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Compositional Zero-Shot Learning

Compositional Zero-Shot Learning (CZSL) is a computer vision task in which the goal is to recognize unseen compositions fromed from seen state and object during training. The key challenge in CZSL is the inherent entanglement between the state and object within the context of an image. Some example benchmarks for this task are MIT-states, UT-Zappos, and C-GQA. Models are usually evaluated with the Accuracy for both seen and unseen compositions, as well as their Harmonic Mean(HM).

( Image credit: Heosuab )

Papers

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Showing 21–30 of 65 papers

TitleStatusHype
Symmetry and Group in Attribute-Object CompositionsCode1
EVA: Mixture-of-Experts Semantic Variant Alignment for Compositional Zero-Shot Learning—0
Feasibility with Language Models for Open-World Compositional Zero-Shot Learning—0
Visual Adaptive Prompting for Compositional Zero-Shot Learning—0
Learning Primitive Relations for Compositional Zero-Shot Learning—0
Dual-Modal Prototype Joint Learning for Compositional Zero-Shot Learning—0
Separated Inter/Intra-Modal Fusion Prompts for Compositional Zero-Shot Learning—0
Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning—0
Duplex: Dual Prototype Learning for Compositional Zero-Shot Learning—0
Beyond Image Classification: A Video Benchmark and Dual-Branch Hybrid Discrimination Framework for Compositional Zero-Shot Learning—0
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