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arxiv: 2606.06294 · v2 · pith:X3VXN4MXnew · submitted 2026-06-04 · 💻 cs.CV · cs.AI

Towards One-to-Many Temporal Grounding

Qi Xu , Yue Tan , Shihao Chen , Jiahao Meng , Anna Wang , Shunping Ji , Hao Fei , Jason Li This is my paper

Pith reviewed 2026-06-28 02:09 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords one-to-many temporal groundingvideo localizationmultimodal large language modelsreward functionschain-of-thoughtbenchmarkevent cardinality
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The pith

A new benchmark, 56k dataset, and caption rewards let models localize multiple disjoint video segments for one query.

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

The paper targets one-to-many temporal grounding, a setting where one textual query matches several disjoint segments in a video. Standard multimodal models optimized for single-segment retrieval produce near-zero scores because they lack awareness of how many events a query describes. The authors introduce the first dedicated benchmark with Count Accuracy and Effective Temporal F1 metrics, build a 56k-sample training set via a multi-stage curation pipeline, and design temporal and caption reward functions. The caption reward applies chain-of-thought reasoning over dense video captions to push policy optimization toward both segment precision and event completeness.

Core claim

We establish the one-to-many temporal grounding task and supply a complete solution: the first OMTG benchmark, a 56k-sample dataset built through a sophisticated construction pipeline, and novel temporal plus caption reward functions, where the caption reward uses Chain-of-Thought reasoning over dense video captions to steer optimization toward both preciseness and completeness, reaching 43.65% EtF1.

What carries the argument

Caption reward function that applies Chain-of-Thought reasoning over dense video captions to guide policy optimization for both preciseness and completeness.

If this is right

  • One-to-one temporal grounding methods are insufficient once queries require multiple disjoint segments.
  • Explicit cardinality perception must be trained through rewards that penalize both missing events and incorrect boundaries.
  • Chain-of-thought reasoning inside the reward can simultaneously enforce precision on segment boundaries and completeness across all matching events.
  • Large curated datasets are required to move MLLMs from near-zero to usable performance on this task.

Where Pith is reading between the lines

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

  • The same caption-reward pattern could be tested on other cardinality-sensitive tasks such as counting repeated actions across long videos.
  • The benchmark construction steps may transfer to audio or 3D scene data where one description maps to several disjoint intervals.
  • Models trained this way might improve downstream applications like video editing or surveillance query systems that naturally involve multiple matching clips.

Load-bearing premise

The 56k-sample dataset construction pipeline and the C-Acc/EtF1 metrics accurately reflect real-world one-to-many scenarios without systematic bias from the curation choices or metric definitions.

What would settle it

If models trained with the new rewards show no gain over standard fine-tuning when evaluated on a fresh set of human-annotated one-to-many queries drawn from sources outside the curation pipeline, the performance claim would be falsified.

Figures

Figures reproduced from arXiv: 2606.06294 by Anna Wang, Hao Fei, Jason Li, Jiahao Meng, Qi Xu, Shihao Chen, Shunping Ji, Yue Tan.

