ToTG-Bench: Task-Oriented Temporal Grounding Evaluation

• ToTG-Bench is a benchmark for localizing implicit task intervals in long videos via multi-stage annotation.
• It covers 12 task types and 35 video categories, ensuring diverse real-world applicability and robust evaluation.
• TimeScope's coarse-to-fine search method enables efficient, superior temporal localization in complex video settings.

Task-oriented Temporal Grounding Benchmark (ToTG-Bench) is a comprehensive evaluation suite for the novel problem of localizing critical temporal intervals in long videos based on high-level task descriptions. Unlike conventional temporal grounding, which grounds events explicitly stated in the query, ToTG-Bench targets settings where the query is a task or indirect instruction and the grounding target must be inferred through higher-level reasoning. This benchmark is structured to reflect the increased complexity encountered in real-world long-video scenarios and to drive research on methods that can handle implicit task semantics, distractor content, and generalization across multiple domains (Liu et al., 30 Sep 2025).

1. Definition and Problem Scope

Task-oriented Temporal Grounding (ToTG) is defined as the task of localizing a time interval $[t_s, t_e]$ within an untrimmed video $V = \{f_1, f_2, \ldots, f_T\}$ given a "task-oriented" natural language query $q$. Unlike traditional temporal grounding, queries in ToTG do not provide explicit event cues. For example, instead of “find the moment when a child is playing with a ball,” a ToTG query would be “why does the boy look happy when he comes home?,” which requires reasoning to select the interval (e.g., the segment showing the boy receiving a gift).

ToTG-Bench formalizes and evaluates this challenging setting, focusing on naturalistic queries that necessitate semantic and temporal reasoning over long and information-dense videos.

2. Benchmark Construction and Dataset Design

ToTG-Bench comprises the following design elements:

• Data Diversity: The benchmark includes 12 distinct task types (e.g., action reasoning, OCR perception, temporal reasoning) across 35 video categories. Sample durations range from short clips to nearly one hour, ensuring coverage of a wide temporal scale and multiple real-world domains.
• Annotation Pipeline:
  • Initial candidate samples are sourced from four established long-video understanding datasets.
  • Task Type Filtering eliminates non-ToTG-compatible samples (e.g., summarization or multilingual queries).
  • Uniqueness Filtering enforces that each query is mapped to a single, clearly-identified target segment. This is achieved by segmenting videos, running a temporal grounding model, and conducting manual curation.
  • Information Validation uses an advanced MLLM to ensure that each selected interval contains all information necessary to answer the corresponding task-oriented query.

The pipeline results in a balanced, rigorously annotated evaluation dataset with high discriminative power for both model ability and robustness.

Table: ToTG-Bench Dataset Overview

Aspect	Specification
Task Types	12 (action reasoning, OCR, temporal, etc.)
Video Categories	35
Duration Range	Short clips to ~1 hour
Query Types	Implicit, task-oriented, multi-domain
Annotation Validation	Automated model + manual multi-stage checks

3. Evaluation Protocol and Metrics

ToTG-Bench employs standard temporal grounding metrics adapted to the task-oriented setup:

• Recall@1 (R@1) is computed at multiple Intersection-over-Union (IoU) thresholds, typically 0.3, 0.5, and 0.7. For a sample $i$, $\text{IoU}_i$ is calculated as

$$\text{IoU}_i = \frac{|T^{\text{pred}}_i \cap T^{\text{gt}}_i|}{|T^{\text{pred}}_i \cup T^{\text{gt}}_i|}$$

where $T^{\text{pred}}_i$ and $T^{\text{gt}}_i$ are the predicted and ground truth intervals.

• Mean IoU (mIoU) is computed as

$$\text{mIoU} = \frac{1}{N} \sum_{i=1}^N \text{IoU}_i$$

with $N$ as the number of test instances.

These metrics support nuanced measurement across variable video lengths and task complexities.

4. Unique Challenges and Benchmark Significance

ToTG-Bench is designed with several intentional challenges beyond those in traditional event grounding:

• Implicit Queries: The relevant segment is not specified verbatim in the query. Models must reason about the task and infer which interval fulfills the implicit information requirement.
• Long Video Distractions: Videos contain substantial irrelevant or distractor content, requiring models to filter signals from extensive temporal noise.
• Generalizability: The task distribution and video domains are broad, compelling models to go beyond domain-specific cues or overfitting seen in prior event-centric datasets.
• Task Diversity: Inclusion of scenarios such as action reasoning, OCR, and temporal causality increases the annotation and modeling complexity, promoting development of more broadly capable temporal reasoning engines.

5. The TimeScope Framework

The TimeScope framework is specifically designed to meet ToTG’s requirements and demonstrates substantial improvements on ToTG-Bench (Liu et al., 30 Sep 2025). Its methodology is defined by progressive reasoning:

• Coarse-to-Fine Search: Rather than processing all video frames directly, TimeScope encodes the entire sequence to produce fine-grained key-value (KV) representations ("KV_fine"). These are averaged (pooled) to produce a compact "KV_coarse," yielding a hypothesized coarse temporal window $\hat{W}$ that likely contains the answer interval.
• Selective Loading: Fine-grained processing is restricted to $\hat{W}$ by reloading only the corresponding segment of KV states, allowing computationally efficient yet precise localization.
• Training Regime: TimeScope is trained in two stages. First, the model is directly supervised to predict intervals on ToTG-Pile—a curated dataset that mixes standard grounding samples and new task-oriented samples. Then, heavy temporal augmentations (cropping, shifting, scaling of training intervals) are applied to reinforce progressive attention and generalization.

This approach enables TimeScope to outperform prior temporal grounding baselines and multi-modal LLMs, especially as video duration increases and when queries are semantically complex.

6. Experimental Results and Insights

Extensive benchmarking demonstrates the following:

• Superior Localization: TimeScope achieves higher R@[email protected] and mIoU than comparative methods on both traditional benchmarks (e.g., Charades-STA, ActivityNet) and challenging long-video scenarios typical in ToTG-Bench.
• Robustness to Video Length: Performance advantages are most pronounced for medium and long-duration videos, with the progressive windowing mechanism preventing degradation from irrelevant content outside $\hat{W}$.
• Query Centering Bias: TimeScope’s performance is less affected by the position of the target segment within the video, indicating reduced bias compared to prior models.
• Ablative Evidence: TimeScope’s gain is closely attributed to its progressive search mechanism, as ablation removing coarse-to-fine reasoning results in substantial performance drops, especially with longer input sequences.

7. Future Directions and Broader Impact

ToTG-Bench establishes a rigorous protocol and resource for the community to develop and evaluate task-oriented temporal reasoning in video understanding. Key directions include:

• Enhancing the semantic modeling of tasks to improve reasoning in the face of implicit queries.
• Scaling the approach to accommodate increasingly long videos and more complex task types.
• Addressing annotation and evaluation in the presence of overlapping or multi-target intervals as encountered in naturalistic settings.

A plausible implication is that progress on ToTG-Bench may directly translate to improved real-world applications in video QA, narrative understanding, surveillance review, and instructional task-following, where extracting the most relevant moment requires integrating high-level goals and temporal inference.

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ToTG-Bench thus fills a critical gap in video understanding research by targeting the intersection of task-driven semantic reasoning and temporal localization within long, diverse, and realistic video data (Liu et al., 30 Sep 2025).