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 )

Most implemented papers

Zero-Shot Instance Segmentation

zhengye1995/Zero-shot-Instance-Segmentation 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

tensorflow/tpu ICLR 2022

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

KennithLi/Awesome-Zero-Shot-Object-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.

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.

Learning Open-World Object Proposals without Learning to Classify

mcahny/object_localization_network 15 Aug 2021

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

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.

GTNet: Generative Transfer Network for Zero-Shot Object Detection

X-BrainLab/GTNet 19 Jan 2020

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 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.

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.

Many Heads but One Brain: an Overview of Fusion Brain Challenge on AI Journey 2021

sberbank-ai/fusion_brain_aij2021 22 Nov 2021

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.