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Publications associées (4)
Improving Object Detection under Domain Shifts
Object detection plays a critical role in various computer vision applications, encompassingdomains like autonomous vehicles, object tracking, and scene understanding. These applica-tions rely on detectors that generate bounding boxes around known object c ...
EPFL2023CLIP the Gap: A Single Domain Generalization Approach for Object Detection
Mathieu Salzmann, Martin Pierre Engilberge, Vidit Vidit
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 remai ...
Los Alamitos2023Learning Transformations To Reduce the Geometric Shift in Object Detection
Mathieu Salzmann, Martin Pierre Engilberge, Vidit Vidit
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 syntheti ...
Los Alamitos2023Attention-based domain adaptation for single-stage detectors
While domain adaptation has been used to improve the performance of object detectors when the training and test data follow different distributions, previous work has mostly focused on two-stage detectors. This is because their use of region proposals make ...
SPRINGER2022