GTNet: Generative Transfer Network for Zero-Shot Object Detection
We propose a Generative Transfer Network (GTNet) for zero shot object detection (ZSD). GTNet consists of an Object Detection Module and a Knowledge Transfer Module. The Object Detection Module can learn large-scale seen domain knowledge. The Knowledge Transfer Module leverages a feature synthesizer to generate unseen class features, which are applied to train a new classification layer for the Object Detection Module. In order to synthesize features for each unseen class with both the intra-class variance and the IoU variance, we design an IoU-Aware Generative Adversarial Network (IoUGAN) as the feature synthesizer, which can be easily integrated into GTNet. Specifically, IoUGAN consists of three unit models: Class Feature Generating Unit (CFU), Foreground Feature Generating Unit (FFU), and Background Feature Generating Unit (BFU). CFU generates unseen features with the intra-class variance conditioned on the class semantic embeddings. FFU and BFU add the IoU variance to the results of CFU, yielding class-specific foreground and background features, respectively. We evaluate our method on three public datasets and the results demonstrate that our method performs favorably against the state-of-the-art ZSD approaches.
Code (1)
Tasks
Generative Adversarial NetworkObjectobject-detectionObject DetectionTransfer LearningZero-Shot Object DetectionSimilar Papers 제목 키워드 기반
Review of Zero-Shot and Few-Shot AI Algorithms in The Medical Domain
In this paper, different techniques of few-shot, zero-shot, and regular object detection have been investigated. The need for few-shot learning and zero-shot learning techniques is crucial and arises from the limitations…
Few-Shot Learningobject-detectionObject DetectionZero-Shot LearningEfficient Remote Sensing Segmentation With Generative Adversarial Transformer
Most deep learning methods that achieve high segmentation accuracy require deep network architectures that are too heavy and complex to run on embedded devices with limited storage and memory space. To address this issue…
SegmentationSemantic SegmentationGTNet:Guided Transformer Network for Detecting Human-Object Interactions
The human-object interaction (HOI) detection task refers to localizing humans, localizing objects, and predicting the interactions between each human-object pair. HOI is considered one of the fundamental steps in truly u…
Human-Object Interaction DetectionObjectGenSF: Simultaneous Adaptation of Generative Pre-trained Models and Slot Filling
In transfer learning, it is imperative to achieve strong alignment between a pre-trained model and a downstream task. Prior work has done this by proposing task-specific pre-training objectives, which sacrifices the inhe…
Open-Domain Dialogslot-fillingSlot FillingTransfer Learning+1PGTNet: A Process Graph Transformer Network for Remaining Time Prediction of Business Process Instances
We present PGTNet, an approach that transforms event logs into graph datasets and leverages graph-oriented data for training Process Graph Transformer Networks to predict the remaining time of business process instances.…
Deep Learning