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VTG-LLM: Integrating Timestamp Knowledge into Video LLMs for Enhanced Video Temporal Grounding
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Proposes VTG-LLM, a video LLM with sequence-time embeddings, absolute-time tokens initialized from digit embeddings, and slot-based token compression, plus the re-annotated VTG-IT-120K instruction dataset for zero-shot and fine-tuned video temporal grounding.
Problem
Video LLMs struggle to pinpoint timestamps accurately, which limits their performance on video temporal grounding (VTG) tasks, and VTG needs many sampled frames that exceed LLM context budgets; existing VTG instruction datasets are also low quality or task-imbalanced.
Approach
Combine absolute-time embeddings added to visual tokens (zero-initialized, with test-time interpolation), a six-token formatted absolute-time vocabulary initialized from digit embeddings, and a learned slot dispatcher that compresses visual tokens to a fixed 256 slots so more frames can be sampled.
Key results
- Zero-shot on Charades-STA, VTG-LLM reaches R@1 (IOU=0.5) 33.8 and R@1 (IOU=0.7) 15.7, above TimeChat's 32.2 and 13.4.
- On Youcook2 zero-shot, VTG-LLM gets F1 17.5 and CIDEr 5.0 versus TimeChat's 12.6 F1 and 3.4 CIDEr.
- Ablations show random initialization of time embeddings or time tokens drops Charades-STA R@1 (IOU=0.5) to 21.4 and 15.6, versus 33.8 for the full model.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add absolute-time embedding to sequence embedding (ablation) | VTG-LLM · YouCook2 | F1 Score: 17.2 -> 17.5 | Helped |
| add absolute-time embedding to sequence embedding (ablation) | VTG-LLM · Charades-STA | R@1 (IoU=0.5): 33.6 -> 33.8 | Helped |
| randomly initialize the absolute-time embedding (ablation) | VTG-LLM · Charades-STA | R@1 (IoU=0.5): 33.8 -> 21.4 | Hurt |
| add special time tokens (ablation) | VTG-LLM · Charades-STA | R@1 (IoU=0.5): 29.5 -> 33.8 | Helped |
| add special time tokens (ablation) | VTG-LLM · YouCook2 | CIDEr: 5.5 -> 5.0 | Hurt |
| format all timestamps to six time tokens (ablation) | VTG-LLM with time tokens · Charades-STA | R@1 (IoU=0.5): 27.0 -> 33.8 | Helped |
| initialize time-token embeddings and prediction head from digit tokens (ablation) | VTG-LLM with time tokens · Charades-STA | R@1 (IoU=0.5): 15.6 -> 33.8 | Helped |
| use slot-based compression instead of entropy sampling (ablation) | VTG-LLM · Charades-STA | R@1 (IoU=0.5): 21.1 -> 33.8 | Helped |
| use slot-based compression instead of diverse sampling (ablation) | VTG-LLM · Charades-STA | R@1 (IoU=0.5): 22.4 -> 33.8 | Helped |
| use slot-based compression instead of cross-attention compression (ablation) | VTG-LLM · Charades-STA | R@1 (IoU=0.5): 19.9 -> 33.8 | Helped |
| reduce decoding temperature from τ = 1.0 to τ = 0.1 (ablation) | VTG-LLM · YouCook2 | F1 Score: 17.5 -> 18.4 | Helped |
| remove test-time interpolation (ablation) | VTG-LLM · YouCook2 | F1 Score: 17.5 -> 17.0 | Hurt |
Surprising findings
- Special time tokens improve timestamp accuracy but slightly reduce caption quality (SODA_c and CIDEr drop), indicating a trade-off.
- Cross-attention compression performs worse than simple sampling-based compression baselines.
Limitations
- Focuses mainly on timestamp accuracy; salient score precision and caption quality are not improved.
- Audio components of videos are not used.
- Slot compression evaluated on a limited set of tasks.
Experimental setup
Datasets: VTG-IT-120K TimeIT Valley HiREST QuerYD DiDeMo COIN ActivityNet Captions TVSum SumMe YT-Temporal-180M
Benchmarks: Youcook2 Charades-STA QVHighlights ActivityNet Captions
Metrics: SODA_c CIDEr F1 Score R@1 IOU=0.5 R@1 IOU=0.7 mAP HIT@1 METEOR
Baselines: Valley VideoChat Video-ChatGPT Video-LLaMA TimeChat VTimeLLM Momentor HawkEye
Builds on: TimeIT Video-LLaMA InstructBLIP SoftMoE
Closest papers by meaning
- TRACE: Temporal Grounding Video LLM via Causal Event Modeling (ICLR 2025 · similarity 0.93)
- Number it: Temporal Grounding Videos like Flipping Manga (CVPR 2025 · similarity 0.90)
- VTimeLLM: Empower LLM to Grasp Video Moments (CVPR 2024 · similarity 0.89)
- Empower Words: DualGround for Structured Phrase and Sentence-Level Temporal Grounding (NeurIPS 2025 · similarity 0.89)
- ReVisionLLM: Recursive Vision-Language Model for Temporal Grounding in Hour-Long Videos (CVPR 2025 · similarity 0.89)
- TimeSuite: Improving MLLMs for Long Video Understanding via Grounded Tuning (ICLR 2025 · similarity 0.88)
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