Summary of Believing Is Seeing: Unobserved Object Detection Using Generative Models, by Subhransu S. Bhattacharjee and Dylan Campbell and Rahul Shome
Believing is Seeing: Unobserved Object Detection using Generative Models
by Subhransu S. Bhattacharjee, Dylan Campbell, Rahul Shome
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces the novel tasks of 2D, 2.5D, and 3D unobserved object detection, aiming to predict the location of nearby objects occluded or outside the image frame. The authors adapt state-of-the-art generative models for this task, including diffusion models and vision-language models, demonstrating their ability to infer unseen object presence. To evaluate performance, the paper proposes a suite of metrics capturing different aspects. Empirical results on indoor scenes from RealEstate10k and NYU Depth v2 datasets demonstrate promising outcomes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Can objects outside an image be detected? This study explores this idea by introducing new tasks: detecting 2D, 2.5D, or 3D objects not visible in the picture but nearby. The paper shows that special computer models can predict where these hidden objects are. It also suggests ways to measure how well these models work. |
Keywords
» Artificial intelligence » Object detection
