# Learning Multiple Object States from Actions via Large Language Models

arXiv: https://arxiv.org/abs/2405.01090 · HTML: https://mlsanity.com/paper/2405.01090/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 50 references that are in the map.
- First posted on arXiv 2 May 2024.

## Builds on (most cited first, 30 of 37)

- [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)
- [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)
- [Ego4D: Around the World in 3,000 Hours of Egocentric Video](https://mlsanity.com/paper/2110.07058/) (2021)
- [HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips](https://mlsanity.com/paper/1906.03327/) (2019)
- [VideoBERT: A Joint Model for Video and Language Representation Learning](https://mlsanity.com/paper/1904.01766/) (2019)
- [Towards Automatic Learning of Procedures from Web Instructional Videos](https://mlsanity.com/paper/1703.09788/) (2017)
- [Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language](https://mlsanity.com/paper/2204.00598/) (2022)
- [End-to-End Learning of Visual Representations from Uncurated Instructional Videos](https://mlsanity.com/paper/1912.06430/) (2019)
- [InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation](https://mlsanity.com/paper/2307.06942/) (2023)
- [InternVideo: General Video Foundation Models via Generative and Discriminative Learning](https://mlsanity.com/paper/2212.03191/) (2022)
- [MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation](https://mlsanity.com/paper/1903.01945/) (2019)
- [Assembly101: A Large-Scale Multi-View Video Dataset for Understanding Procedural Activities](https://mlsanity.com/paper/2203.14712/) (2022)
- [Advancing High-Resolution Video-Language Representation with Large-Scale Video Transcriptions](https://mlsanity.com/paper/2111.10337/) (2021)
- [Learning Video Representations from Large Language Models](https://mlsanity.com/paper/2212.04501/) (2022)
- [Cross-task weakly supervised learning from instructional videos](https://mlsanity.com/paper/1903.08225/) (2019)
- [VisualGPT: Data-efficient Adaptation of Pretrained Language Models for Image Captioning](https://mlsanity.com/paper/2102.10407/) (2021)
- [Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners](https://mlsanity.com/paper/2205.10747/) (2022)
- [Unsupervised Learning from Narrated Instruction Videos](https://mlsanity.com/paper/1506.09215/) (2015)
- [Learning To Recognize Procedural Activities with Distant Supervision](https://mlsanity.com/paper/2201.10990/) (2022)
- [Temporal Alignment Networks for Long-term Video](https://mlsanity.com/paper/2204.02968/) (2022)
- [Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels](https://mlsanity.com/paper/1512.06974/) (2015)
- [Procedure-Aware Pretraining for Instructional Video Understanding](https://mlsanity.com/paper/2303.18230/) (2023)
- [Learning Procedure-aware Video Representation from Instructional Videos and Their Narrations](https://mlsanity.com/paper/2303.17839/) (2023)
- [MAtch, eXpand and Improve: Unsupervised Finetuning for Zero-Shot Action Recognition with Language Knowledge](https://mlsanity.com/paper/2303.08914/) (2023)
- [Look for the Change: Learning Object States and State-Modifying Actions from Untrimmed Web Videos](https://mlsanity.com/paper/2203.11637/) (2022)
- [StepFormer: Self-supervised Step Discovery and Localization in Instructional Videos](https://mlsanity.com/paper/2304.13265/) (2023)
- [Learning Object State Changes in Videos: An Open-World Perspective](https://mlsanity.com/paper/2312.11782/) (2023)

## Built on by (most cited first, 4 of 4)

- [Masked Video and Body-worn IMU Autoencoder for Egocentric Action Recognition](https://mlsanity.com/paper/2407.06628/) (2024)
- [ActionVOS: Actions as Prompts for Video Object Segmentation](https://mlsanity.com/paper/2407.07402/) (2024)
- [HanDyVQA: A Video QA Benchmark for Fine-Grained Hand-Object Interaction Dynamics](https://mlsanity.com/paper/2512.00885/) (2025)
- [Do Egocentric Video-Language Models Capture Both Hand- and Object-Centric Cues?](https://mlsanity.com/paper/2607.08514/) (2026)

## Abstract (arXiv)

Recognizing the states of objects in a video is crucial in understanding the scene beyond actions and objects. For instance, an egg can be raw, cracked, and whisked while cooking an omelet, and these states can coexist simultaneously (an egg can be both raw and whisked). However, most existing research assumes a single object state change (e.g., uncracked -> cracked), overlooking the coexisting nature of multiple object states and the influence of past states on the current state. We formulate object state recognition as a multi-label classification task that explicitly handles multiple states. We then propose to learn multiple object states from narrated videos by leveraging large language models (LLMs) to generate pseudo-labels from the transcribed narrations, capturing the influence of past states. The challenge is that narrations mostly describe human actions in the video but rarely explain object states. Therefore, we use the LLMs knowledge of the relationship between actions and states to derive the missing object states. We further accumulate the derived object states to consider past state contexts to infer current object state pseudo-labels. We newly collect a dataset called the Multiple Object States Transition (MOST) dataset, which includes manual multi-label annotation for evaluation purposes, covering 60 object states across six object categories. Experimental results show that our model trained on LLM-generated pseudo-labels significantly outperforms strong vision-language models, demonstrating the effectiveness of our pseudo-labeling framework that considers past context via LLMs.