VideoITG: Instructed Temporal Grounding

• The paper introduces VideoITG by formalizing event prediction as structured outputs (timestamp, saliency, caption) from instruction-conditioned video frames.
• It employs diverse architectures—from frame selection to text-generation methods—enabling precise localization across supervised and training-free regimes.
• VideoITG shows improved grounding metrics and efficiency, underlining its potential for enhanced instruction-aware video understanding in research applications.

Searching arXiv for papers on instructed temporal grounding for videos and closely related VTG/Video-LLM grounding work. arXiv_search(query="Instructed Temporal Grounding for Videos VideoITG temporal video grounding video LLM", max_results=10, sort_by="relevance") Searching for the specific core paper and adjacent methods to ground the article in recent arXiv literature. arXiv_search(query="TimeExpert An Expert-Guided Video LLM for Video Temporal Grounding", max_results=5, sort_by="relevance") Searching for the VideoITG framework paper and universal temporal grounding models for additional corroboration. arXiv_search(query="VideoITG Multimodal Video Understanding with Instructed Temporal Grounding", max_results=5, sort_by="relevance") Searching for UniTime and Tempo-R0 as representative 2025 systems for universal and reinforcement-based temporal grounding. arXiv_search(query="Universal Video Temporal Grounding with Generative Multi-modal LLMs UniTime Tempo-R0", max_results=10, sort_by="relevance") Instructed Temporal Grounding for Videos (VideoITG) denotes instruction-conditioned localization of temporally relevant evidence in video. In current literature, the term spans closely related formulations in which a model receives a video and a natural-language instruction, question, or description, then returns grounded temporal evidence rather than an unconstrained verbal response. That evidence may be a single interval, a sequence of timestamped events with saliency and captions, a set of relevant clips, or a selected subset of frames for downstream reasoning. Recent work has therefore positioned VideoITG at the intersection of video temporal grounding, moment retrieval, dense video captioning, highlight detection, long-video question answering, and instruction-aware frame selection (Yang et al., 3 Aug 2025, Wang et al., 17 Jul 2025, Li et al., 23 Jun 2025).

1. Task definitions and formal scope

A central formalization treats VideoITG as structured event prediction. In "TimeExpert: An Expert-Guided Video LLM for Video Temporal Grounding" (Yang et al., 3 Aug 2025), given instruction $\mathcal{I}$ and frames $\mathcal{F}$, the output is a sequence of events

$$\mathcal{R} = \{\epsilon_1, \epsilon_2, \dots, \epsilon_M\} = \{(t_m, s_m, c_m) \mid 1 \leq m \leq M\},$$

where each event $\epsilon_m$ contains a timestamp $t_m$, a saliency score $s_m$, and a caption $c_m$. The same work also gives the causal factorization

$$\mathcal{P}(\epsilon_m \mid \epsilon_{1:m-1}, \mathcal{I}, \mathcal{F}) = \mathcal{P}(t_m, s_m, c_m \mid \epsilon_{1:m-1}, \mathcal{I}, \mathcal{F}),$$

making the task explicitly event-centric rather than treating it as plain text generation.

Other formulations specialize this general idea to a single grounded interval. "Grounding-Prompter: Prompting LLM with Multimodal Information for Temporal Sentence Grounding in Long Videos" (Chen et al., 2023) defines temporal sentence grounding as predicting start-and-end timestamps $(\hat{t}_s,\hat{t}_e)$ for a query $Q$ over a video $\mathcal{F}$0. "Tempo-R0: A Video-MLLM for Temporal Video Grounding through Efficient Temporal Sensing Reinforcement Learning" (Yue et al., 7 Jul 2025) similarly frames the output as intervals $\mathcal{F}$1, while "Reinforcing Temporal Answer Grounding in Instructional Video via Candidate-Aware Causal Reasoning" (Qi et al., 7 Jun 2026) formulates temporal answer grounding as estimating $\mathcal{F}$2. These interval-based settings emphasize precise localization, but they are narrower than the structured event-sequence view.

VideoITG also extends beyond single-query, single-span settings. "Temporally Grounding Instructional Diagrams in Unconstrained Videos" (Zhang et al., 2024) studies simultaneous grounding of a sequence of instructional diagrams, with each diagram assigned a normalized span $\mathcal{F}$3. This establishes that instructed temporal grounding need not be confined to isolated queries; it may instead be a structured prediction problem over multiple related instructions with soft temporal-order and overlap constraints.