Figure 1
Figure 1. Figure 1: The Road to One-to-Many Temporal Grounding. Left: Leveraging our OMTG dataset, we empower the model to evolve from One-to-One to One-to-Many through SFT and RL, underpinned by novel temporal and caption rewards. Right: The capability landscape on the proposed OMTG Bench. As a fundamental task in video understanding, it has wit￾nessed significant advancements driven by Multi-modal Large Language Models (MLL… view at source ↗
Figure 2
Figure 2. Figure 2: The deceptiveness of tIoU in One-to-Many Temporal Grounding. Gemini 3 pro achieves high tIoU (> 0.9) in both examples despite event counting failures: under-segmentation (Left) and over-segmentation (Right). tIoU metrics fail to capture these counting errors. In contrast, our proposed EtF1 strictly penalizes count mismatches, providing a rigorous evaluation that highlights our method’s superior precision i… view at source ↗
Figure 3
Figure 3. Figure 3: Overview of our data construction pipeline: The annotation pipeline includes repetitive event discovery, initial one-to-many grounding, strict visual verification, recall check and query refinement. Policy Model Referenc Model KL Video&Query Outouts GRPO GT Segments Pred.; Segments Pred.; Caption Temporal Reward tIoU Score C-Acc Score Coverage Caption Guided Grounding tF1 Score Precision Length Penalty … view at source ↗
Figure 4
Figure 4. Figure 4: Composite reward function optimized via GRPO. The framework combines rule-based rewards for temporal precision with an LLM-as-a-judge mechanism for caption quality evaluation to improve one-to-many temporal grounding. event, the model generates a descriptive query, serving as the initial prompt for the subsequent stages. Stage 2: Initial One-to-Many Grounding. Using the gen￾erated queries, we prompt Gemini… view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative visualization of One-to-Many Temporal Grounding. We compare the predicted temporal segments from our model against state-of-the-art baselines across four diverse datasets. The green bars denote the Ground Truth. Baselines: Existing MLLMs (e.g., Gemini 3 Pro, Qwen3-VL) typically fail to capture the repetitiveness nature of events. They often retrieve only a single segment (e.g., Gemini 3 Pro in … view at source ↗
Figure 6
Figure 6. Figure 6: OMTG Benchmark Statistics. Left: The distribution of the number of temporal segments in the ground truth. Right: The histogram of video durations in the benchmark. distinctive features, the model prioritizes scene context over fine-grained motion semantics, leading to predictions that either dilute the precise temporal boundaries or completely drift away from the actual motion event. I. Annotation Interfac… view at source ↗
Figure 7
Figure 7. Figure 7: Failure case from video T42IZ.mp4 • If the video contains distinct events not covered by the list, the annotator adds a new query and labels its occurrences. 2. Submission. Annotations are automatically saved to a local json file. Once a batch is completed, this file is collected for quality verification. Annotation Quality Criteria: To ensure the benchmark rigorously evaluates a model’s ability to handle … view at source ↗
Figure 8
Figure 8. Figure 8: Illustration of our annotation interface. The tool allows annotators to watch videos and label multiple disjoint time segments for a given query. Pre-generated Gemini queries are provided as hints but require manual verification and adjustment. 24 [PITH_FULL_IMAGE:figures/full_fig_p024_8.png] view at source ↗
read the original abstract

Temporal Grounding (TG) aims to localize video segments corresponding to a textual query. Prior research predominantly focuses on single-segment retrieval. Real-world scenarios, however, often require localizing multiple disjoint segments for a single query -- a setting we term One-to-Many Temporal Grounding (OMTG). Previous state-of-the-art MLLMs, optimized for one-to-one settings, struggle in this context, often yielding near-zero scores due to a lack of event cardinality perception. To bridge this gap, we present a systematic solution with three key contributions. First, we establish the first comprehensive OMTG benchmark, introducing Count Accuracy (C-Acc) and Effective Temporal F1 (EtF1) as evaluation metrics. Second, we curate a high-quality OMTG dataset comprising 56k samples through a sophisticated construction pipeline. Third, we develop novel temporal and caption reward functions specifically designed for OMTG. In particular, the caption reward leverages Chain-of-Thought reasoning over dense video captions to explicitly guide policy optimization toward both preciseness and completeness. Extensive experiments show our model achieves a new state-of-the-art EtF1 of 43.65\% on OMTG Bench, outperforming Gemini 2.5 Pro and Seed-1.8 by 15.85\% and 15.61\%, respectively. Project Page: https://insomniaaac.github.io/OMTG/

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 / 1 minor

Summary. The paper introduces One-to-Many Temporal Grounding (OMTG), a task requiring localization of multiple disjoint video segments for one textual query. It establishes the first OMTG benchmark with new metrics Count Accuracy (C-Acc) and Effective Temporal F1 (EtF1), curates a 56k-sample dataset through a sophisticated construction pipeline, and proposes novel temporal and caption reward functions (the latter using Chain-of-Thought reasoning over dense captions) for policy optimization of MLLMs. Experiments report a new SOTA EtF1 of 43.65% on OMTG Bench, outperforming Gemini 2.5 Pro and Seed-1.8 by 15.85% and 15.61%.

