Zero-Shot Object Detection
10 papers with code • 3 benchmarks • 4 datasets
Zero-shot object detection (ZSD) is the task of object detection where no visual training data is available for some of the target object classes.
( Image credit: Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts )
Benchmarks
These leaderboards are used to track progress in Zero-Shot Object Detection
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ImageNet
COCO
PASCAL VOC
PASCAL VOC 2007
Most implemented papers
Zero-Shot Instance Segmentation
zhengye1995/Zero-shot-Instance-Segmentation
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CVPR 2021
We follow this motivation and propose a new task set named zero-shot instance segmentation (ZSI).
Open-vocabulary Object Detection via Vision and Language Knowledge Distillation
On COCO, ViLD outperforms the previous state-of-the-art by 4. 8 on novel AP and 11. 4 on overall AP.
Zero-Shot Object Detection by Hybrid Region Embedding
Object detection is considered as one of the most challenging problems in computer vision, since it requires correct prediction of both classes and locations of objects in images.
Polarity Loss for Zero-shot Object Detection
salman-h-khan/PL-ZSD_Release
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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.
Learning Open-World Object Proposals without Learning to Classify
mcahny/object_localization_network
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In this paper, we identify that the problem is that the binary classifiers in existing proposal methods tend to overfit to the training categories.
Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts
We hypothesize that this setting is ill-suited for real-world applications where unseen objects appear only as a part of a complex scene, warranting both the `recognition' and `localization' of an unseen category.
GTNet: Generative Transfer Network for Zero-Shot Object Detection
X-BrainLab/GTNet
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FFU and BFU add the IoU variance to the results of CFU, yielding class-specific foreground and background features, respectively.
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.
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.
Many Heads but One Brain: an Overview of Fusion Brain Challenge on AI Journey 2021
sberbank-ai/fusion_brain_aij2021
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Supporting the current trend in the AI community, we propose the AI Journey 2021 Challenge called Fusion Brain which is targeted to make the universal architecture process different modalities (namely, images, texts, and code) and to solve multiple tasks for vision and language.