2. Annotation pipelines, datasets, and supervision regimes

A defining development in VideoITG is the move from generic or unsupervised sampling toward instruction-guided supervision. "VideoITG: Multimodal Video Understanding with Instructed Temporal Grounding" (Wang et al., 17 Jul 2025) introduces the VidThinker pipeline and the VideoITG-40K dataset. VidThinker explicitly imitates a human coarse-to-fine process: it first generates instruction-conditioned clip-level captions for 5-second clips, then retrieves relevant segments through instruction-guided reasoning, and finally performs fine-grained frame selection. VideoITG-40K contains 40,000 videos and 500,000 instructed temporal grounding annotations, sourced from the LLaVA-Video dataset, with videos averaging about 120 seconds and durations distributed across 30–60 seconds, 1–2 minutes, and 2–3 minutes. The same work also classifies instructions into Semantic-only, Motion-only, Semantic + Motion, and Non-clues categories, tying annotation policy directly to task type.

A second supervision paradigm is large-scale temporal grounding pretraining for text-to-text models. "HawkEye: Training Video-Text LLMs for Grounding Text in Videos" (Wang et al., 2024) constructs InternVid-G from 100k videos sampled from InternVid-10M-FLT and reports 715,489 segment-level captions and negative spans. Its supervision is built around positive spans and carefully mined negative spans that exclude semantically similar distractors. This yields training data specifically designed for temporal localization rather than only for general video-language alignment.

Long-video prompting methods use a different supervision model, often textualized and sometimes training-free. Grounding-Prompter evaluates on VidChapters-mini, a long-video benchmark constructed by randomly selecting 3 chapter annotations per video from the test split for videos of duration 13–15 minutes, yielding a final test set size of 1830 query-moment pairs (Chen et al., 2023). The method textualizes speech transcriptions and visual captions with timestamps, showing that some VideoITG settings can be realized without end-to-end video-token training.

Resource	Scale	Distinguishing property
VideoITG-40K	40,000 videos; 500,000 annotations	instruction-guided clips and frame-level evidence
InternVid-G	100k videos; 715,489 captions/spans	segment-level captions and negative spans
VidChapters-mini	1830 query-moment pairs	long-video, speech-rich evaluation

These dataset choices indicate three distinct supervision regimes: direct instruction-aware evidence annotation, large-scale synthetic grounding corpora for LLM adaptation, and textualized multimodal prompting over long videos. This suggests that VideoITG is not tied to a single annotation ontology, but to the shared requirement that temporal evidence be explicitly recoverable under an instruction.

3. Architectural patterns and grounding mechanisms

Current VideoITG systems fall into several architectural families. One family treats grounding as instruction-aware frame selection for downstream multimodal understanding. The VideoITG model in (Wang et al., 17 Jul 2025) is a plug-and-play selector that operates over visual features $\mathcal{F}$4, predicts relevant frame indices $\mathcal{F}$5, and passes the selected evidence to a Video-LLM. It proposes three variants: text-generation-based classification, anchor-based classification with causal attention where the per-frame anchor is $\mathcal{F}$6, and pooling-based classification with full attention. The paper reports that the pooling-based variant with full attention is the best-performing architecture.

A second family retains a pure language-generation interface. HawkEye never directly predicts raw timestamps during training; instead, it maps segments to one of four textual classes—“At the beginning of the video.”, “In the middle of the video.”, “At the end of the video.”, or “Throughout the entire video.”—and then uses recursive grounding at inference to narrow the interval (Wang et al., 2024). The method therefore converts temporal localization into a stable, fully text-to-text decision process rather than immediate numeric generation.

A third family makes temporal structure explicit within the decoder. TimeExpert replaces a single shared decoder with a Mixture-of-Experts decoder tailored to VTG and separates time, score, and text through independent encoders and decoding heads (Yang et al., 3 Aug 2025). In its vanilla MoE form,

$\mathcal{F}$7

and routing is then modified by token similarity and task-specific activation statistics. Task-aware dynamic gating and token-adaptive routing send timestamps, saliency scores, and caption tokens to specialized experts; if underrepresented task tokens activate no expert, the model creates a new expert with

$\mathcal{F}$8

and removes experts with low activation according to $\mathcal{F}$9. The training procedure is staged into task module pretraining, MoE decoder pretraining, and supervised fine-tuning, with cross-entropy, z-loss, and a task-aware auxiliary loss.

