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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 26–50 of 65 papers

TitleStatusHype
Revealing the Proximate Long-Tail Distribution in Compositional Zero-Shot LearningCode0
Compositional Zero-Shot Learning for Attribute-Based Object Reference in Human-Robot Interaction—0
Prompt Tuning for Zero-shot Compositional Learning—0
Synthesize, Diagnose, and Optimize: Towards Fine-Grained Vision-Language UnderstandingCode1
Compositional Zero-shot Learning via Progressive Language-based Observations—0
HOMOE: A Memory-Based and Composition-Aware Framework for Zero-Shot Learning with Hopfield Network and Soft Mixture of Experts—0
GIPCOL: Graph-Injected Soft Prompting for Compositional Zero-Shot LearningCode0
Hierarchical Visual Primitive Experts for Compositional Zero-Shot LearningCode0
Learning Conditional Attributes for Compositional Zero-Shot LearningCode1
CAILA: Concept-Aware Intra-Layer Adapters for Compositional Zero-Shot LearningCode1
Prompting Language-Informed Distribution for Compositional Zero-Shot LearningCode1
DRPT: Disentangled and Recurrent Prompt Tuning for Compositional Zero-Shot Learning—0
Learning Attention as Disentangler for Compositional Zero-shot LearningCode1
Troika: Multi-Path Cross-Modal Traction for Compositional Zero-Shot LearningCode1
Distilled Reverse Attention Network for Open-world Compositional Zero-Shot Learning—0
ProCC: Progressive Cross-primitive Compatibility for Open-World Compositional Zero-Shot Learning—0
Decomposed Soft Prompt Guided Fusion Enhancing for Compositional Zero-Shot LearningCode1
Mutual Balancing in State-Object Components for Compositional Zero-Shot Learning—0
Simple Primitives with Feasibility- and Contextuality-Dependence for Open-World Compositional Zero-shot Learning—0
Learning Attention Propagation for Compositional Zero-Shot Learning—0
Reference-Limited Compositional Zero-Shot LearningCode0
Siamese Contrastive Embedding Network for Compositional Zero-Shot LearningCode1
Learning Invariant Visual Representations for Compositional Zero-Shot LearningCode0
Disentangling Visual Embeddings for Attributes and ObjectsCode1
KG-SP: Knowledge Guided Simple Primitives for Open World Compositional Zero-Shot LearningCode1
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