2022年3月20日 · Our model visually divides a set of images from seen classes into clusters of local image regions according to their visual similarity, and further imposes their class …

We propose to discover semantic embeddings containing discriminative visual properties for zero-shot learning, without requiring any human annotation.

2022年6月29日 · 为了充分挖掘不同类别之间共享的视觉特征，vgse 模型将大量局部图像切片按其视觉相似度聚类形成属性簇，从图像底层特征中归纳不同类别实例所共享的视觉特征。

To this end, we propose the Visually-Grounded Semantic Embedding (VGSE) Network to discover semantic embeddings with minimal human supervision (we only use category labels for seen …

To this end, we propose the Visually-Grounded Semantic Embedding (VGSE) Network to discover semantic embeddings with minimal human supervision (we only use category labels for seen …

Embedding (VGSE) Network to discover semantic embed-dings with minimal human supervision (we only use cate-gory labels for seen class images). Our network explicitly explores visual …

2022年6月29日 · 针对以上问题，来自北京邮电大学、马普所等机构的研究者提出了类别嵌入发掘网络（Visually-Grounded Semantic Embedding Network, VGSE），本文主要回答了两个问 …

2022年6月1日 · We achieve this with our proposed Image-free Classifier Injection with Semantics (ICIS) that injects classifiers for new, unseen classes into pre-trained classification models in a …

Our model visually divides a set of images from seen classes into clusters of local image regions according to their visual similarity, and further imposes their class discrimination and semantic …

Our model visually divides a set of images from seen classes into clusters of local image regions according to their visual similarity, and further imposes their class discrimination and semantic …