# Encoding and Controlling Global Semantics for Long-form Video Question Answering

arXiv: https://arxiv.org/abs/2405.19723 · HTML: https://mlsanity.com/paper/2405.19723/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 46 of its 67 references that are in the map.
- First posted on arXiv 30 May 2024.

## Builds on (most cited first, 30 of 46)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021)
- [Ego4D: Around the World in 3,000 Hours of Egocentric Video](https://mlsanity.com/paper/2110.07058/) (2021)
- [Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval](https://mlsanity.com/paper/2104.00650/) (2021)
- [AI2-THOR: An Interactive 3D Environment for Visual AI](https://mlsanity.com/paper/1712.05474/) (2017)
- [NExT-QA:Next Phase of Question-Answering to Explaining Temporal Actions](https://mlsanity.com/paper/2105.08276/) (2021)
- [EgoSchema: A Diagnostic Benchmark for Very Long-form Video Language Understanding](https://mlsanity.com/paper/2308.09126/) (2023)
- [TVQA: Localized, Compositional Video Question Answering](https://mlsanity.com/paper/1809.01696/) (2018)
- [TGIF-QA: Toward Spatio-Temporal Reasoning in Visual Question Answering](https://mlsanity.com/paper/1704.04497/) (2017)
- [MovieQA: Understanding Stories in Movies through Question-Answering](https://mlsanity.com/paper/1512.02902/) (2015)
- [Just Ask: Learning to Answer Questions from Millions of Narrated Videos](https://mlsanity.com/paper/2012.00451/) (2020)
- [STAR: A Benchmark for Situated Reasoning in Real-World Videos](https://mlsanity.com/paper/2405.09711/) (2024)
- [MERLOT Reserve: Neural Script Knowledge through Vision and Language and Sound](https://mlsanity.com/paper/2201.02639/) (2022)
- [All in One: Exploring Unified Video-Language Pre-training](https://mlsanity.com/paper/2203.07303/) (2022)
- [Revisiting the "Video" in Video-Language Understanding](https://mlsanity.com/paper/2206.01720/) (2022)
- [TVQA+: Spatio-Temporal Grounding for Video Question Answering](https://mlsanity.com/paper/1904.11574/) (2019)
- [Towards Long-Form Video Understanding](https://mlsanity.com/paper/2106.11310/) (2021)
- [AGQA: A Benchmark for Compositional Spatio-Temporal Reasoning](https://mlsanity.com/paper/2103.16002/) (2021)
- [MAD: A Scalable Dataset for Language Grounding in Videos from Movie Audio Descriptions](https://mlsanity.com/paper/2112.00431/) (2021)
- [Long Movie Clip Classification with State-Space Video Models](https://mlsanity.com/paper/2204.01692/) (2022)
- [Video ReCap: Recursive Captioning of Hour-Long Videos](https://mlsanity.com/paper/2402.13250/) (2024)
- [An Empirical Study of End-to-End Video-Language Transformers with Masked Visual Modeling](https://mlsanity.com/paper/2209.01540/) (2022)
- [Video as Conditional Graph Hierarchy for Multi-Granular Question Answering](https://mlsanity.com/paper/2112.06197/) (2021)
- [Contrastive Learning for Neural Topic Model](https://mlsanity.com/paper/2110.12764/) (2021)
- [Effective Neural Topic Modeling with Embedding Clustering Regularization](https://mlsanity.com/paper/2306.04217/) (2023)
- [ECLIPSE: Efficient Long-range Video Retrieval using Sight and Sound](https://mlsanity.com/paper/2204.02874/) (2022)
- [Multimodal Learning using Optimal Transport for Sarcasm and Humor Detection](https://mlsanity.com/paper/2110.10949/) (2021)
- [Efficient Long Sequence Modeling via State Space Augmented Transformer](https://mlsanity.com/paper/2212.08136/) (2022)

## Built on by (most cited first, 8 of 8)

- [AIM: Adaptive Inference of Multi-Modal LLMs via Token Merging and Pruning](https://mlsanity.com/paper/2412.03248/) (2024)
- [Motion-aware Contrastive Learning for Temporal Panoptic Scene Graph Generation](https://mlsanity.com/paper/2412.07160/) (2024)
- [TI-JEPA: An Innovative Energy-based Joint Embedding Strategy for Text-Image Multimodal Systems](https://mlsanity.com/paper/2503.06380/) (2025)
- [Penguin-VL: Exploring the Efficiency Limits of VLM with LLM-based Vision Encoders](https://mlsanity.com/paper/2603.06569/) (2026)
- [AdaCodec: A Predictive Visual Code for Video MLLMs](https://mlsanity.com/paper/2606.02569/) (2026)
- [PAVE: Patching and Adapting Video Large Language Models](https://mlsanity.com/paper/2503.19794/) (2025)
- [EgoPro-Bench: Benchmarking Personalized Proactive Interaction in Egocentric Video Streams](https://mlsanity.com/paper/2605.07299/) (2026)
- [Temporal-Oriented Recipe for Transferring Large Vision-Language Model to Video Understanding](https://mlsanity.com/paper/2505.12605/) (2025)

## Abstract (arXiv)

Seeking answers effectively for long videos is essential to build video question answering (videoQA) systems. Previous methods adaptively select frames and regions from long videos to save computations. However, this fails to reason over the whole sequence of video, leading to sub-optimal performance. To address this problem, we introduce a state space layer (SSL) into multi-modal Transformer to efficiently integrate global semantics of the video, which mitigates the video information loss caused by frame and region selection modules. Our SSL includes a gating unit to enable controllability over the flow of global semantics into visual representations. To further enhance the controllability, we introduce a cross-modal compositional congruence (C^3) objective to encourage global semantics aligned with the question. To rigorously evaluate long-form videoQA capacity, we construct two new benchmarks Ego-QA and MAD-QA featuring videos of considerably long length, i.e. 17.5 minutes and 1.9 hours, respectively. Extensive experiments demonstrate the superiority of our framework on these new as well as existing datasets. The code, model, and data have been made available at https://nguyentthong.github.io/Long_form_VideoQA.