# Zero-Shot Compositional Concept Learning

arXiv: https://arxiv.org/abs/2107.05176 · HTML: https://mlsanity.com/paper/2107.05176/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 29 references that are in the map.
- First posted on arXiv 12 Jul 2021.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018)
- [Stacked Cross Attention for Image-Text Matching](https://mlsanity.com/paper/1803.08024/) (2018)
- [Attention-Based Models for Speech Recognition](https://mlsanity.com/paper/1506.07503/) (2015)
- [A Baseline for Few-Shot Image Classification](https://mlsanity.com/paper/1909.02729/) (2019)
- [Task-Driven Modular Networks for Zero-Shot Compositional Learning](https://mlsanity.com/paper/1905.05908/) (2019)
- [IMRAM: Iterative Matching with Recurrent Attention Memory for Cross-Modal Image-Text Retrieval](https://mlsanity.com/paper/2003.03772/) (2020)
- [Symmetry and Group in Attribute-Object Compositions](https://mlsanity.com/paper/2004.00587/) (2020)
- [Learning to Self-Train for Semi-Supervised Few-Shot Classification](https://mlsanity.com/paper/1906.00562/) (2019)
- [Semantic Jitter: Dense Supervision for Visual Comparisons via Synthetic Images](https://mlsanity.com/paper/1612.06341/) (2016)
- [Rethinking Zero-Shot Learning: A Conditional Visual Classification Perspective](https://mlsanity.com/paper/1909.05995/) (2019)
- [TAFE-Net: Task-Aware Feature Embeddings for Low Shot Learning](https://mlsanity.com/paper/1904.05967/) (2019)
- [Attributes as Operators: Factorizing Unseen Attribute-Object Compositions](https://mlsanity.com/paper/1803.09851/) (2018)

## Built on by (most cited first, 16 of 16)

- [Learning to Compose Soft Prompts for Compositional Zero-Shot Learning](https://mlsanity.com/paper/2204.03574/) (2022)
- [Disentangling Visual Embeddings for Attributes and Objects](https://mlsanity.com/paper/2205.08536/) (2022)
- [Learning Conditional Attributes for Compositional Zero-Shot Learning](https://mlsanity.com/paper/2305.17940/) (2023)
- [Meta Learning for Natural Language Processing: A Survey](https://mlsanity.com/paper/2205.01500/) (2022)
- [Context-based and Diversity-driven Specificity in Compositional Zero-Shot Learning](https://mlsanity.com/paper/2402.17251/) (2024)
- [Distilled Reverse Attention Network for Open-world Compositional Zero-Shot Learning](https://mlsanity.com/paper/2303.00404/) (2023)
- [TsCA: On the Semantic Consistency Alignment via Conditional Transport for Compositional Zero-Shot Learning](https://mlsanity.com/paper/2408.08703/) (2024)
- [LOCL: Learning Object-Attribute Composition using Localization](https://mlsanity.com/paper/2210.03780/) (2022)
- [Compositional Learning of Visually-Grounded Concepts Using Reinforcement](https://mlsanity.com/paper/2309.04504/) (2023)
- [Sequential Compositional Generalization in Multimodal Models](https://mlsanity.com/paper/2404.12013/) (2024)
- [Compositional Zero-Shot Learning with Contextualized Cues and Adaptive Contrastive Training](https://mlsanity.com/paper/2412.07161/) (2024)
- [Describe me an Aucklet: Generating Grounded Perceptual Category Descriptions](https://mlsanity.com/paper/2303.04053/) (2023)
- [Prompt Tuning for Zero-shot Compositional Learning](https://mlsanity.com/paper/2312.02191/) (2023)
- [Attention Based Simple Primitives for Open World Compositional Zero-Shot Learning](https://mlsanity.com/paper/2407.13715/) (2024)
- [Focus-Consistent Multi-Level Aggregation for Compositional Zero-Shot Learning](https://mlsanity.com/paper/2408.17083/) (2024)
- [Human-like compositional learning of visually-grounded concepts using synthetic environments](https://mlsanity.com/paper/2504.06618/) (2025)

## Abstract (arXiv)

In this paper, we study the problem of recognizing compositional attribute-object concepts within the zero-shot learning (ZSL) framework. We propose an episode-based cross-attention (EpiCA) network which combines merits of cross-attention mechanism and episode-based training strategy to recognize novel compositional concepts. Firstly, EpiCA bases on cross-attention to correlate concept-visual information and utilizes the gated pooling layer to build contextualized representations for both images and concepts. The updated representations are used for a more in-depth multi-modal relevance calculation for concept recognition. Secondly, a two-phase episode training strategy, especially the transductive phase, is adopted to utilize unlabeled test examples to alleviate the low-resource learning problem. Experiments on two widely-used zero-shot compositional learning (ZSCL) benchmarks have demonstrated the effectiveness of the model compared with recent approaches on both conventional and generalized ZSCL settings.