Papers › The Ikshana Hypothesis of Human Scene Understanding
The Ikshana Hypothesis of Human Scene Understanding
Venkata Satya Sai Ajay Daliparthi
In recent years, deep neural networks (DNNs) achieved state-of-the-art performance on several computer vision tasks. However, the one typical drawback of these DNNs is the requirement of massive labeled data. Even though few-shot learning methods address this problem, they often use techniques such as meta-learning and metric-learning on top of the existing methods. In this work, we address this problem from a neuroscience perspective by proposing a hypothesis named Ikshana, which is supported by several findings in neuroscience. Our hypothesis approximates the refining process of conceptual gist in the human brain while understanding a natural scene/image. While our hypothesis holds no particular novelty in neuroscience, it provides a novel perspective for designing DNNs for vision tasks. By following the Ikshana hypothesis, we design a novel neural-inspired CNN architecture named IkshanaNet. The empirical results demonstrate the effectiveness of our method by outperforming several baselines on the entire and subsets of the Cityscapes and the CamVid semantic segmentation benchmarks.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semantic Segmentation | Cityscapes test | IkshanaNet-1 | Category mIoU | 82.22% | #103 of 105 | Archive leaderboard | report |
| Semantic Segmentation | Cityscapes test | IkshanaNet-1 | Mean IoU (class) | 54.82% | #103 of 105 | Archive leaderboard | report |
| Semantic Segmentation | Cityscapes test | IkshanaNet-2 | Category mIoU | 76.73% | #104 of 105 | Archive leaderboard | report |
| Semantic Segmentation | Cityscapes test | IkshanaNet-2 | Mean IoU (class) | 45.02% | #104 of 105 | Archive leaderboard | report |
| Semantic Segmentation | Cityscapes test | IkshanaNet-3 | Category mIoU | 75.61% | #105 of 105 | Archive leaderboard | report |
| Semantic Segmentation | Cityscapes test | IkshanaNet-3 | Mean IoU (class) | 42.07% | #105 of 105 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: IkshanaNet
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