# Detecting Object States vs Detecting Objects: A New Dataset and a Quantitative Experimental Study

arXiv: https://arxiv.org/abs/2112.08281 · HTML: https://mlsanity.com/paper/2112.08281/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 22 references that are in the map.
- First posted on arXiv 15 Dec 2021.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [On the effectiveness of task granularity for transfer learning](https://mlsanity.com/paper/1804.09235/) (2018)
- [Actions ~ Transformations](https://mlsanity.com/paper/1512.00795/) (2015)
- [Joint Discovery of Object States and Manipulation Actions](https://mlsanity.com/paper/1702.02738/) (2017)
- [COBE: Contextualized Object Embeddings from Narrated Instructional Video](https://mlsanity.com/paper/2007.07306/) (2020)
- [Recognizing Manipulation Actions from State-Transformations](https://mlsanity.com/paper/1906.05147/) (2019)

## Built on by (most cited first, 6 of 6)

- [Details Make a Difference: Object State-Sensitive Neurorobotic Task Planning](https://mlsanity.com/paper/2406.09988/) (2024)
- [Fusing Domain-Specific Content from Large Language Models into Knowledge Graphs for Enhanced Zero Shot Object State Classification](https://mlsanity.com/paper/2403.12151/) (2024)
- [STATUS Bench: A Rigorous Benchmark for Evaluating Object State Understanding in Vision-Language Models](https://mlsanity.com/paper/2510.22571/) (2025)
- [Anticipating Object State Changes in Long Procedural Videos](https://mlsanity.com/paper/2405.12789/) (2024)
- [Recognizing Unseen States of Unknown Objects by Leveraging Knowledge Graphs](https://mlsanity.com/paper/2307.12179/) (2023)
- [StateVLM: A State-Aware Vision-Language Model for Robotic Affordance Reasoning](https://mlsanity.com/paper/2605.03927/) (2026)

## Abstract (arXiv)

The detection of object states in images (State Detection - SD) is a problem of both theoretical and practical importance and it is tightly interwoven with other important computer vision problems, such as action recognition and affordance detection. It is also highly relevant to any entity that needs to reason and act in dynamic domains, such as robotic systems and intelligent agents. Despite its importance, up to now, the research on this problem has been limited. In this paper, we attempt a systematic study of the SD problem. First, we introduce the Object State Detection Dataset (OSDD), a new publicly available dataset consisting of more than 19,000 annotations for 18 object categories and 9 state classes. Second, using a standard deep learning framework used for Object Detection (OD), we conduct a number of appropriately designed experiments, towards an in-depth study of the behavior of the SD problem. This study enables the setup of a baseline on the performance of SD, as well as its relative performance in comparison to OD, in a variety of scenarios. Overall, the experimental outcomes confirm that SD is harder than OD and that tailored SD methods need to be developed for addressing effectively this significant problem.