A fourth family embeds time directly into the multimodal token stream. UniTime interleaves timestamp tokens with video tokens so that a generative MLLM can read and emit timestamps in textual form (Li et al., 23 Jun 2025). For short-video grounding, each timestamp string $$\mathcal{R} = \{\epsilon_1, \epsilon_2, \dots, \epsilon_M\} = \{(t_m, s_m, c_m) \mid 1 \leq m \leq M\},$$0 “timestamp: $$\mathcal{R} = \{\epsilon_1, \epsilon_2, \dots, \epsilon_M\} = \{(t_m, s_m, c_m) \mid 1 \leq m \leq M\},$$1 seconds” is tokenized and inserted before the visual tokens of frame $$\mathcal{R} = \{\epsilon_1, \epsilon_2, \dots, \epsilon_M\} = \{(t_m, s_m, c_m) \mid 1 \leq m \leq M\},$$2. For longer videos, the same paper uses segment-level timestamps, adaptive frame scaling with frame sampling at 2 fps, $$\mathcal{R} = \{\epsilon_1, \epsilon_2, \dots, \epsilon_M\} = \{(t_m, s_m, c_m) \mid 1 \leq m \leq M\},$$3, $$\mathcal{R} = \{\epsilon_1, \epsilon_2, \dots, \epsilon_M\} = \{(t_m, s_m, c_m) \mid 1 \leq m \leq M\},$$4, and a 16,384-token cap, thereby coupling explicit temporal anchors with multi-granular inference.

A fifth family emphasizes temporal perception and refusal-aware reasoning. Tempo-R0 combines Self-adaptive Attention Allocation (SAA), Explicit Timestamp Alignment (ETA), and Partial Irrelevance Refusing-based Group Relative Policy Optimization (PIR-GRPO) (Yue et al., 7 Jul 2025). SAA allocates more visual detail to frames with higher OT-based content variation, ETA zero-pads and text-encodes timestamp strings as an independent modality, and PIR-GRPO rewards both correct localization and correct refusal on irrelevant video-query pairs. This is a notable departure from conventional grounding training, which ordinarily assumes every query should be matched to an interval.

Prompt-centric long-video methods remain important as a sixth family. Grounding-Prompter textualizes ASR transcripts and BLIP captions, then uses Boundary-Perceptive Prompting with Multiscale Denoising Chain-of-Thought, validity principles, and one-shot ICL to produce JSON-formatted timestamp predictions (Chen et al., 2023). CACR adopts a different two-stage variant: Visual-Language Pre-training based Candidate Selection (VBCS) first generates Top-$$\mathcal{R} = \{\epsilon_1, \epsilon_2, \dots, \epsilon_M\} = \{(t_m, s_m, c_m) \mid 1 \leq m \leq M\},$$5 candidate segments, then a temporal logic reasoning module built on Qwen2.5-VL-7B-Instruct verifies, refines, or rejects candidates under a GRPO objective with an explicit rejection reward (Qi et al., 7 Jun 2026).

4. Benchmarks, metrics, and empirical landscape

VideoITG research is evaluated across several task families. TimeExpert explicitly targets Dense Video Captioning on YouCook2 and ActivityNet Captions, Moment Retrieval on Charades-STA and ActivityNet Captions, and Video Highlight Detection on QVHighlights, using metrics such as SODA$$\mathcal{R} = \{\epsilon_1, \epsilon_2, \dots, \epsilon_M\} = \{(t_m, s_m, c_m) \mid 1 \leq m \leq M\},$$6, CIDEr, F1, METEOR, R@1 at IoU thresholds 0.5/0.7, mIoU, mAP, and HIT@1 (Yang et al., 3 Aug 2025). This breadth is significant because it frames instructed grounding as a unified structured-event problem rather than a benchmark-specific interval-localization task.

