Titlebook: Computer Vision – ACCV 2020; 15th Asian Conferenc Hiroshi Ishikawa,Cheng-Lin Liu,Jianbo Shi Conference proceedings 2021 Springer Nature Swi

TOXIN

Fully Supervised and Guided Distillation for One-Stage Detectors regions and false detection regions of student networks to effectively distill the feature representation from teacher networks. To address it, we propose a fully supervised and guided distillation algorithm for one-stage detectors, where an excitation and suppression loss is designed to make a stu

opportune

Visualizing Color-Wise Saliency of Black-Box Image Classification Modelsarning, is often hard to interpret. This problem of interpretability is one of the major obstacles in deploying a trained model in safety-critical systems. Several techniques have been proposed to address this problem; one of which is RISE, which explains a classification result by a heatmap, called

exorbitant

責(zé)難

D2D: Keypoint Extraction with Describe to Detect Approachbe, or jointly detect and describe are two typical strategies for extracting local features. In contrast, we propose an approach that inverts this process by first describing and then detecting the keypoint locations. Describe-to-Detect (D2D) leverages successful descriptor models without the need f

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Arresting

Adaptive Spotting: Deep Reinforcement Object Search in 3D Point Cloudsa. A straightforward approach that exhaustively scans the scene is often prohibitive due to computational inefficiencies. High-quality feature representation also needs to be learned to achieve accurate recognition and localization. Aiming to address these two fundamental problems in a unified frame