known durations, the granularity-mimicking premise is falsified; if the method still separates and clusters the unknown actions correctly, the premise survives.
Extended reading notes
Core claim
The central claim is that unknown, unlabeled actions in temporal action segmentation do not have to be treated as background or noise. The paper argues that known annotations carry two latent signals, temporal granularity and semantic embedding structure, that can be transferred to unknown actions. GGSM uses the annotated action segments to estimate the typical duration and transition structure of meaningful actions, then proposes intervals across the whole video at that same granularity. UASA then takes the feature embeddings of the proposed unknown intervals and clusters them, so each resulting cluster corresponds to a recurring unknown action type even though no label was ever provided. T
Load-bearing premise
The method assumes that the temporal granularity and embedding structure of unknown actions are similar enough to the known actions that guidance from known annotations transfers; if unknown actions occur at very different timescales or do not form coherent clusters in the learned embedding space, the approach collapses.
Editorial extensions
If this is right
- Temporal action segmentation can be trained on partially annotated videos while still producing complete segmentations in which unknown actions appear as explicit segments rather than background.
- In annotation-sparse domains such as neuroscience, Action Discovery can surface candidate action types that annotators can later name, turning a partial label set into a fuller one.
- The granularity of the known annotations acts as a control knob: coarser or finer known-action annotations should produce correspondingly coarser or finer discovered unknown segments.
- The setup provides a new evaluation protocol for partial-label action segmentation, with Breakfast, 50Salads, and Desktop Assembly as reference benchmarks.
Reading between the lines
- If the granularity-transfer premise holds, the same two-step recipe could be adapted to other partially labeled sequence problems, such as audio event detection or motion-capture activity analysis, where only a few event types are annotated.
- A direct stress test would be to vary the duration distribution of unknown actions relative to known ones; the paper's own logic predicts that discovered segments will track the known granularity, so a dataset with deliberately mismatched durations should expose the boundary.
- Provenance note: the full-text body attached to this record is a different manuscript; this summary is grounded in the paper's title, abstract, and reader notes.
Signed reviews
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The manuscript submitted under arXiv:2508.05529 has an abstract introducing "Action Discovery," a proposed new setup for Temporal Action Segmentation in which only known actions are annotated and unknown actions are segmented and clustered via two components: a Granularity-Guided Segmentation Module (GGSM) and an Unknown Action Segment Assignment (UASA). The abstract further claims systematic evaluation on Breakfast, 50Salads, and Desktop Assembly with considerable improvements over existing baselines. However, the full text supplied for review is an entirely different paper, "Mixed-Initiative Dialog for Human-Robot Collaborative Manipulation" (MICoBot), a cs.RO human-robot collaboration paper. The full text contains no description of GGSM or UASA, no Action Discovery formulation, no temporal action segmentation method, and no evaluation on the claimed datasets. The central claims of the abstract are therefore completely unsupported by the manuscript text.
Significance. If the Action Discovery setup and the proposed two-step method were actually implemented and evaluated as claimed, the work could be of practical interest for partially annotated video datasets, particularly in domains such as neuroscience where rare or ambiguous behaviors are under-annotated. The problem formulation is a reasonable extension of partially supervised temporal action segmentation. However, the submitted text provides no method, no derivation, no experimental protocol, no quantitative results, and no reproducibility artifacts. The visible paper is about human-robot dialog, not video segmentation. Consequently, the significance cannot be assessed beyond the abstract's promises, and the manuscript in its current form provides no scientific evidence for its main claims.
major comments (3)
- [Abstract vs. Full Text] The abstract announces a Temporal Action Segmentation method (GGSM and UASA) evaluated on Breakfast, 50Salads, and Desktop Assembly. The supplied full text is a completely different paper, "Mixed-Initiative Dialog for Human-Robot Collaborative Manipulation" (MICoBot), reporting physical robot trials with 18 participants. None of the terms GGSM, UASA, Action Discovery, temporal action segmentation, Breakfast, 50Salads, or Desktop Assembly appear in the full text. This is a load-bearing mismatch: the central claim of the paper cannot be checked because the supporting method and experiments are absent from the submitted manuscript.
- [Experimental evidence] The abstract states that the method "considerably improves upon existing baselines" on three datasets. No experiments, baselines, metrics, error bars, ablations, or comparisons are reported anywhere in the supplied text. Even setting the paper mismatch aside, the abstract alone does not constitute a testable result. The reader cannot verify the claimed improvements, and the claim is therefore unsubstantiated in the available material.
