# A Competence-aware Curriculum for Visual Concepts Learning via Question Answering

arXiv: https://arxiv.org/abs/2007.01499 · HTML: https://mlsanity.com/paper/2007.01499/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2020.

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

- [Competence-based Multimodal Curriculum Learning for Medical Report Generation](https://mlsanity.com/paper/2206.14579/) (2022)
- [ComPhy: Compositional Physical Reasoning of Objects and Events from Videos](https://mlsanity.com/paper/2205.01089/) (2022)
- [CLOVA: A Closed-Loop Visual Assistant with Tool Usage and Update](https://mlsanity.com/paper/2312.10908/) (2023)
- [From Easy to Hard: Learning Language-guided Curriculum for Visual Question Answering on Remote Sensing Data](https://mlsanity.com/paper/2205.03147/) (2022)
- [FALCON: Fast Visual Concept Learning by Integrating Images, Linguistic descriptions, and Conceptual Relations](https://mlsanity.com/paper/2203.16639/) (2022)
- [Calibrating Concepts and Operations: Towards Symbolic Reasoning on Real Images](https://mlsanity.com/paper/2110.00519/) (2021)
- [Grammar-Based Grounded Lexicon Learning](https://mlsanity.com/paper/2202.08806/) (2022)
- [Building Intelligent Agents with Neuro-Symbolic Concepts](https://mlsanity.com/paper/2505.06191/) (2025)
- [Interactive Visual Task Learning for Robots](https://mlsanity.com/paper/2312.13219/) (2023)
- [Exploring Curriculum Learning for Vision-Language Tasks: A Study on Small-Scale Multimodal Training](https://mlsanity.com/paper/2410.15509/) (2024)
- [Sim-To-Real Transfer of Visual Grounding for Human-Aided Ambiguity Resolution](https://mlsanity.com/paper/2205.12089/) (2022)
- [Visual Superordinate Abstraction for Robust Concept Learning](https://mlsanity.com/paper/2205.14444/) (2022)
- [Maneuver Decision-Making For Autonomous Air Combat Through Curriculum Learning And Reinforcement Learning With Sparse Rewards](https://mlsanity.com/paper/2302.05838/) (2023)
- [TPCL: Task Progressive Curriculum Learning for Robust Visual Question Answering](https://mlsanity.com/paper/2411.17292/) (2024)
- [A Concept-Centric Approach to Multi-Modality Learning](https://mlsanity.com/paper/2412.13847/) (2024)
- [Bridging the gap to real-world language-grounded visual concept learning](https://mlsanity.com/paper/2510.21412/) (2025)

## Abstract (arXiv)

Humans can progressively learn visual concepts from easy to hard questions. To mimic this efficient learning ability, we propose a competence-aware curriculum for visual concept learning in a question-answering manner. Specifically, we design a neural-symbolic concept learner for learning the visual concepts and a multi-dimensional Item Response Theory (mIRT) model for guiding the learning process with an adaptive curriculum. The mIRT effectively estimates the concept difficulty and the model competence at each learning step from accumulated model responses. The estimated concept difficulty and model competence are further utilized to select the most profitable training samples. Experimental results on CLEVR show that with a competence-aware curriculum, the proposed method achieves state-of-the-art performances with superior data efficiency and convergence speed. Specifically, the proposed model only uses 40% of training data and converges three times faster compared with other state-of-the-art methods.