7 citations · 12 across the 10 of their papers we have counts for
7 papers · 1 filter
Epsilon: Exploring Comprehensive Visual-Semantic Projection for Multi-Label Zero-Shot Learning
Ziming Liu, Jingcai Guo, Song Guo +1
This paper investigates a challenging problem of zero-shot learning in the multi-label scenario (MLZSL), wherein the model is trained to recognize multiple unseen classes within a…
ParsNets: A Parsimonious Orthogonal and Low-Rank Linear Networks for Zero-Shot Learning
Jingcai Guo, Qihua Zhou, Ruibing Li +5
This paper provides a novel parsimonious yet efficient design for zero-shot learning (ZSL), dubbed ParsNets, where we are interested in learning a composition of on-device friendly…
GBE-MLZSL: A Group Bi-Enhancement Framework for Multi-Label Zero-Shot Learning
Ziming Liu, Jingcai Guo, Xiaocheng Lu +3
This paper investigates a challenging problem of zero-shot learning in the multi-label scenario (MLZSL), wherein, the model is trained to recognize multiple unseen classes within a…
DRPT: Disentangled and Recurrent Prompt Tuning for Compositional Zero-Shot Learning
Xiaocheng Lu, Ziming Liu, Song Guo +4
Compositional Zero-shot Learning (CZSL) aims to recognize novel concepts composed of known knowledge without training samples. Standard CZSL either identifies visual primitives or…
Decomposed Soft Prompt Guided Fusion Enhancing for Compositional Zero-Shot Learning
Xiaocheng Lu, Ziming Liu, Song Guo +1
Compositional Zero-Shot Learning (CZSL) aims to recognize novel concepts formed by known states and objects during training. Existing methods either learn the combined state-object…
ProCC: Progressive Cross-primitive Compatibility for Open-World Compositional Zero-Shot Learning
Fushuo Huo, Wenchao Xu, Song Guo +4
Open-World Compositional Zero-shot Learning (OW-CZSL) aims to recognize novel compositions of state and object primitives in images with no priors on the compositional space, which…
Towards Unbiased Multi-label Zero-Shot Learning with Pyramid and Semantic Attention
Ziming Liu, Song Guo, Jingcai Guo +2
Multi-label zero-shot learning extends conventional single-label zero-shot learning to a more realistic scenario that aims at recognizing multiple unseen labels of classes for each…