- [Scope of the reviewed manuscript] Because the full text is unrelated to the abstract, the manuscript is internally inconsistent at the level of its research contribution. This is not a minor presentational issue: it means that no section of the submitted text can be cited as evidence for the claimed Action Discovery method. The paper as submitted cannot be assigned to a reviewer for technical evaluation of the claimed contribution.
minor comments (2)
- [Header] The arXiv identifier in the review request (2508.05529) differs from the identifier visible in the full text's footer (2508.05535). This may indicate a pipeline error; the authors/editor should verify that the correct PDF is attached.
- [Author list] The author list of the full text (Yu et al.) does not correspond to any listed authors for the Action Discovery abstract. If this is a submission mix-up, the correct version should include the original authors and the corresponding title.
Circularity Check
No circular derivation can be identified because the reviewed full text is a different paper; the claimed Action Discovery method is not described, so there is no derivation chain to assess.
full rationale
The abstract announces Action Discovery for temporal action segmentation with GGSM and UASA modules and experiments on Breakfast, 50Salads, and Desktop Assembly. However, the supplied full text is 'Mixed-Initiative Dialog for Human-Robot Collaborative Manipulation' (MICoBot), a cs.RO paper about human-robot collaboration. None of the claimed method components, equations, baselines, or results appear in the reviewed text. Under the hard rule that circularity must be demonstrated by quoting a specific reduction (e.g., Eq. X = Eq. Y by construction, or a fitted parameter renamed as prediction), no such step can be exhibited because the claimed derivation is entirely absent. This is a missing-support / manuscript-mismatch issue, not a circularity finding. The score is therefore 0, with no circular steps identified.
Assumptions & free parameters
assumptions (3)
- domain assumption Known actions' temporal granularity is representative of unknown actions' granularity.
- domain assumption Unknown actions form distinct clusters in the embedding space learned from known actions.
- domain assumption The datasets contain unknown actions that are unannotated and discoverable.
Cite this review
Pith. "Pith review of Looking into the Unknown: Exploring Action Discovery for Segmentation of Known and Unknown Actions." pith.science (2026). https://pith.science/paper/K32VR5RR
@misc{pith2026250805529,
author = {Pith},
title = {Pith review of: Looking into the Unknown: Exploring Action Discovery for Segmentation of Known and Unknown Actions},
year = {2026},
howpublished = {\url{https://pith.science/paper/K32VR5RR}},
note = {Machine review of arXiv:2508.05529}
}
read the original abstract
We introduce Action Discovery, a novel setup within Temporal Action Segmentation that addresses the challenge of defining and annotating ambiguous actions and incomplete annotations in partially labeled datasets. In this setup, only a subset of actions - referred to as known actions - is annotated in the training data, while other unknown actions remain unlabeled. This scenario is particularly relevant in domains like neuroscience, where well-defined behaviors (e.g., walking, eating) coexist with subtle or infrequent actions that are often overlooked, as well as in applications where datasets are inherently partially annotated due to ambiguous or missing labels. To address this problem, we propose a two-step approach that leverages the known annotations to guide both the temporal and semantic granularity of unknown action segments. First, we introduce the Granularity-Guided Segmentation Module (GGSM), which identifies temporal intervals for both known and unknown actions by mimicking the granularity of annotated actions. Second, we propose the Unknown Action Segment Assignment (UASA), which identifies semantically meaningful classes within the unknown actions, based on learned embedding similarities. We systematically explore the proposed setting of Action Discovery on three challenging datasets - Breakfast, 50Salads, and Desktop Assembly - demonstrating that our method considerably improves upon existing baselines.
Reference graph
Works this paper leans on
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[1]
Mixed-Initiative Dialog for Human-Robot Collaborative Manipulation Albert Yu1,†, Chengshu Li 2, Luca Macesanu 3, Arnav Balaji 1, Ruchira Ray 4, Raymond Mooney 1, Roberto Martín-Martín 1 Abstract— Effective robotic systems for long-horizon human- robot collaboration must adapt to a wide range of human partners, whose physical behavior, willingness to assis...
arXiv 2026
Reviewed August 5, 2026 · model on record in the stance chip above.
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