HawkEye reports best or near-best zero-shot temporal grounding among compared video-text LLMs on Charades-STA and ActivityNet-Captions while using only 12 frames as input (Wang et al., 2024). On Charades-STA, its zero-shot result is 33.7 mIoU / 50.6 R@IoU$$\mathcal{R} = \{\epsilon_1, \epsilon_2, \dots, \epsilon_M\} = \{(t_m, s_m, c_m) \mid 1 \leq m \leq M\},$$7 / 31.4 R@IoU$$\mathcal{R} = \{\epsilon_1, \epsilon_2, \dots, \epsilon_M\} = \{(t_m, s_m, c_m) \mid 1 \leq m \leq M\},$$8 / 14.5 R@IoU$$\mathcal{R} = \{\epsilon_1, \epsilon_2, \dots, \epsilon_M\} = \{(t_m, s_m, c_m) \mid 1 \leq m \leq M\},$$9, and after fine-tuning it reaches 49.3 / 72.5 / 58.3 / 28.8. On ActivityNet-Captions, the reported zero-shot result is 32.7 / 49.1 / 29.3 / 10.7.

Training-free prompting methods have also been competitive in long-video settings. Grounding-Prompter reports $\epsilon_m$0, $\epsilon_m$1, $\epsilon_m$2, $\epsilon_m$3, $\epsilon_m$4, $\epsilon_m$5, $\epsilon_m$6, $\epsilon_m$7, $\epsilon_m$8, and $\epsilon_m$9 on VidChapters-mini, and is described as state-of-the-art in training-free settings (Chen et al., 2023).

Reinforcement-based and universal MLLM models have produced stronger numbers on standard TVG benchmarks. Tempo-R0 reports on QvHighlights $t_m$0, $t_m$1, and $t_m$2, and on corrected QvHighlights $t_m$3, $t_m$4, and $t_m$5 (Yue et al., 7 Jul 2025). UniTime-SP reports dataset-specific fine-tuned results of 24.79 / 16.83 / 17.25 on Ego4D-NLQ, 61.18 / 48.31 / 45.02 on TaCoS, 74.33 / 53.71 on Charades-STA, 54.81 / 36.62 on ANet-Captions, and 77.76 / 63.29 on QVHighlights, while UniTime-Zero substantially improves over prior zero-shot MLLM grounding baselines across the same task family (Li et al., 23 Jun 2025).

Instruction-aware frame selection has shown direct benefits for downstream video understanding. VideoITG reports consistent gains over uniform sampling on LongVideoBench, MLVU, VideoMME, and CG-Bench; for InternVL2.5-8B, the reported scores are 58.3 $t_m$6 61.9, 66.4 $t_m$7 75.0, 63.3 $t_m$8 67.3, and 37.7 $t_m$9 46.7, with an average improvement of +5.6% (Wang et al., 17 Jul 2025). The same work notes that InternVL2.5-8B + VideoITG surpasses the baseline InternVL2.5-26B under uniform sampling.

System	Setting	Reported outcome
Grounding-Prompter	training-free long-video TSG	$s_m$0, $s_m$1
HawkEye	zero-shot / fine-tuned Charades-STA	33.7 mIoU / 49.3 mIoU
Tempo-R0	QvHighlights / cQvH	78.52/65.23/54.50 and 84.65/72.39/61.47
UniTime-SP	dataset-specific VTG	leads across Ego4D-NLQ, TaCoS, Charades-STA, ANet, QVH
VideoITG	frame selection for Video-LLMs	average +5.6% on InternVL2.5-8B

Long-video efficiency remains a parallel concern. SOONet, although not framed as a Video-LLM, is directly relevant because it grounds query-conditioned moments in hours-long video with one-time network execution, reporting 14.6$s_m$2, 58.5$s_m$3, and 102.8$s_m$4 speedups over CLIP, VLG-Net, and 2D-TAN, respectively, in long-video settings (Pan et al., 2023). This provides an important non-LLM baseline for the computational demands that VideoITG systems inherit.

5. Relation to adjacent tasks and common misconceptions

One common misconception is that VideoITG is simply moment retrieval with a different name. The literature does not support that reduction. TimeExpert explicitly models outputs as event tuples $s_m$5, so the target can include temporal boundaries, saliency, and textual description simultaneously (Yang et al., 3 Aug 2025). VideoITG, in the instruction-aware frame-selection sense, may return relevant clips and final frame-level evidence rather than a single interval (Wang et al., 17 Jul 2025). The instructional-diagram setting grounds multiple queries simultaneously and treats overlap suppression and temporal order as first-class concerns (Zhang et al., 2024). This suggests that moment retrieval is only one subcase within a broader VideoITG design space.

