Foresee-to-Ground: From Predictive Temporal Perception to Evidence-Driven Reasoning for Video Temporal Grounding

2026-05-22 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision · Depth: Expert, extended

Summary

Foresee-to-Ground (F2G) is a novel framework designed to improve Video Temporal Grounding (VTG) in Video-LLMs, addressing the brittle numerics and inconsistent boundaries often resulting from direct timestamp generation. F2G reformulates VTG as a verifiable "Identify-then-Measure" problem, integrating Predictive Temporal Perception with Evidence-Driven Reasoning. It constructs a video-wide evidence pool of candidate event segments using boundary-sensitive temporal representations, which are then presented to the LLM as citable evidence units. This approach decouples event identification from precise boundary measurement, stabilizing grounding and making predictions verifiable. Extensive experiments show F2G consistently improves grounding accuracy across benchmarks like Charades-STA, ActivityNet Captions, and QVHighlights, and transfers robustly across Video-LLM backbones such as Qwen3-VL-8B, LLaVA-NeXT-7B, and Qwen2.5-VL-7B. The framework also preserves general video understanding capabilities, as demonstrated on VideoMME, and reduces inference instability with a modest overhead of ~0.5B parameters and <5% latency increase.

Key takeaway

For Machine Learning Engineers building Video-LLM applications, if you are struggling with brittle numerics and unstable temporal boundaries in video grounding, you should adopt the Foresee-to-Ground (F2G) framework. Its "Identify-then-Measure" routine, which explicitly cites event segments before refining boundaries, consistently improves accuracy and stability. This approach is robust across different Video-LLM backbones and preserves general video understanding capabilities, offering a more reliable solution for auditable video understanding.

Key insights

F2G improves video temporal grounding by decoupling event identification from precise boundary measurement using citable evidence.

Principles

• Decouple event identification from precise measurement.
• Expose intermediate evidence explicitly for LLM reasoning.
• Use boundary-sensitive features for event discovery.

Method

F2G uses a three-stage curriculum: pretrain predictive temporal perception, warm up proposal generation, then fine-tune the Video-LLM for evidence-driven grounding via LoRA with joint span-ID and timestamp supervision.

In practice

• Use Top-K=8 candidates with 4 SEE queries.
• Apply LoRA for LLM fine-tuning.
• Sample videos at 1 FPS for processing.

Topics

• Video Temporal Grounding
• Video-LLMs
• Predictive Temporal Perception
• Evidence-Driven Reasoning
• Identify-then-Measure
• Qwen3-VL-8B

Code references

• zelion2003/Foresee-to-Ground

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.