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

接觸

Background Learnable Cascade for Zero-Shot Object Detectionriate word vector for background class and use this learned vector in Cascade Semantic R-CNN, this design makes “Background Learnable” and reduces the confusion between background and unseen classes. Our extensive experiments show BLC obtains significantly performance improvements for MS-COCO over s

Celiac-Plexus

知識分子

Synthesizing the Unseen for Zero-Shot Object Detectiontected bounding boxes. We test our approach on three object detection benchmarks, PASCAL VOC, MSCOCO, and ILSVRC detection, under both conventional and generalized settings, showing impressive gains over the state-of-the-art methods. Our codes are available at ..

indubitable

Fully Supervised and Guided Distillation for One-Stage Detectors process. Extensive experiments on Pascal VOC and COCO benchmarks demonstrate the following advantages of our algorithm, including the effectiveness for improving recall and reducing false detections, the robustness on common one-stage detector heads and the superiority compared with state-of-the-ar

GILD

Visualizing Color-Wise Saliency of Black-Box Image Classification Modelsion). We implemented MC-RISE and evaluate them using two datasets (GTSRB and ImageNet) to demonstrate the effectiveness of our methods in comparison with existing techniques for interpreting image classification results.

懶惰人民

實(shí)現(xiàn)

Synthetic-to-Real Unsupervised Domain Adaptation for Scene Text Detection in the Wildadverse effects of false positives?(FPs) and false negatives?(FNs) from inaccurate pseudo-labels. Two components have positive effects on improving the performance of scene text detectors when adapting from synthetic-to-real scenes. We evaluate the proposed method by transferring from SynthText, VIS

amputation

固定某物

Satya Krishna Ramachandran,Sachin Kheterpalr than some independent operators. We perform experiments on multiple benchmarks including image matching, camera localisation, and 3D reconstruction. The results indicate that our method improves the matching performance of various descriptors and that it generalises across methods and tasks.

incredulity