mlsanityData to 14 Oct 2026

Separated Inter/Intra-Modal Fusion Prompts for Compositional Zero-Shot Learning

arXiv 2501.17171 ↗First posted 22 Jan 2025cs.CV cs.AI cs.LGcited by 0

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Abstract (arXiv)

Compositional Zero-Shot Learning (CZSL) aims to recognize subtle differences in meaning or the combination of states and objects through the use of known and unknown concepts during training. Existing methods either focused on prompt configuration or on using prompts to tune the pre-trained Vision-Language model. However, these methods faced challenges in accurately identifying subtle differences in meaning or combining states with objects. To jointly eradicate the above issues and construct an efficient and effective CZSL technique, we suggest a method to improve attribute recognition performance by utilizing diverse Prompt Learning with an Inter/Intra-Modality Fusion Synthesizer in scene understanding involving subtle semantic differences and multiple objects.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 20 of its 25 references that are in the map. First posted on arXiv 22 Jan 2025.

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20 of 25 references.

References · 20

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2022Visual Prompt Tuningcited by 1,681
  4. 2022Conditional Prompt Learning for Vision-Language Modelscited by 1,433
  5. 2023What does CLIP know about a red circle? Visual prompt engineering for VLMscited by 218
  6. 2016Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Modelscited by 213
  7. 2019Task-Driven Modular Networks for Zero-Shot Compositional Learningcited by 121
  8. 2021Learning Graph Embeddings for Compositional Zero-shot Learningcited by 112
  9. 2021Open World Compositional Zero-Shot Learningcited by 101
  10. 2020Symmetry and Group in Attribute-Object Compositionscited by 89
  11. 2021On Transferability of Prompt Tuning for Natural Language Processingcited by 75
  12. 2022Learning to Compose Soft Prompts for Compositional Zero-Shot Learningcited by 67
8 more
  1. 2020Multi-Head Attention: Collaborate Instead of Concatenatecited by 60
  2. 2022Disentangling Visual Embeddings for Attributes and Objectscited by 55
  3. 2022Siamese Contrastive Embedding Network for Compositional Zero-Shot Learningcited by 55
  4. 2021Learning Graph Embeddings for Open World Compositional Zero-Shot Learningcited by 49
  5. 2022KG-SP: Knowledge Guided Simple Primitives for Open World Compositional Zero-Shot Learningcited by 41
  6. 2022Decomposed Soft Prompt Guided Fusion Enhancing for Compositional Zero-Shot Learningcited by 37
  7. 2023Detecting Any Human-Object Interaction Relationship: Universal HOI Detector with Spatial Prompt Learning on Foundation Modelscited by 34
  8. 2024Open-World Human-Object Interaction Detection via Multi-modal Promptscited by 28

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  1. 2023Prompting Language-Informed Distribution for Compositional Zero-Shot Learning
  2. 2023CAILA: Concept-Aware Intra-Layer Adapters for Compositional Zero-Shot Learning
  3. 2024Unified Framework for Open-World Compositional Zero-shot Learning
  4. 2025Learning Visual Proxy for Compositional Zero-Shot Learning
  5. 2023Troika: Multi-Path Cross-Modal Traction for Compositional Zero-Shot Learning
  6. 2023Prompt Tuning for Zero-shot Compositional Learning
  7. 2022Prompting Large Pre-trained Vision-Language Models For Compositional Concept Learning
  8. 2025Prompt-Based Continual Compositional Zero-Shot Learning