# EgoThink: Evaluating First-Person Perspective Thinking Capability of Vision-Language Models

arXiv: https://arxiv.org/abs/2311.15596 · HTML: https://mlsanity.com/paper/2311.15596/

## Key facts

- Cited by 60 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal reasoning, one of 17 directions in LLM reasoning.
- Builds on 33 of its 91 references that are in the map.
- First posted on arXiv 27 Nov 2023.

## Placed in

- LLM reasoning / [Multimodal reasoning](https://mlsanity.com/lr/multimodal-reasoning/)
- Multimodal LLMs / vision-language models / [Embodied and 3D vision-language models](https://mlsanity.com/mllm/embodied-and-3d-vision-language-models/)

## Builds on (most cited first, 30 of 33)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [MMBench: Is Your Multi-modal Model an All-around Player?](https://mlsanity.com/paper/2307.06281/) (2023)
- [PaLM-E: An Embodied Multimodal Language Model](https://mlsanity.com/paper/2303.03378/) (2023)
- [MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models](https://mlsanity.com/paper/2306.13394/) (2023)
- [Video-LLaVA: Learning United Visual Representation by Alignment Before Projection](https://mlsanity.com/paper/2311.10122/) (2023)
- [Inner Monologue: Embodied Reasoning through Planning with Language Models](https://mlsanity.com/paper/2207.05608/) (2022)
- [ShareGPT4V: Improving Large Multi-Modal Models with Better Captions](https://mlsanity.com/paper/2311.12793/) (2023)
- [VideoChat: Chat-Centric Video Understanding](https://mlsanity.com/paper/2305.06355/) (2023)
- [VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models](https://mlsanity.com/paper/2307.05973/) (2023)
- [The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)](https://mlsanity.com/paper/2309.17421/) (2023)
- [LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model](https://mlsanity.com/paper/2304.15010/) (2023)
- [OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models](https://mlsanity.com/paper/2308.01390/) (2023)
- [Otter: A Multi-Modal Model with In-Context Instruction Tuning](https://mlsanity.com/paper/2305.03726/) (2023)
- [PandaGPT: One Model To Instruction-Follow Them All](https://mlsanity.com/paper/2305.16355/) (2023)
- [Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V](https://mlsanity.com/paper/2310.11441/) (2023)
- [MIMIC-IT: Multi-Modal In-Context Instruction Tuning](https://mlsanity.com/paper/2306.05425/) (2023)
- [OpenChat: Advancing Open-source Language Models with Mixed-Quality Data](https://mlsanity.com/paper/2309.11235/) (2023)
- [LVLM-eHub: A Comprehensive Evaluation Benchmark for Large Vision-Language Models](https://mlsanity.com/paper/2306.09265/) (2023)
- [Multimodal C4: An Open, Billion-scale Corpus of Images Interleaved with Text](https://mlsanity.com/paper/2304.06939/) (2023)
- [Physically Grounded Vision-Language Models for Robotic Manipulation](https://mlsanity.com/paper/2309.02561/) (2023)
- [F-VLM: Open-Vocabulary Object Detection upon Frozen Vision and Language Models](https://mlsanity.com/paper/2209.15639/) (2022)
- [VL-CheckList: Evaluating Pre-trained Vision-Language Models with Objects, Attributes and Relations](https://mlsanity.com/paper/2207.00221/) (2022)
- [Octopus: Embodied Vision-Language Programmer from Environmental Feedback](https://mlsanity.com/paper/2310.08588/) (2023)
- [Towards Language Models That Can See: Computer Vision Through the LENS of Natural Language](https://mlsanity.com/paper/2306.16410/) (2023)

