Search Results for author:
Found 7 papers, 4 papers with code
Bootstrap Your Own Prior: Towards Distribution-Agnostic Novel Class Discovery
Novel Class Discovery (NCD) aims to discover unknown classes without any annotation, by exploiting the transferable knowledge already learned from a base set of known classes.
Siamese Contrastive Embedding Network for Compositional Zero-Shot Learning
Some methods recognize state and object with two trained classifiers, ignoring the impact of the interaction between object and state; the other methods try to learn the joint representation of the state-object compositions, leading to the domain gap between seen and unseen composition sets.
Divide and Conquer: Compositional Experts for Generalized Novel Class Discovery
In response to the explosively-increasing requirement of annotated data, Novel Class Discovery (NCD) has emerged as a promising alternative to automatically recognize unknown classes without any annotation.
Fewer is More: A Deep Graph Metric Learning Perspective Using Fewer Proxies
In this paper, we propose a novel Proxy-based deep Graph Metric Learning (ProxyGML) approach from the perspective of graph classification, which uses fewer proxies yet achieves better comprehensive performance.
Learning Unseen Concepts via Hierarchical Decomposition and Composition
To model intricate contextuality between sub-concepts and their visual features, compositions are generated from these subspaces in three hierarchical forms, and the composed concepts are learned in a unified composition space.
Progressive Domain-Independent Feature Decomposition Network for Zero-Shot Sketch-Based Image Retrieval
Specifically, with the supervision of original semantic knowledge, PDFD decomposes visual features into domain features and semantic ones, and then the semantic features are projected into common space as retrieval features for ZS-SBIR.
Fully-Featured Attribute Transfer
Image attribute transfer aims to change an input image to a target one with expected attributes, which has received significant attention in recent years.
