mlsanityData to 7 Oct 2026

Unified Framework for Open-World Compositional Zero-shot Learning

arXiv 2412.04083 ↗First posted 5 Dec 2024cs.CVcited by 1

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Abstract (arXiv)

Open-World Compositional Zero-Shot Learning (OW-CZSL) addresses the challenge of recognizing novel compositions of known primitives and entities. Even though prior works utilize language knowledge for recognition, such approaches exhibit limited interactions between language-image modalities. Our approach primarily focuses on enhancing the inter-modality interactions through fostering richer interactions between image and textual data. Additionally, we introduce a novel module aimed at alleviating the computational burden associated with exhaustive exploration of all possible compositions during the inference stage. While previous methods exclusively learn compositions jointly or independently, we introduce an advanced hybrid procedure that leverages both learning mechanisms to generate final predictions. Our proposed model, achieves state-of-the-art in OW-CZSL in three datasets, while surpassing Large Vision Language Models (LLVM) in two datasets.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 34 of its 46 references that are in the map. First posted on arXiv 5 Dec 2024.

Builds on

34 of 46 references.

References · 34

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22 more
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  16. 2022KG-SP: Knowledge Guided Simple Primitives for Open World Compositional Zero-Shot Learningcited by 41
  17. 2023Learning Attention as Disentangler for Compositional Zero-shot Learningcited by 29
  18. 2023Learning Conditional Attributes for Compositional Zero-Shot Learningcited by 28
  19. 2021Relation-aware Compositional Zero-shot Learning for Attribute-Object Pair Recognitioncited by 19
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Cited by year

Citing papers per year: 2025: 1.

1 paper.

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  1. 2023Prompting Language-Informed Distribution for Compositional Zero-Shot Learning
  2. 2025Learning Visual Proxy for Compositional Zero-Shot Learning
  3. 2023CAILA: Concept-Aware Intra-Layer Adapters for Compositional Zero-Shot Learning
  4. 2023Prompt Tuning for Zero-shot Compositional Learning
  5. 2023Troika: Multi-Path Cross-Modal Traction for Compositional Zero-Shot Learning
  6. 2023Compositional Zero-shot Learning via Progressive Language-based Observations
  7. 2025Separated Inter/Intra-Modal Fusion Prompts for Compositional Zero-Shot Learning
  8. 2026DIFFCZSL: Compositional Zero-Shot Learning Regularized by Diffusion Representations