Zero-Shot Object Detection

6 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 )

Greatest papers with code

Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts

salman-h-khan/ZSD_Release 16 Mar 2018

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.

Zero-Shot Learning Zero-Shot Object Detection

Polarity Loss for Zero-shot Object Detection

salman-h-khan/PL-ZSD_Release 22 Nov 2018

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.

Metric Learning Zero-Shot Learning +1

Zero-Shot Instance Segmentation

zhengye1995/Zero-shot-Instance-Segmentation 14 Apr 2021

We follow this motivation and propose a new task set named zero-shot instance segmentation (ZSI).

Instance Segmentation Semantic Segmentation +1

Synthesizing the Unseen for Zero-shot Object Detection

nasir6/zero_shot_detection 19 Oct 2020

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.

Generalized Zero-Shot Object Detection Zero-Shot Object Detection

Zero-Shot Object Detection by Hybrid Region Embedding

berkandemirel/zero-shot-detection 16 May 2018

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.

Zero-Shot Object Detection

Background Learnable Cascade for Zero-Shot Object Detection

zhengye1995/BLC 9 Oct 2020

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.

Region Proposal Zero-Shot Object Detection