# Chrono: A Simple Blueprint for Representing Time in MLLMs

arXiv: https://arxiv.org/abs/2406.18113 · HTML: https://mlsanity.com/paper/2406.18113/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Placed in Video understanding with MLLMs, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 42 of its 63 references that are in the map.
- First posted on arXiv 26 Jun 2024.

## Placed in

- Multimodal LLMs / vision-language models / [Video understanding with MLLMs](https://mlsanity.com/mllm/video-understanding-with-mllms/)

## Builds on (most cited first, 30 of 42)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding](https://mlsanity.com/paper/2306.02858/) (2023)
- [Dense-Captioning Events in Videos](https://mlsanity.com/paper/1705.00754/) (2017)
- [NExT-QA:Next Phase of Question-Answering to Explaining Temporal Actions](https://mlsanity.com/paper/2105.08276/) (2021)
- [Localizing Moments in Video with Natural Language](https://mlsanity.com/paper/1708.01641/) (2017)
- [CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval](https://mlsanity.com/paper/2104.08860/) (2021)
- [TALL: Temporal Activity Localization via Language Query](https://mlsanity.com/paper/1705.02101/) (2017)
- [Language Is Not All You Need: Aligning Perception with Language Models](https://mlsanity.com/paper/2302.14045/) (2023)
- [Less is More: ClipBERT for Video-and-Language Learning via Sparse Sampling](https://mlsanity.com/paper/2102.06183/) (2021)
- [TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding](https://mlsanity.com/paper/2312.02051/) (2023)
- [InternVideo2: Scaling Foundation Models for Multimodal Video Understanding](https://mlsanity.com/paper/2403.15377/) (2024)
- [Prompting Visual-Language Models for Efficient Video Understanding](https://mlsanity.com/paper/2112.04478/) (2021)
- [X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval](https://mlsanity.com/paper/2207.07285/) (2022)
- [Vid2Seq: Large-Scale Pretraining of a Visual Language Model for Dense Video Captioning](https://mlsanity.com/paper/2302.14115/) (2023)
- [VTimeLLM: Empower LLM to Grasp Video Moments](https://mlsanity.com/paper/2311.18445/) (2023)
- [CLIP2Video: Mastering Video-Text Retrieval via Image CLIP](https://mlsanity.com/paper/2106.11097/) (2021)
- [Self-Chained Image-Language Model for Video Localization and Question Answering](https://mlsanity.com/paper/2305.06988/) (2023)
- [Local-Global Video-Text Interactions for Temporal Grounding](https://mlsanity.com/paper/2004.07514/) (2020)
- [A Simple LLM Framework for Long-Range Video Question-Answering](https://mlsanity.com/paper/2312.17235/) (2023)
- [Dense Regression Network for Video Grounding](https://mlsanity.com/paper/2004.03545/) (2020)
- [Query-Dependent Video Representation for Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2303.13874/) (2023)
- [mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video](https://mlsanity.com/paper/2302.00402/) (2023)

## Built on by (most cited first, 18 of 18)

- [Lighthouse: A User-Friendly Library for Reproducible Video Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2408.02901/) (2024)
- [EgoExo-Con: Exploring View-Invariant Video Temporal Understanding](https://mlsanity.com/paper/2510.26113/) (2025)
- [ShotFinder: Imagination-Driven Open-Domain Video Shot Retrieval via Web Search](https://mlsanity.com/paper/2601.23232/) (2026)
- [How Should Video LLMs Output Time? An Analysis of Efficient Temporal Grounding Paradigms](https://mlsanity.com/paper/2604.08966/) (2026)
- [Measure Twice, Cut Once: A Semantic-Oriented Approach to Video Temporal Localization with Video LLMs](https://mlsanity.com/paper/2503.09027/) (2025)
- [Image-to-Video Transfer Learning based on Image-Language Foundation Models: A Comprehensive Survey](https://mlsanity.com/paper/2510.10671/) (2025)
- [Audio-Visual Intelligence in Large Foundation Models](https://mlsanity.com/paper/2605.04045/) (2026)
- [ShotVL: Human-Centric Highlight Frame Retrieval via Language Queries](https://mlsanity.com/paper/2412.12675/) (2024)
- [SlotVTG: Object-Centric Adapter for Generalizable Video Temporal Grounding](https://mlsanity.com/paper/2603.25733/) (2026)
- [EVIDENT: Routing MLLM Adaptation through Entity-Grounded Visual Evidence for Cross-Domain Video Temporal Grounding](https://mlsanity.com/paper/2605.26104/) (2026)
- [Decoding Pedestrian Crossing Intention from Egocentric Vision via Vision Language Models](https://mlsanity.com/paper/2606.09142/) (2026)
- [MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing](https://mlsanity.com/paper/2607.25300/) (2026)
- [Temporal Grounding as a Learning Signal for Referring Video Object Segmentation](https://mlsanity.com/paper/2508.11955/) (2025)
- [Understanding Temporal Logic Consistency in Video-Language Models through Cross-Modal Attention Discriminability](https://mlsanity.com/paper/2510.08138/) (2025)
- [Moment and Highlight Detection via MLLM Frame Segmentation](https://mlsanity.com/paper/2512.12246/) (2025)
- [EventCoT: Event-centric Video Chain-of-thought for Reasoning Temporal Localization](https://mlsanity.com/paper/2607.04872/) (2026)
- [IAE-VTG: Interaction-Aligned Action-Entity Video Temporal Grounding](https://mlsanity.com/paper/2609.09736/) (2026)
- [Long-to-Short Video Evidence Reasoning for Grounded Question Answering](https://mlsanity.com/paper/2609.15224/) (2026)

## Abstract (arXiv)

The recent success of Large Language Models (LLMs) has prompted the extension to the multimodal domain, developing image-text Multimodal LLMs (MLLMs) and then video-text models. In this work, we investigate the challenge of contextual and temporal comprehension in video-language models by exploring the task of temporal localization in videos. To address this problem, prior works have developed complex task-specific architectures, novel modules to embed time into MLLMs, or leveraged additional input signals such as video transcripts to best encode contextual and temporal information. We find that most of these efforts are surpassed by a much simpler design. We introduce Chrono, a universal sequence blueprint that can be applied to any image-text pretrained MLLM. In extensive experiments spanning different MLLM architectures and sizes, finetuning and zero-shot settings, we demonstrate new state-of-the-art results in moment retrieval on the widely used benchmarks Charades-STA, QVHighlights, and ActivityNet Captions, as well as in grounded video question answering on NExT-GQA.