Generalized Zero-Shot Recognition based on Visually Semantic Embedding
We propose a novel Generalized Zero-Shot learning (GZSL) method that is agnostic to both unseen images and unseen semantic vectors during training. Prior works in this context propose to map high-dimensional visual features to the semantic domain, we believe contributes to the semantic gap. To bridge the gap, we propose a novel low-dimensional embedding of visual instances that is "visually semantic." Analogous to semantic data that quantifies the existence of an attribute in the presented instance, components of our visual embedding quantifies existence of a prototypical part-type in the presented instance. In parallel, as a thought experiment, we quantify the impact of noisy semantic data by utilizing a novel visual oracle to visually supervise a learner. These factors, namely semantic noise, visual-semantic gap and label noise lead us to propose a new graphical model for inference with pairwise interactions between label, semantic data, and inputs. We tabulate results on a number of benchmark datasets demonstrating significant improvement in accuracy over state-of-the-art under both semantic and visual supervision.
Code (0)
등록된 구현이 없습니다.
Tasks
AttributeGeneralized Zero-Shot LearningZero-Shot LearningSimilar Papers 제목 키워드 기반
SkeletonContext: Skeleton-side Context Prompt Learning for Zero-Shot Skeleton-based Action Recognition
Zero-shot skeleton-based action recognition aims to recognize unseen actions by transferring knowledge from seen categories through semantic descriptions. Most existing methods typically align skeleton features with text…
Action UnderstandingAction RecognitionFine-Grained Generalized Zero-Shot Learning via Dense Attribute-Based Attention
We address the problem of fine-grained generalized zero-shot recognition of visually similar classes without training images for some classes. We propose a dense attribute-based attention mechanism that for each attribut…
AttributeGeneralized Zero-Shot LearningZero-Shot LearningLearning the Redundancy-free Features for Generalized Zero-Shot Object Recognition
Zero-shot object recognition or zero-shot learning aims to transfer the object recognition ability among the semantically related categories, such as fine-grained animal or bird species. However, the images of different …
Generalized Zero-Shot LearningObjectObject RecognitionZero-Shot LearningVisual and Semantic Prompt Collaboration for Generalized Zero-Shot Learning
Generalized zero-shot learning aims to recognize both seen and unseen classes with the help of semantic information that is shared among different classes. It inevitably requires consistent visual-semantic alignment. Exi…
Generalized Zero-Shot LearningZero-Shot LearningLearning 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. The ma…
Transfer LearningZero-Shot Learning