A second misconception is that all VideoITG systems are end-to-end multimodal transformers operating directly on video tokens. Grounding-Prompter is training-free and relies on Whisper-based ASR, PySceneDetect, BLIP captions, and GPT-3.5-turbo-16k at inference (Chen et al., 2023). CACR uses a two-stage pipeline with VBCS candidate generation, semantic aids such as Subtitle$s_m$6 and Pre-answer, and a first-valid sequential verification rule (Qi et al., 7 Jun 2026). Mamba-VMR augments the query with subtitle-selected context and generated short videos from CogVideoX, then grounds the target video with a controlled Mamba backbone rather than a standard Transformer (Sun et al., 23 Mar 2026). VideoITG therefore includes native end-to-end models, modular retriever-reasoners, and prompt-engineered systems.

A third misconception is that coarse textual temporal labels are inherently incompatible with precise localization. HawkEye directly contradicts this assumption by using the four-way coarse representation only as an intermediate output space and applying recursive grounding to recover exact spans (Wang et al., 2024). Its own limitation is narrower: the paper notes that the coarse scheme is not ideal for representing multiple disjoint segments at once, so tasks like highlight detection would need additional representation ideas.

A fourth misconception concerns terminology such as “causal reasoning.” CACR explicitly states that its “causal reasoning” is operational rather than strict Pearl-style intervention (Qi et al., 7 Jun 2026). The phrase refers to a structured flow of candidate generation, context enrichment, hypothesis verification, and answer decision or rejection, not to formal structural causal models. That clarification matters because VideoITG has increasingly absorbed reasoning language from LLM research, but the underlying methodological commitments differ substantially across papers.

6. Limitations, unresolved problems, and likely directions

Several limitations recur across the literature. VideoITG’s current deployment uses two separate modules at inference—VideoITG for frame selection and a separate Video-LLM for answering—so there is no end-to-end gradient flow between selection and answer quality (Wang et al., 17 Jul 2025). HawkEye’s coarse scheme is not ideal for multiple disjoint segments (Wang et al., 2024). TimeExpert’s ablations show that removing token-adaptive routing hurts performance, removing separate encoders and heads breaks instruction following, and removing the task-dependent loss reduces accuracy, indicating that specialization is beneficial but also architecturally delicate (Yang et al., 3 Aug 2025).

Other systems expose different trade-offs. Tempo-R0’s setup implies dependence on heuristic frame scoring and thresholding in SAA, careful timestamp formatting in ETA, and a tuned ratio of irrelevant pairs in PIR-GRPO; the paper reports that around 10% irrelevant pairs gives the best average performance, while higher ratios improve refusal recall but can reduce grounding performance on relevant samples (Yue et al., 7 Jul 2025). CACR depends on candidate quality, subtitle availability, and external LLM summaries, and it identifies GRPO verification as the main latency cost (Qi et al., 7 Jun 2026). Mamba-VMR relies on LLM query decomposition, subtitle matching, and offline auxiliary video generation, even though it reports average latency of about 1.2 seconds per query-video pair and substantially lower memory than Transformer counterparts on long sequences (Sun et al., 23 Mar 2026). UniTime’s qualitative analysis notes failure cases involving repeated objects or events and also points to incomplete annotations and incorrect labels on ANet-Captions (Li et al., 23 Jun 2025).

These limitations collectively suggest a consistent research trajectory. One direction is stronger explicit time modeling, seen in timestamp interleaving, explicit timestamp alignment, and structured time/score/text factorization. Another is search-space compression, seen in coarse clip retrieval, candidate screening, recursive narrowing, and adaptive frame allocation. A third is instruction-aware evidence control, seen in frame selection policies that vary with Semantic-only, Motion-only, Semantic + Motion, or Non-clues instructions. A plausible implication is that future VideoITG systems will increasingly combine explicit temporal tokens, candidate-space pruning, rejection or abstention mechanisms, and tighter coupling between evidence selection and downstream answer generation.