Generalized Zero-Shot Object Detection
7 papers with code • 1 benchmarks • 1 datasets
MS COCO
Most implemented papers
Polarity Loss for Zero-shot Object Detection
This setting gives rise to the need for correct alignment between visual and semantic concepts, so that the unseen objects can be identified using only their semantic attributes.
Synthesizing the Unseen for Zero-shot Object Detection
nasir6/zero_shot_detection
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The existing zero-shot detection approaches project visual features to the semantic domain for seen objects, hoping to map unseen objects to their corresponding semantics during inference.
Background Learnable Cascade for Zero-Shot Object Detection
zhengye1995/BLC
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The major contributions for BLC are as follows: (i) we propose a multi-stage cascade structure named Cascade Semantic R-CNN to progressively refine the alignment between visual and semantic of ZSD; (ii) we develop the semantic information flow structure and directly add it between each stage in Cascade Semantic RCNN to further improve the semantic feature learning; (iii) we propose the background learnable region proposal network (BLRPN) to learn an appropriate 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.
Robust Region Feature Synthesizer for Zero-Shot Object Detection
HPL123/RRFS
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CVPR 2022
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.
From Node to Graph: Joint Reasoning on Visual-Semantic Relational Graph for Zero-Shot Detection
witnessai/GRAN
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Winter Conference on Applications of Computer Vision (WACV) 2022
Zero-Shot Detection (ZSD), which aims at localizing andrecognizing unseen objects in a complicated scene, usuallyleverages the visual and semantic information of individ-ual objects alone.
Resolving Semantic Confusions for Improved Zero-Shot Detection
sandipan211/ZSD-SC-Resolver
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British Machine Vision Conference 2022
Zero-shot detection (ZSD) is a challenging task where we aim to recognize and localize objects simultaneously, even when our model has not been trained with visual samples of a few target ("unseen") classes.
SeeDS: Semantic Separable Diffusion Synthesizer for Zero-shot Food Detection
lancezpf/seeds
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To tackle this, we propose the Semantic Separable Diffusion Synthesizer (SeeDS) framework for Zero-Shot Food Detection (ZSFD).