## Built on by (most cited first, 30 of 60)

- [Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security](https://mlsanity.com/paper/2401.05459/) (2024)
- [VisuLogic: A Benchmark for Evaluating Visual Reasoning in Multi-modal Large Language Models](https://mlsanity.com/paper/2504.15279/) (2025)
- [EgoLife: Towards Egocentric Life Assistant](https://mlsanity.com/paper/2503.03803/) (2025)
- [EgoTextVQA: Towards Egocentric Scene-Text Aware Video Question Answering](https://mlsanity.com/paper/2502.07411/) (2025)
- [Omnia de EgoTempo: Benchmarking Temporal Understanding of Multi-Modal LLMs in Egocentric Videos](https://mlsanity.com/paper/2503.13646/) (2025)
- [EVA: An Embodied World Model for Future Video Anticipation](https://mlsanity.com/paper/2410.15461/) (2024)
- [Robobench: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models as Embodied Brain](https://mlsanity.com/paper/2510.17801/) (2025)
- [MM-Ego: Towards Building Egocentric Multimodal LLMs for Video QA](https://mlsanity.com/paper/2410.07177/) (2024)
- [In the Eye of MLLM: Benchmarking Egocentric Video Intent Understanding with Gaze-Guided Prompting](https://mlsanity.com/paper/2509.07447/) (2025)
- [EgoBlind: Towards Egocentric Visual Assistance for the Blind](https://mlsanity.com/paper/2503.08221/) (2025)
- [EgoCross: Benchmarking Multimodal Large Language Models for Cross-Domain Egocentric Video Question Answering](https://mlsanity.com/paper/2508.10729/) (2025)
- [Challenges and Trends in Egocentric Vision: A Survey](https://mlsanity.com/paper/2503.15275/) (2025)
- [EgoNight: Towards Egocentric Vision Understanding at Night with a Challenging Benchmark](https://mlsanity.com/paper/2510.06218/) (2025)
- [Instruction-Guided Visual Masking](https://mlsanity.com/paper/2405.19783/) (2024)
- [ET-Plan-Bench: Embodied Task-level Planning Benchmark Towards Spatial-Temporal Cognition with Foundation Models](https://mlsanity.com/paper/2410.14682/) (2024)
- [Euclid's Gift: Enhancing Spatial Perception and Reasoning in Vision-Language Models via Geometric Surrogate Tasks](https://mlsanity.com/paper/2509.24473/) (2025)
- [Wow, wo, val! A Comprehensive Embodied World Model Evaluation Turing Test](https://mlsanity.com/paper/2601.04137/) (2026)
- [StreamEQA: Towards Streaming Video Understanding for Embodied Scenarios](https://mlsanity.com/paper/2512.04451/) (2025)
- [TeleEgo: Benchmarking Egocentric AI Assistants in the Wild](https://mlsanity.com/paper/2510.23981/) (2025)
- [PhysBrain: Human Egocentric Data as a Bridge from Vision Language Models to Physical Intelligence](https://mlsanity.com/paper/2512.16793/) (2025)
- [EgoSound: Benchmarking Sound Understanding in Egocentric Videos](https://mlsanity.com/paper/2602.14122/) (2026)
- [Advancing Egocentric Video Question Answering with Multimodal Large Language Models](https://mlsanity.com/paper/2504.04550/) (2025)
- [Benchmarking Egocentric Multimodal Goal Inference for Assistive Wearable Agents](https://mlsanity.com/paper/2510.22443/) (2025)
- [EgoAction: Egocentric Action Composition with Reliability-Aware Temporal Fusion for the EPIC-KITCHENS Action Detection Challenge at CVPR 2026](https://mlsanity.com/paper/2605.24496/) (2026)
- [Ego-Grounding for Personalized Question-Answering in Egocentric Videos](https://mlsanity.com/paper/2604.01966/) (2026)
- [HIS-GPT: Towards 3D Human-In-Scene Multimodal Understanding](https://mlsanity.com/paper/2503.12955/) (2025)
- [Towards Comprehensive Scene Understanding: Integrating First and Third-Person Views for LVLMs](https://mlsanity.com/paper/2505.21955/) (2025)
- [EASG-Bench: Video Q&A Benchmark with Egocentric Action Scene Graphs](https://mlsanity.com/paper/2506.05787/) (2025)
- [Recurrent Reasoning with Vision-Language Models for Estimating Long-Horizon Embodied Task Progress](https://mlsanity.com/paper/2603.17312/) (2026)
- [EgoSAT: A Comprehensive Benchmark of Egocentric Streaming Interaction Understanding](https://mlsanity.com/paper/2606.24422/) (2026)

## Abstract (arXiv)

Vision-language models (VLMs) have recently shown promising results in traditional downstream tasks. Evaluation studies have emerged to assess their abilities, with the majority focusing on the third-person perspective, and only a few addressing specific tasks from the first-person perspective. However, the capability of VLMs to "think" from a first-person perspective, a crucial attribute for advancing autonomous agents and robotics, remains largely unexplored. To bridge this research gap, we introduce EgoThink, a novel visual question-answering benchmark that encompasses six core capabilities with twelve detailed dimensions. The benchmark is constructed using selected clips from egocentric videos, with manually annotated question-answer pairs containing first-person information. To comprehensively assess VLMs, we evaluate eighteen popular VLMs on EgoThink. Moreover, given the open-ended format of the answers, we use GPT-4 as the automatic judge to compute single-answer grading. Experimental results indicate that although GPT-4V leads in numerous dimensions, all evaluated VLMs still possess considerable potential for improvement in first-person perspective tasks. Meanwhile, enlarging the number of trainable parameters has the most significant impact on model performance on EgoThink. In conclusion, EgoThink serves as a valuable addition to existing evaluation benchmarks for VLMs, providing an indispensable resource for future research in the realm of embodied artificial intelligence and robotics.