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arxiv: 2512.03666 · v2 · submitted 2025-12-03 · 💻 cs.CV · cs.AI

ToG-Bench: Task-Oriented Spatio-Temporal Grounding in Egocentric Videos

Qi'ao Xu , Tianwen Qian , Yuqian Fu , Kailing Li , Yang Jiao , Jiacheng Zhang , Xiaoling Wang , Liang He This is my paper

Pith reviewed 2026-05-17 02:45 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords task-oriented spatio-temporal groundingegocentric videobenchmarkexplicit-implicit groundingone-to-many groundingmultimodal large language modelsembodied intelligence
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The pith

ToG-Bench requires models to localize objects needed for intended tasks in egocentric videos, including implicit and multi-object cases.

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

This paper establishes ToG-Bench as the first benchmark for spatio-temporal video grounding that centers on task-oriented instructions rather than simple object descriptions. It incorporates explicit-implicit dual grounding, where targets may need contextual inference, and one-to-many grounding, where one instruction links to multiple relevant objects. A sympathetic reader would care because these capabilities are essential for embodied agents that must translate goals into physical interactions. The benchmark draws 100 clips from ScanNet videos and supplies 2,704 instructions via a semi-automated annotation process. Experiments on seven multimodal large language models expose clear performance shortfalls on the new task dimensions.

Core claim

We introduce ToG-Bench, the first task-oriented spatio-temporal video grounding benchmark for egocentric videos, characterized by task-oriented grounding that identifies objects based on intended tasks, explicit-implicit dual grounding that handles both mentioned and inferred targets, and one-to-many grounding that allows multiple objects per instruction.

What carries the argument

The three key features of ToG-Bench: task-oriented grounding based on intended tasks, explicit-implicit dual grounding, and one-to-many grounding that links single instructions to multiple objects.

If this is right

  • Task-level metrics are required to properly score multi-object and explicit-implicit cases rather than single-target accuracy.
  • Current multimodal large language models exhibit substantial gaps when required to bridge perception with goal-directed interaction.
  • Embodied agents need additional reasoning layers to move from descriptive localization to task-driven object selection.
  • Performance on ToG-Bench highlights the remaining distance between existing video grounding methods and practical embodied use.

Where Pith is reading between the lines

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

  • Robotic systems trained only on descriptive grounding may fail when given goal-oriented commands in real environments.
  • The benchmark could be extended to longer video sequences or active exploration settings to test sequential decision making.
  • Models that succeed here might transfer better to downstream tasks such as instruction-following manipulation.

Load-bearing premise

The semi-automated pipeline that combines foundation model annotation and human refinement produces accurate, unbiased task-oriented instructions that faithfully capture the intended explicit-implicit and one-to-many distinctions.

What would settle it

Systematic evaluation of whether state-of-the-art multimodal models achieve high accuracy on the explicit-implicit and one-to-many subsets of ToG-Bench while still struggling on standard descriptive grounding would confirm or refute the claimed distinct challenges.

Figures

Figures reproduced from arXiv: 2512.03666 by Jiacheng Zhang, Kailing Li, Liang He, Qi'ao Xu, Tianwen Qian, Xiaoling Wang, Yang Jiao, Yuqian Fu.

