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Found 8 papers, 3 papers with code
Domain Adaptive Object Detection via Balancing Between Self-Training and Adversarial Learning
Deep learning based object detectors struggle generalizing to a new target domain bearing significant variations in object and background.
Cal-DETR: Calibrated Detection Transformer
Third, we develop a logit mixing approach that acts as a regularizer with detection-specific losses and is also complementary to the uncertainty-guided logit modulation technique to further improve the calibration performance.
Bridging Precision and Confidence: A Train-Time Loss for Calibrating Object Detection
Since the original formulation of our loss depends on the counts of true positives and false positives in a minibatch, we develop a differentiable proxy of our loss that can be used during training with other application-specific loss functions.
Towards Improving Calibration in Object Detection Under Domain Shift
To this end, we first propose a new, plug-and-play, train-time calibration loss for object detection (coined as TCD).
SSAL: Synergizing between Self-Training and Adversarial Learning for Domain Adaptive Object Detection
In this paper, we propose to leverage model’s predictive uncertainty to strike the right balance between adversarial feature alignment and class-level alignment.
Synergizing between Self-Training and Adversarial Learning for Domain Adaptive Object Detection
In this paper, we propose to leverage model predictive uncertainty to strike the right balance between adversarial feature alignment and class-level alignment.
Localizing Firearm Carriers by Identifying Human-Object Pairs
Visual identification of gunmen in a crowd is a challenging problem, that requires resolving the association of a person with an object (firearm).
Leveraging Orientation for Weakly Supervised Object Detection with Application to Firearm Localization
The OAOD algorithm is evaluated on the ITUF dataset and compared with current state-of-the-art object detectors, including fully supervised oriented object detectors.
