Papers Generalized Zero-Shot Object Detection
“Generalized Zero-Shot Object Detection” 태그가 달린 논문 10편 · 필터 해제
UniFa: A unified feature hallucination framework for any-shot object detection
Any-shot object detection seeks to simultaneously detect base (many-shot), few-shot and zero-shot categories. The primary challenge lies in insufficient visual data for rare (few-shot and zero-shot) categories, hindering…
Generalized Zero-Shot Object DetectionHallucinationobject-detectionObject Detection+1Synthesizing Knowledge-enhanced Features for Real-world Zero-shot Food Detection
Food computing brings various perspectives to computer vision like vision-based food analysis for nutrition and health. As a fundamental task in food computing, food detection needs Zero-Shot Detection (ZSD) on novel uns…
AttributeGeneralized Zero-Shot Object DetectionNutritionZero-Shot Object DetectionSeeDS: Semantic Separable Diffusion Synthesizer for Zero-shot Food Detection
Food detection is becoming a fundamental task in food computing that supports various multimedia applications, including food recommendation and dietary monitoring. To deal with real-world scenarios, food detection needs…
DenoisingFood recommendationGeneralized Zero-Shot Object DetectionGeneralized Zero-Shot Object Detection on MS-COCO+2Resolving Semantic Confusions for Improved Zero-Shot Detection
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. Recently, me…
Generalized Zero-Shot Object DetectionObject DetectionTransfer LearningTriplet+2From Node to Graph: Joint Reasoning on Visual-Semantic Relational Graph for Zero-Shot Detection
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. However, scene und…
Generalized Zero-Shot Object DetectionScene UnderstandingZero-Shot Object DetectionRobust Region Feature Synthesizer for Zero-Shot Object Detection
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. In this study, we reveal the core challenges in this r…
Generalized Zero-Shot Object DetectionObjectobject-detectionObject Detection+1Semantics-Guided Contrastive Network for Zero-Shot Object detection
Zero-shot object detection (ZSD), the task that extends conventional detection models to detecting objects from unseen categories, has emerged as a new challenge in computer vision. Most existing approaches tackle the ZS…
Contrastive LearningGeneralized Zero-Shot Object DetectionObjectobject-detection+2Synthesizing the Unseen for Zero-shot Object Detection
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. However, since the unseen objec…
DiversityGeneralized Zero-Shot Object DetectionObjectObject Detection+1Background Learnable Cascade for Zero-Shot Object Detection
Zero-shot detection (ZSD) is crucial to large-scale object detection with the aim of simultaneously localizing and recognizing unseen objects. There remain several challenges for ZSD, including reducing the ambiguity bet…
Generalized Zero-Shot Object DetectionObjectobject-detectionObject Detection+2Polarity Loss for Zero-shot Object Detection
Conventional object detection models require large amounts of training data. In comparison, humans can recognize previously unseen objects by merely knowing their semantic description. To mimic similar behaviour, zero-sh…
Generalized Zero-Shot Object DetectionMetric LearningObjectobject-detection+3