Figure 1
Figure 1. Figure 1: Illustration and comparison of existing STVG bench [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Semi-automated Annotation Pipeline. The process con [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Dataset characteristics of ToG-Bench. Top-left: Task distribution by type (explicit vs. implicit) and object count; Top-right: Object category frequency (top 40 categories); Bottom: Example grounding tubes for explicit (blue) and implicit (pink) tasks, highlighting contextual inference and multi-object grounding. Task-Oriented Instruction Generation. We begin by generating a structured caption for each vid… view at source ↗
Figure 4
Figure 4. Figure 4: Video duration distribution: bars show video count per [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Task-level performance of GPT-5 across video duration bins on ToG-Bench. Left: task accuracy (T-Acc). Middle: temporal [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: The distribution of different types of objects in ToG-Bench. [PITH_FULL_IMAGE:figures/full_fig_p012_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: More explicit samples. 14 [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: More implicit samples. 15 [PITH_FULL_IMAGE:figures/full_fig_p015_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Explicit spatio-temporal grounding for “Turn off the heater”: model predictions vs. ground truth (GT) [PITH_FULL_IMAGE:figures/full_fig_p016_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Implicit spatio-temporal grounding for “Get what you need to clean the floor”: model predictions vs. ground truth (GT). 16 [PITH_FULL_IMAGE:figures/full_fig_p016_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Explicit spatio-temporal grounding for “Use the marker to write on the whiteboard”: model predictions vs. ground truth (GT). 17 [PITH_FULL_IMAGE:figures/full_fig_p017_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Implicit spatio-temporal grounding for “Turn on what’s near the refrigerator to improve the air quality”: model predictions vs. ground truth (GT). 18 [PITH_FULL_IMAGE:figures/full_fig_p018_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: The complete prompt utilized for inference. [PITH_FULL_IMAGE:figures/full_fig_p019_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: System prompt for explicit task generation. [PITH_FULL_IMAGE:figures/full_fig_p020_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: User prompt for explicit task generation. [PITH_FULL_IMAGE:figures/full_fig_p021_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: System prompt for implicit task generation. [PITH_FULL_IMAGE:figures/full_fig_p022_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: User prompt for implicit task generation. [PITH_FULL_IMAGE:figures/full_fig_p023_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: System prompt for explicit task validation. [PITH_FULL_IMAGE:figures/full_fig_p024_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: User prompt for explicit task validation. [PITH_FULL_IMAGE:figures/full_fig_p025_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: System prompt for implicit task validation. [PITH_FULL_IMAGE:figures/full_fig_p026_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: User prompt for implicit task validation. [PITH_FULL_IMAGE:figures/full_fig_p026_21.png] view at source ↗
read the original abstract

A core capability towards general embodied intelligence lies in localizing task-relevant objects from an egocentric perspective, formulated as Spatio-Temporal Video Grounding (STVG). Despite recent progress, existing STVG studies remain largely confined to object-centric and descriptive instructions, neglecting the task-oriented reasoning that is crucial for embodied agents to accomplish goal-directed interactions. To bridge this gap, we introduce \textbf{ToG-Bench}, the first task-oriented spatio-temporal video grounding benchmark for egocentric videos. ToG-Bench is characterized by three key features: (1) \textbf{Task-oriented Grounding}, which requires identifying and localizing objects based on intended tasks rather than straightforward descriptions; (2) \textbf{Explicit-Implicit Dual Grounding}, where target objects can be either explicitly mentioned or implicitly inferred by contextual reasoning; (3) \textbf{One-to-Many Grounding}, where a single instruction may correspond to multiple objects involved in task execution. Built upon videos sourced from ScanNet, ToG-Bench comprises 100 annotated clips with 2,704 task-oriented grounding instructions, constructed via a semi-automated pipeline that combines foundation model annotation and human refinement. In addition, we introduce a set of task-level evaluation metrics tailored for multi-object and explicit-implicit object grounding, and systematically benchmark seven state-of-the-art MLLMs. Extensive experiments reveal the intrinsic challenges of task-oriented STVG and substantial performance gaps across explicit-implicit and multi-object grounding, highlighting the difficulty of bridging perception and interaction in embodied scenarios. Data and code will be released at: \href{https://github.com/qaxuDev/ToG-Bench}{https://github.com/qaxuDev/ToG-Bench}..

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

1 major / 2 minor

Summary. The paper introduces ToG-Bench, the first task-oriented spatio-temporal video grounding benchmark for egocentric videos. It is built from 100 ScanNet clips yielding 2,704 instructions via a semi-automated pipeline that combines foundation-model pre-annotation with human refinement. The benchmark is defined by three features: task-oriented grounding (objects localized by intended task rather than description), explicit-implicit dual grounding, and one-to-many grounding. The authors propose tailored task-level metrics for multi-object and explicit-implicit cases and evaluate seven MLLMs, reporting performance gaps that highlight challenges in bridging perception and interaction.

