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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
Leveraging MLLM Embeddings and Attribute Smoothing for Compositional Zero-Shot LearningCode1
Beyond Image Classification: A Video Benchmark and Dual-Branch Hybrid Discrimination Framework for Compositional Zero-Shot Learning—0
Context-based and Diversity-driven Specificity in Compositional Zero-Shot Learning—0
Learning Attention Propagation for Compositional Zero-Shot Learning—0
Focus-Consistent Multi-Level Aggregation for Compositional Zero-Shot Learning—0
Feasibility with Language Models for Open-World Compositional Zero-Shot Learning—0
Compositional Zero-Shot Learning with Contextualized Cues and Adaptive Contrastive Training—0
Anticipating Future Object Compositions without Forgetting—0
HOMOE: A Memory-Based and Composition-Aware Framework for Zero-Shot Learning with Hopfield Network and Soft Mixture of Experts—0
Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning—0
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