Search Results for author:
Found 7 papers, 2 papers with code
Meta-tuning Loss Functions and Data Augmentation for Few-shot Object Detection
Few-shot object detection, the problem of modelling novel object detection categories with few training instances, is an emerging topic in the area of few-shot learning and object detection.
Caption Generation on Scenes with Seen and Unseen Object Categories
For this problem, we propose a detection-driven approach that consists of a single-stage generalized zero-shot detection model to recognize and localize instances of both seen and unseen classes, and a template-based captioning model that transforms detections into sentences.
Image Captioning with Unseen Objects
Image caption generation is a long standing and challenging problem at the intersection of computer vision and natural language processing.
Segmentation-Aware Hyperspectral Image Classification
In this paper, we propose an unified hyperspectral image classification method which takes three-dimensional hyperspectral data cube as an input and produces a classification map.
Learning Visually Consistent Label Embeddings for Zero-Shot Learning
In this work, we propose a zero-shot learning method to effectively model knowledge transfer between classes via jointly learning visually consistent word vectors and label embedding model in an end-to-end manner.
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.
Ranked #7 on
Zero-Shot Object Detection
on PASCAL VOC'07
Attributes2Classname: A discriminative model for attribute-based unsupervised zero-shot learning
We propose a novel approach for unsupervised zero-shot learning (ZSL) of classes based on their names.
