Transcription of Dense Relation Distillation With Context-Aware Aggregation ...
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Dense Relation Distillation With Context-Aware Aggregation ...
Dense Relation Distillation with Context-Aware Aggregation forFew-Shot Object DetectionHanzhe Hu1, Shuai Bai2, Aoxue Li1, Jinshi Cui1*, Liwei Wang1*1 Key Laboratory of Machine Perception (MOE), School of EECS, Peking University2 Beijing University of Posts and Telecommunications{huhz, deep learning based methods for object de-tection require a large amount of bounding box annotationsfor training, which is expensive to obtain such high qual-ity annotated data. Few-shot object detection, which learnsto adapt to novel classes with only a few annotated exam-ples, is very challenging since the fine-grained feature ofnovel object can be easily overlooked with only a few dataavailable. In this work, aiming to fully exploit features ofannotated novel object and capture fine-grained features ofquery object, we propose Dense Relation Distillation withContext-aware Aggregation (DCNet) to tackle the few-shotdetection problem.}
tion Network [27] learns a distance metric to compare the target image with a few labeled images. While optimiza-tion based methods [19, 5] are proposed for fast adapta-tion to new few-shot task. [11] proposes a cross-attention mechanism to learn correlations between support and query images. Above methods are focusing on the few-shot clas-
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