# Recognizing Objects In-the-wild: Where Do We Stand?

arXiv: https://arxiv.org/abs/1709.05862 · HTML: https://mlsanity.com/paper/1709.05862/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2017.

## Built on by (most cited first, 10 of 10)

- [Early or Late Fusion Matters: Efficient RGB-D Fusion in Vision Transformers for 3D Object Recognition](https://mlsanity.com/paper/2210.00843/) (2022)
- [F-SIOL-310: A Robotic Dataset and Benchmark for Few-Shot Incremental Object Learning](https://mlsanity.com/paper/2103.12242/) (2021)
- [Mobile Robot Manipulation using Pure Object Detection](https://mlsanity.com/paper/2201.12437/) (2022)
- [Persistent Homology Meets Object Unity: Object Recognition in Clutter](https://mlsanity.com/paper/2305.03815/) (2023)
- [Show and Grasp: Few-shot Semantic Segmentation for Robot Grasping through Zero-shot Foundation Models](https://mlsanity.com/paper/2404.12717/) (2024)
- [TiROD: Tiny Robotics Dataset and Benchmark for Continual Object Detection](https://mlsanity.com/paper/2409.16215/) (2024)
- [On the Challenges of Open World Recognitionunder Shifting Visual Domains](https://mlsanity.com/paper/2107.04461/) (2021)
- [Grasping the Inconspicuous](https://mlsanity.com/paper/2211.08182/) (2022)
- [RF-Annotate: Automatic RF-Supervised Image Annotation of Common Objects in Context](https://mlsanity.com/paper/2211.08837/) (2022)
- [Contrastive Learning for Cross-Domain Open World Recognition](https://mlsanity.com/paper/2203.09257/) (2022)

## Abstract (arXiv)

The ability to recognize objects is an essential skill for a robotic system acting in human-populated environments. Despite decades of effort from the robotic and vision research communities, robots are still missing good visual perceptual systems, preventing the use of autonomous agents for real-world applications. The progress is slowed down by the lack of a testbed able to accurately represent the world perceived by the robot in-the-wild. In order to fill this gap, we introduce a large-scale, multi-view object dataset collected with an RGB-D camera mounted on a mobile robot. The dataset embeds the challenges faced by a robot in a real-life application and provides a useful tool for validating object recognition algorithms. Besides describing the characteristics of the dataset, the paper evaluates the performance of a collection of well-established deep convolutional networks on the new dataset and analyzes the transferability of deep representations from Web images to robotic data. Despite the promising results obtained with such representations, the experiments demonstrate that object classification with real-life robotic data is far from being solved. Finally, we provide a comparative study to analyze and highlight the open challenges in robot vision, explaining the discrepancies in the performance.