Vidit Vidit

2 accepted papers

2023

CLIP the Gap: A Single Domain Generalization Approach for Object Detection

Vidit Vidit, Martin Engilberge, Mathieu Salzmann

CVPR 2023poster

Single Domain Generalization (SDG) tackles the problem of training a model on a single source domain so that it generalizes to any unseen target domain. While this has been well studied for image classification, the literature on SDG object detection remains almost non-existent. To address the chall…

Cited by 107 Source ↗PDF ↗code ↗Scholar ↗

2023

Learning Transformations To Reduce the Geometric Shift in Object Detection

Vidit Vidit, Martin Engilberge, Mathieu Salzmann

CVPR 2023poster

The performance of modern object detectors drops when the test distribution differs from the training one. Most of the methods that address this focus on object appearance changes caused by, e.g., different illumination conditions, or gaps between synthetic and real images. Here, by contrast, we tac…

Cited by 4 Source ↗PDF ↗code ↗Scholar ↗