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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 1–50 of 65 papers

TitleStatusHype
BatchFormer: Learning to Explore Sample Relationships for Robust Representation LearningCode2
Disentangling Visual Embeddings for Attributes and ObjectsCode1
Siamese Contrastive Embedding Network for Compositional Zero-Shot LearningCode1
Prompting Language-Informed Distribution for Compositional Zero-Shot LearningCode1
A causal view of compositional zero-shot recognitionCode1
Open World Compositional Zero-Shot LearningCode1
KG-SP: Knowledge Guided Simple Primitives for Open World Compositional Zero-Shot LearningCode1
Learning Attention as Disentangler for Compositional Zero-shot LearningCode1
Troika: Multi-Path Cross-Modal Traction for Compositional Zero-Shot LearningCode1
Learning Clustering-based Prototypes for Compositional Zero-shot LearningCode1
Decomposed Soft Prompt Guided Fusion Enhancing for Compositional Zero-Shot LearningCode1
Learning Graph Embeddings for Compositional Zero-shot LearningCode1
Learning Graph Embeddings for Open World Compositional Zero-Shot LearningCode1
MSCI: Addressing CLIP's Inherent Limitations for Compositional Zero-Shot LearningCode1
CAILA: Concept-Aware Intra-Layer Adapters for Compositional Zero-Shot LearningCode1
Learning Single/Multi-Attribute of Object with Symmetry and GroupCode1
Learning to Compose Soft Prompts for Compositional Zero-Shot LearningCode1
Leveraging MLLM Embeddings and Attribute Smoothing for Compositional Zero-Shot LearningCode1
Symmetry and Group in Attribute-Object CompositionsCode1
Synthesize, Diagnose, and Optimize: Towards Fine-Grained Vision-Language UnderstandingCode1
Learning Conditional Attributes for Compositional Zero-Shot LearningCode1
Learning Primitive Relations for Compositional Zero-Shot Learning—0
Logical Activation Functions: Logit-space equivalents of Probabilistic Boolean Operators—0
LOGICZSL: Exploring Logic-induced Representation for Compositional Zero-shot Learning—0
MAC: A Benchmark for Multiple Attributes Compositional Zero-Shot Learning—0
Visual Adaptive Prompting for Compositional Zero-Shot Learning—0
Anticipating Future Object Compositions without Forgetting—0
Beyond Image Classification: A Video Benchmark and Dual-Branch Hybrid Discrimination Framework for Compositional Zero-Shot Learning—0
Beyond Seen Primitive Concepts and Attribute-Object Compositional Learning—0
Compositional Zero-Shot Learning for Attribute-Based Object Reference in Human-Robot Interaction—0
Compositional Zero-shot Learning via Progressive Language-based Observations—0
Compositional Zero-Shot Learning via Fine-Grained Dense Feature Composition—0
Compositional Zero-Shot Learning with Contextualized Cues and Adaptive Contrastive Training—0
Context-based and Diversity-driven Specificity in Compositional Zero-Shot Learning—0
Cross-composition Feature Disentanglement for Compositional Zero-shot Learning—0
CSCNET: Class-Specified Cascaded Network for Compositional Zero-Shot Learning—0
Distilled Reverse Attention Network for Open-world Compositional Zero-Shot Learning—0
DRPT: Disentangled and Recurrent Prompt Tuning for Compositional Zero-Shot Learning—0
Dual-Modal Prototype Joint Learning for Compositional Zero-Shot Learning—0
Duplex: Dual Prototype Learning for Compositional Zero-Shot Learning—0
EVA: Mixture-of-Experts Semantic Variant Alignment for Compositional Zero-Shot Learning—0
Exploring Transferable Homogeneous Groups for Compositional Zero-Shot Learning—0
Feasibility with Language Models for Open-World Compositional Zero-Shot Learning—0
Focus-Consistent Multi-Level Aggregation for Compositional Zero-Shot Learning—0
HOMOE: A Memory-Based and Composition-Aware Framework for Zero-Shot Learning with Hopfield Network and Soft Mixture of Experts—0
Learning Attention Propagation for Compositional Zero-Shot Learning—0
Mutual Balancing in State-Object Components for Compositional Zero-Shot Learning—0
On Leveraging Variational Graph Embeddings for Open World Compositional Zero-Shot Learning—0
ProCC: Progressive Cross-primitive Compatibility for Open-World Compositional Zero-Shot Learning—0
Prompt Tuning for Zero-shot Compositional Learning—0
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