# Benchmarking Omni-Vision Representation through the Lens of Visual Realms

arXiv: https://arxiv.org/abs/2207.07106 · HTML: https://mlsanity.com/paper/2207.07106/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Builds on 55 of its 76 references that are in the map.
- First posted on arXiv 14 Jul 2022.

## Builds on (most cited first, 30 of 55)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [BEiT: BERT Pre-Training of Image Transformers](https://mlsanity.com/paper/2106.08254/) (2021)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [Learning to Prompt for Vision-Language Models](https://mlsanity.com/paper/2109.01134/) (2021)
- [Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination](https://mlsanity.com/paper/1805.01978/) (2018)
- [Barlow Twins: Self-Supervised Learning via Redundancy Reduction](https://mlsanity.com/paper/2103.03230/) (2021)
- [Visual Prompt Tuning](https://mlsanity.com/paper/2203.12119/) (2022)
- [Do ImageNet Classifiers Generalize to ImageNet?](https://mlsanity.com/paper/1902.10811/) (2019)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [MLP-Mixer: An all-MLP Architecture for Vision](https://mlsanity.com/paper/2105.01601/) (2021)
- [Conditional Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2203.05557/) (2022)
- [Self-training with Noisy Student improves ImageNet classification](https://mlsanity.com/paper/1911.04252/) (2019)
- [Colorful Image Colorization](https://mlsanity.com/paper/1603.08511/) (2016)
- [Deep Clustering for Unsupervised Learning of Visual Features](https://mlsanity.com/paper/1807.05520/) (2018)
- [What Makes for Good Views for Contrastive Learning?](https://mlsanity.com/paper/2005.10243/) (2020)

## Built on by (most cited first, 30 of 38)

- [Revisiting Class-Incremental Learning with Pre-Trained Models: Generalizability and Adaptivity are All You Need](https://mlsanity.com/paper/2303.07338/) (2023)
- [RanPAC: Random Projections and Pre-trained Models for Continual Learning](https://mlsanity.com/paper/2307.02251/) (2023)
- [Continual Learning with Pre-Trained Models: A Survey](https://mlsanity.com/paper/2401.16386/) (2024)
- [Expandable Subspace Ensemble for Pre-Trained Model-Based Class-Incremental Learning](https://mlsanity.com/paper/2403.12030/) (2024)
- [PILOT: A Pre-Trained Model-Based Continual Learning Toolbox](https://mlsanity.com/paper/2309.07117/) (2023)
- [HumanBench: Towards General Human-centric Perception with Projector Assisted Pretraining](https://mlsanity.com/paper/2303.05675/) (2023)
- [Assessment of Multimodal Large Language Models in Alignment with Human Values](https://mlsanity.com/paper/2403.17830/) (2024)
- [MOS: Model Surgery for Pre-Trained Model-Based Class-Incremental Learning](https://mlsanity.com/paper/2412.09441/) (2024)
- [Towards Universal Image Embeddings: A Large-Scale Dataset and Challenge for Generic Image Representations](https://mlsanity.com/paper/2309.01858/) (2023)
- [SAFE: Slow and Fast Parameter-Efficient Tuning for Continual Learning with Pre-Trained Models](https://mlsanity.com/paper/2411.02175/) (2024)
- [Mixture of Noise for Pre-Trained Model-Based Class-Incremental Learning](https://mlsanity.com/paper/2509.16738/) (2025)
- [LW2G: Learning Whether to Grow for Prompt-based Continual Learning](https://mlsanity.com/paper/2409.18860/) (2024)
- [Reflecting on the State of Rehearsal-free Continual Learning with Pretrained Models](https://mlsanity.com/paper/2406.09384/) (2024)
- [PECTP: Parameter-Efficient Cross-Task Prompts for Incremental Vision Transformer](https://mlsanity.com/paper/2407.03813/) (2024)
- [LoRanPAC: Low-rank Random Features and Pre-trained Models for Bridging Theory and Practice in Continual Learning](https://mlsanity.com/paper/2410.00645/) (2024)
- [Read Between the Layers: Leveraging Multi-Layer Representations for Rehearsal-Free Continual Learning with Pre-Trained Models](https://mlsanity.com/paper/2312.08888/) (2023)
- [Order-Robust Class Incremental Learning: Graph-Driven Dynamic Similarity Grouping](https://mlsanity.com/paper/2502.20032/) (2025)
- [FG-OrIU: Towards Better Forgetting via Feature-Gradient Orthogonality for Incremental Unlearning](https://mlsanity.com/paper/2601.13578/) (2026)
- [Premonition: Using Generative Models to Preempt Future Data Changes in Continual Learning](https://mlsanity.com/paper/2403.07356/) (2024)
- [Enhancing Pretrained Model-based Continual Representation Learning via Guided Random Projection](https://mlsanity.com/paper/2603.19145/) (2026)
- [On-Device Domain Generalization](https://mlsanity.com/paper/2209.07521/) (2022)
- [I Spy With My Little Eye: A Minimum Cost Multicut Investigation of Dataset Frames](https://mlsanity.com/paper/2412.01296/) (2024)
- [Scaling Continual Learning to 300+ Tasks with Bi-Level Routing Mixture-of-Experts](https://mlsanity.com/paper/2602.03473/) (2026)
- [Enhancing Plasticity for First Session Adaptation Continual Learning](https://mlsanity.com/paper/2310.11482/) (2023)
- [Balancing the Causal Effects in Class-Incremental Learning](https://mlsanity.com/paper/2402.10063/) (2024)
- [Boosting the Class-Incremental Learning in 3D Point Clouds via Zero-Collection-Cost Basic Shape Pre-Training](https://mlsanity.com/paper/2504.08412/) (2025)
- [Key-Value Pair-Free Continual Learner via Task-Specific Prompt-Prototype](https://mlsanity.com/paper/2601.04864/) (2026)
- [CA2: Class-Agnostic Adaptive Feature Adaptation for One-class Classification](https://mlsanity.com/paper/2309.01483/) (2023)
- [Unlocking [CLS] Features for Continual Post-Training](https://mlsanity.com/paper/2502.14762/) (2025)
- [Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse](https://mlsanity.com/paper/2504.18437/) (2025)

## Abstract (arXiv)

Though impressive performance has been achieved in specific visual realms (e.g. faces, dogs, and places), an omni-vision representation generalizing to many natural visual domains is highly desirable. But, existing benchmarks are biased and inefficient to evaluate the omni-vision representation -- these benchmarks either only include several specific realms, or cover most realms at the expense of subsuming numerous datasets that have extensive realm overlapping. In this paper, we propose Omni-Realm Benchmark (OmniBenchmark). It includes 21 realm-wise datasets with 7,372 concepts and 1,074,346 images. Without semantic overlapping, these datasets cover most visual realms comprehensively and meanwhile efficiently. In addition, we propose a new supervised contrastive learning framework, namely Relational Contrastive learning (ReCo), for a better omni-vision representation. Beyond pulling two instances from the same concept closer -- the typical supervised contrastive learning framework -- ReCo also pulls two instances from the same semantic realm closer, encoding the semantic relation between concepts, and facilitating omni-vision representation learning. We benchmark ReCo and other advances in omni-vision representation studies that are different in architectures (from CNNs to transformers) and in learning paradigms (from supervised learning to self-supervised learning) on OmniBenchmark. We illustrate the superior of ReCo to other supervised contrastive learning methods and reveal multiple practical observations to facilitate future research.