# Coverage-aware Semantic Representation Learning for Underrepresented Visual Concepts

arXiv: https://arxiv.org/abs/2602.16917 · HTML: https://mlsanity.com/paper/2602.16917/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 34 of its 45 references that are in the map.
- First posted on arXiv 18 Feb 2026.

## Builds on (most cited first, 30 of 34)

- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [DINOv3](https://mlsanity.com/paper/2508.10104/) (2025)
- [Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization](https://mlsanity.com/paper/1911.08731/) (2019)
- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [Concept Bottleneck Models](https://mlsanity.com/paper/2007.04612/) (2020)
- [Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss](https://mlsanity.com/paper/1906.07413/) (2019)
- [Decoupling Representation and Classifier for Long-Tailed Recognition](https://mlsanity.com/paper/1910.09217/) (2019)
- [Long-tail learning via logit adjustment](https://mlsanity.com/paper/2007.07314/) (2020)
- [Just Train Twice: Improving Group Robustness without Training Group Information](https://mlsanity.com/paper/2107.09044/) (2021)
- [Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations](https://mlsanity.com/paper/2204.02937/) (2022)
- [Post-hoc Concept Bottleneck Models](https://mlsanity.com/paper/2205.15480/) (2022)
- [Label-Free Concept Bottleneck Models](https://mlsanity.com/paper/2304.06129/) (2023)
- [The Majority Can Help The Minority: Context-rich Minority Oversampling for Long-tailed Classification](https://mlsanity.com/paper/2112.00412/) (2021)
- [VLG-CBM: Training Concept Bottleneck Models with Vision-Language Guidance](https://mlsanity.com/paper/2408.01432/) (2024)
- [A Decade's Battle on Dataset Bias: Are We There Yet?](https://mlsanity.com/paper/2403.08632/) (2024)
- [Understanding Imbalanced Semantic Segmentation Through Neural Collapse](https://mlsanity.com/paper/2301.01100/) (2023)
- [Invariant Feature Learning for Generalized Long-Tailed Classification](https://mlsanity.com/paper/2207.09504/) (2022)
- [Semi-supervised Concept Bottleneck Models](https://mlsanity.com/paper/2406.18992/) (2024)
- [Explain via Any Concept: Concept Bottleneck Model with Open Vocabulary Concepts](https://mlsanity.com/paper/2408.02265/) (2024)
- [Aligning Human Knowledge with Visual Concepts Towards Explainable Medical Image Classification](https://mlsanity.com/paper/2406.05596/) (2024)
- [DCBM: Data-Efficient Visual Concept Bottleneck Models](https://mlsanity.com/paper/2412.11576/) (2024)
- [Understanding Bias in Large-Scale Visual Datasets](https://mlsanity.com/paper/2412.01876/) (2024)
- [A Textbook Remedy for Domain Shifts: Knowledge Priors for Medical Image Analysis](https://mlsanity.com/paper/2405.14839/) (2024)
- [Show and Tell: Visually Explainable Deep Neural Nets via Spatially-Aware Concept Bottleneck Models](https://mlsanity.com/paper/2502.20134/) (2025)
- [Discovering Fine-Grained Visual-Concept Relations by Disentangled Optimal Transport Concept Bottleneck Models](https://mlsanity.com/paper/2505.07209/) (2025)
- [Interactive Medical Image Analysis with Concept-based Similarity Reasoning](https://mlsanity.com/paper/2503.06873/) (2025)
- [Long-tailed Recognition with Model Rebalancing](https://mlsanity.com/paper/2510.08177/) (2025)
- [Mitigating Spurious Correlation via Distributionally Robust Learning with Hierarchical Ambiguity Sets](https://mlsanity.com/paper/2510.02818/) (2025)
- [Addressing Concept Mislabeling in Concept Bottleneck Models Through Preference Optimization](https://mlsanity.com/paper/2504.18026/) (2025)
- [Uncovering Conceptual Blindspots in Generative Image Models Using Sparse Autoencoders](https://mlsanity.com/paper/2506.19708/) (2025)

## Abstract (arXiv)

Modern vision models increasingly rely on rich semantic representations that extend beyond class labels to include descriptive concepts, attributes, and contextual cues. However, semantic concepts are not uniformly represented across classes: a concept may be frequent globally yet remain underrepresented within a class, resulting in low class-concept coverage. We formalize this class-concept phenomenon as Semantic Coverage Imbalance (SCI) and study its relationship with learned semantic representations. We find that lower class-concept coverage is consistently associated with weaker semantic representations. To address this, we introduce Coverage-aware Semantic Representation Learning, which shares more semantic structure for low coverage or uncertain relations while preserving more pair-specific structure for high coverage relations. We instantiate this principle in SemCovNet with Coverage-Calibrated Semantic Sharing (CCSS), a coverage- and uncertainty-aware partial-pooling mechanism. Using a range of vision datasets spanning facial attributes and fine-grained recognition, as well as real-world medical datasets, we show that SCI is widespread, degrades representation quality, and can be mitigated through coverage-aware semantic representation learning.