Significance. If the new benchmark and metrics prove free of systematic curation or definition bias and accurately reflect real-world multi-event scenarios, the work would meaningfully extend temporal grounding beyond one-to-one settings. The caption reward with explicit CoT guidance for preciseness and completeness represents a targeted technical contribution. The 56k-sample dataset and benchmark release would provide a useful resource for the community.

major comments (2)
  1. [Abstract] Abstract: The headline SOTA claim (43.65% EtF1, +15.85% over Gemini 2.5 Pro) depends entirely on the newly introduced OMTG Bench, C-Acc/EtF1 metrics, and 56k-sample dataset. The abstract supplies no experimental details, ablation studies, error analysis, or validation that the construction pipeline and metric definitions avoid systematic bias in segment selection, annotation, or scoring. This is load-bearing for the central claim.
  2. [Abstract] Abstract / Experiments: No evidence is provided that the new metrics were validated against human judgments or existing one-to-many annotations, nor are ablations shown for the temporal vs. caption reward components. Without these, the performance gains cannot be attributed to the proposed method rather than benchmark-specific artifacts.
minor comments (1)
  1. [Abstract] The abstract introduces 'EtF1' and 'C-Acc' without an upfront definition or reference to their precise formulas, which should appear in the first paragraph where the metrics are named.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and indicate planned revisions to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The headline SOTA claim (43.65% EtF1, +15.85% over Gemini 2.5 Pro) depends entirely on the newly introduced OMTG Bench, C-Acc/EtF1 metrics, and 56k-sample dataset. The abstract supplies no experimental details, ablation studies, error analysis, or validation that the construction pipeline and metric definitions avoid systematic bias in segment selection, annotation, or scoring. This is load-bearing for the central claim.

    Authors: We agree the abstract is highly condensed. The full paper details the construction pipeline (Section 3), metric definitions with bias-mitigation rationale (Section 4), and experiments with ablations plus error analysis (Section 5). We will revise the abstract to briefly note the pipeline validation steps and key ablation outcomes supporting the SOTA result. revision: yes

  2. Referee: [Abstract] Abstract / Experiments: No evidence is provided that the new metrics were validated against human judgments or existing one-to-many annotations, nor are ablations shown for the temporal vs. caption reward components. Without these, the performance gains cannot be attributed to the proposed method rather than benchmark-specific artifacts.

    Authors: Metrics extend standard IoU-based F1 and cardinality measures; dataset construction included multi-round human verification. We will add an explicit subsection on metric design rationale and any available validation evidence. Ablations comparing reward components appear in the experiments; we will highlight them more clearly with cross-references and expand discussion of their incremental contributions. revision: partial

Circularity Check

0 steps flagged

No significant circularity; new benchmark, metrics, and rewards presented as independent contributions

full rationale

The paper introduces an original OMTG benchmark, C-Acc/EtF1 metrics, 56k-sample dataset via a described construction pipeline, and custom reward functions without any derivation that reduces by construction to fitted parameters, self-citations, or prior ansatzes from the same authors. The reported EtF1 score is an empirical result on the newly defined benchmark rather than a statistical prediction forced by the input data or metric definitions. No self-definitional loops, fitted-input predictions, or load-bearing self-citation chains appear in the provided text. The central claims rest on external model comparisons and the novelty of the OMTG setting, which are self-contained against the paper's own contributions.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no explicit free parameters, axioms, or invented entities; all contributions are empirical dataset and reward design.

pith-pipeline@v0.9.1-grok · 5793 in / 1104 out tokens · 30617 ms · 2026-06-28T02:09:20.456164+00:00 · methodology

discussion (0)

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Reference graph

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