Significance. If the annotations faithfully realize the claimed distinctions, ToG-Bench would fill a clear gap in existing STVG benchmarks by emphasizing goal-directed reasoning required for embodied agents. The systematic MLLM evaluation and public data/code release would provide a useful testbed and improve reproducibility. The work's impact hinges on whether the semi-automated construction reliably produces the explicit-implicit and one-to-many properties rather than surface cues.

major comments (1)
  1. [§3 (Dataset Construction)] §3 (Dataset Construction): The central claim that ToG-Bench exhibits task-oriented, explicit-implicit dual, and one-to-many grounding rests entirely on the semi-automated pipeline. No inter-annotator agreement, no ablation removing the foundation-model stage, and no quantitative check that implicit cases require genuine contextual inference (versus surface cues) are reported. This is load-bearing; without such validation the novelty of the benchmark, the tailored metrics, and the reported MLLM gaps all rest on untested labeling assumptions.
minor comments (2)
  1. [Abstract] Abstract: The statement that experiments 'reveal the intrinsic challenges of task-oriented STVG and substantial performance gaps' would be strengthened by a single concrete quantitative example (e.g., a metric delta between explicit and implicit subsets).
  2. [§4 (Experiments)] §4 (Experiments): The task-level evaluation metrics for multi-object and explicit-implicit grounding should include explicit formulas or pseudocode to ensure they can be reproduced from the released annotations.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback and for recognizing the potential of ToG-Bench to address gaps in task-oriented spatio-temporal grounding. We address the major comment on dataset validation point by point below, with planned revisions.

read point-by-point responses

  1. Referee: The central claim that ToG-Bench exhibits task-oriented, explicit-implicit dual, and one-to-many grounding rests entirely on the semi-automated pipeline. No inter-annotator agreement, no ablation removing the foundation-model stage, and no quantitative check that implicit cases require genuine contextual inference (versus surface cues) are reported. This is load-bearing; without such validation the novelty of the benchmark, the tailored metrics, and the reported MLLM gaps all rest on untested labeling assumptions.

    Authors: We agree that rigorous validation of the semi-automated pipeline is necessary to support the benchmark's core distinctions. The manuscript describes the pipeline of foundation-model pre-annotation followed by human refinement but does not include the requested quantitative checks. In the revised version we will add: (1) inter-annotator agreement computed on a stratified subset of instructions to quantify labeling consistency; (2) an ablation comparing annotation quality and feature coverage with versus without the foundation-model stage; and (3) a quantitative analysis of implicit cases, for example by measuring how often implicit targets cannot be resolved from single frames or isolated object descriptions and by reporting the distribution of required contextual inference steps. These additions will directly substantiate the task-oriented, explicit-implicit, and one-to-many properties as well as the tailored metrics. revision: yes

Circularity Check

0 steps flagged

No circularity: benchmark built from external ScanNet videos via new annotations

full rationale

The paper introduces ToG-Bench by sourcing 100 clips from the external ScanNet dataset and constructing 2,704 instructions through a semi-automated pipeline of foundation-model pre-annotation followed by human refinement. No mathematical derivations, fitted parameters, predictions, or first-principles results appear in the provided text. The three key features (task-oriented grounding, explicit-implicit dual grounding, one-to-many grounding) are defined upfront and then realized by the annotation process rather than being derived from or equivalent to any internal fitted quantities or self-citations. The work is therefore self-contained against external benchmarks and data sources, with no load-bearing steps that reduce to the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that ScanNet indoor videos are representative of egocentric task scenarios and that human-refined foundation-model annotations faithfully encode task-oriented intent.

axioms (1)
  • domain assumption ScanNet videos provide suitable egocentric video data for task-oriented grounding.
    The benchmark is built upon videos sourced from ScanNet.

pith-pipeline@v0.9.0 · 5640 in / 1269 out tokens · 42550 ms · 2026-05-17T02:45:09.442452+00:00 · methodology

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Forward citations

Cited by 1 Pith paper

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    EgoSound is a new benchmark with 7315 QA pairs across seven tasks to evaluate egocentric sound understanding in multimodal large language models.

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