# ReasVQA: Advancing VideoQA with Imperfect Reasoning Process

arXiv: https://arxiv.org/abs/2501.13536 · HTML: https://mlsanity.com/paper/2501.13536/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal reasoning, one of 17 directions in LLM reasoning.
- Builds on 28 of its 60 references that are in the map.
- First posted on arXiv 23 Jan 2025.

## Placed in

- LLM reasoning / [Multimodal reasoning](https://mlsanity.com/lr/multimodal-reasoning/)

## Builds on (most cited first, 28 of 28)

- [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)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Let's Verify Step by Step](https://mlsanity.com/paper/2305.20050/) (2023)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [LLaVA-OneVision: Easy Visual Task Transfer](https://mlsanity.com/paper/2408.03326/) (2024)
- [Video-LLaVA: Learning United Visual Representation by Alignment Before Projection](https://mlsanity.com/paper/2311.10122/) (2023)
- [How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites](https://mlsanity.com/paper/2404.16821/) (2024)
- [Solving math word problems with process- and outcome-based feedback](https://mlsanity.com/paper/2211.14275/) (2022)
- [Gemini: A Family of Highly Capable Multimodal Models](https://mlsanity.com/paper/2312.11805/) (2023)
- [VideoChat: Chat-Centric Video Understanding](https://mlsanity.com/paper/2305.06355/) (2023)
- [Multimodal Chain-of-Thought Reasoning in Language Models](https://mlsanity.com/paper/2302.00923/) (2023)
- [Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language](https://mlsanity.com/paper/2204.00598/) (2022)
- [InternVideo: General Video Foundation Models via Generative and Discriminative Learning](https://mlsanity.com/paper/2212.03191/) (2022)
- [Self-Chained Image-Language Model for Video Localization and Question Answering](https://mlsanity.com/paper/2305.06988/) (2023)
- [A Simple LLM Framework for Long-Range Video Question-Answering](https://mlsanity.com/paper/2312.17235/) (2023)
- [Too Many Frames, Not All Useful: Efficient Strategies for Long-Form Video QA](https://mlsanity.com/paper/2406.09396/) (2024)
- [Language Repository for Long Video Understanding](https://mlsanity.com/paper/2403.14622/) (2024)
- [Vamos: Versatile Action Models for Video Understanding](https://mlsanity.com/paper/2311.13627/) (2023)
- [MMFT-BERT: Multimodal Fusion Transformer with BERT Encodings for Visual Question Answering](https://mlsanity.com/paper/2010.14095/) (2020)
- [Retrieving-to-Answer: Zero-Shot Video Question Answering with Frozen Large Language Models](https://mlsanity.com/paper/2306.11732/) (2023)
- [ViLA: Efficient Video-Language Alignment for Video Question Answering](https://mlsanity.com/paper/2312.08367/) (2023)
- [End-to-End Video Question Answering with Frame Scoring Mechanisms and Adaptive Sampling](https://mlsanity.com/paper/2407.15047/) (2024)
- [Efficient Temporal Extrapolation of Multimodal Large Language Models with Temporal Grounding Bridge](https://mlsanity.com/paper/2402.16050/) (2024)
- [Question-Instructed Visual Descriptions for Zero-Shot Video Question Answering](https://mlsanity.com/paper/2402.10698/) (2024)

## Built on by (most cited first, 5 of 5)

- [Large Language Models Meet Knowledge Graphs for Question Answering: Synthesis and Opportunities](https://mlsanity.com/paper/2505.20099/) (2025)
- [Why Reasoning Matters? A Survey of Advancements in Multimodal Reasoning (v1)](https://mlsanity.com/paper/2504.03151/) (2025)
- [VRR-QA: Visual Relational Reasoning in Videos Beyond Explicit Cues](https://mlsanity.com/paper/2506.21742/) (2025)
- [Beyond Isolated Facts: Synthesizing Narrative and Grounded Supervision for VideoQA](https://mlsanity.com/paper/2509.24445/) (2025)
- [UpstreamQA: A Modular Framework for Explicit Reasoning on Video Question Answering Tasks](https://mlsanity.com/paper/2604.23145/) (2026)

## Abstract (arXiv)

Video Question Answering (VideoQA) is a challenging task that requires understanding complex visual and temporal relationships within videos to answer questions accurately. In this work, we introduce ReasVQA (Reasoning-enhanced Video Question Answering), a novel approach that leverages reasoning processes generated by Multimodal Large Language Models (MLLMs) to improve the performance of VideoQA models. Our approach consists of three phases: reasoning generation, reasoning refinement, and learning from reasoning. First, we generate detailed reasoning processes using additional MLLMs, and second refine them via a filtering step to ensure data quality. Finally, we use the reasoning data, which might be in an imperfect form, to guide the VideoQA model via multi-task learning, on how to interpret and answer questions based on a given video. We evaluate ReasVQA on three popular benchmarks, and our results establish new state-of-the-art performance with significant improvements of +2.9 on NExT-QA, +7.3 on STAR, and +5.9 on IntentQA. Our findings demonstrate the supervising benefits of integrating reasoning processes into VideoQA. Further studies validate each component of our method, also with different backbones and MLLMs, and again highlight the advantages of this simple but effective method. We offer a new perspective on enhancing VideoQA performance by utilizing advanced reasoning techniques, setting a new benchmark in this research field.