Zero-shot learning via discriminative representation extraction
                    
                        
                            نویسندگان
                            
                            
                        
                        
                    
                    
                    چکیده
منابع مشابه
Zero-Shot Relation Extraction via Reading Comprehension
We show that relation extraction can be reduced to answering simple reading comprehension questions, by associating one or more natural-language questions with each relation slot. This reduction has several advantages: we can (1) learn relationextraction models by extending recent neural reading-comprehension techniques, (2) build very large training sets for those models by combining relation-...
متن کاملZero-Shot Transfer Learning for Event Extraction
Most previous event extraction studies have relied heavily on features derived from annotated event mentions, thus cannot be applied to new event types without annotation effort. In this work, we take a fresh look at event extraction and model it as a grounding problem. We design a transferable neural architecture, mapping event mentions and types jointly into a shared semantic space using stru...
متن کاملDiscriminative Learning of Latent Features for Zero-Shot Recognition
Zero-shot learning (ZSL) aims to recognize unseen image categories by learning an embedding space between image and semantic representations. For years, among existing works, it has been the center task to learn the proper mapping matrices aligning the visual and semantic space, whilst the importance to learn discriminative representations for ZSL is ignored. In this work, we retrospect existin...
متن کاملZero-Shot Learning via Latent Space Encoding
Zero-Shot Learning (ZSL) is typically achieved by resorting to a class semantic embedding space to transfer the knowledge from the seen classes to unseen ones. Capturing the common semantic characteristics between the visual modality and the class semantic modality (e.g., attributes or word vector) is a key to the success of ZSL. In this paper, we present a novel approach called Latent Space En...
متن کاملZero-Shot Learning via Visual Abstraction
One of the main challenges in learning fine-grained visual categories is gathering training images. Recent work in Zero-Shot Learning (ZSL) circumvents this challenge by describing categories via attributes or text. However, not all visual concepts, e.g ., two people dancing, are easily amenable to such descriptions. In this paper, we propose a new modality for ZSL using visual abstraction to l...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Pattern Recognition Letters
سال: 2018
ISSN: 0167-8655
DOI: 10.1016/j.patrec.2017.09.030