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Article overview
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Robust Region Feature Synthesizer for Zero-Shot Object Detection | | Peiliang Huang
; Junwei Han
; De Cheng
; Dingwen Zhang
; | | Date: |
1 Jan 2022 | | Abstract: | Zero-shot object detection aims at incorporating class semantic vectors to
realize the detection of (both seen and) unseen classes given an unconstrained
test image. In this study, we reveal the core challenges in this research area:
how to synthesize robust region features (for unseen objects) that are as
intra-class diverse and inter-class separable as the real samples, so that
strong unseen object detectors can be trained upon them. To address these
challenges, we build a novel zero-shot object detection framework that contains
an Intra-class Semantic Diverging component and an Inter-class Structure
Preserving component. The former is used to realize the one-to-more mapping to
obtain diverse visual features from each class semantic vector, preventing
miss-classifying the real unseen objects as image backgrounds. While the latter
is used to avoid the synthesized features too scattered to mix up the
inter-class and foreground-background relationship. To demonstrate the
effectiveness of the proposed approach, comprehensive experiments on PASCAL
VOC, COCO, and DIOR datasets are conducted. Notably, our approach achieves the
new state-of-the-art performance on PASCAL VOC and COCO and it is the first
study to carry out zero-shot object detection in remote sensing imagery. | | Source: | arXiv, 2201.00103 | | Services: | Forum | Review | PDF | Favorites | |
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