mlsanityData to 7 Oct 2026

Learning multiple visual domains with residual adapters

arXiv 1705.08045 ↗First posted 22 May 2017cs.CV stat.MLcited by 553

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Cited by 22 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

There is a growing interest in learning data representations that work well for many different types of problems and data. In this paper, we look in particular at the task of learning a single visual representation that can be successfully utilized in the analysis of very different types of images, from dog breeds to stop signs and digits. Inspired by recent work on learning networks that predict the parameters of another, we develop a tunable deep network architecture that, by means of adapter residual modules, can be steered on the fly to diverse visual domains. Our method achieves a high degree of parameter sharing while maintaining or even improving the accuracy of domain-specific representations. We also introduce the Visual Decathlon Challenge, a benchmark that evaluates the ability of representations to capture simultaneously ten very different visual domains and measures their ability to recognize well uniformly.

Key facts. Cited by 553 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 May 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 76; 2022: 102; 2023: 147; 2024: 112; 2025: 74; 2026: 42.

553 papers.

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  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  2. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,878
  3. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,769
  4. 2022Visual Prompt Tuningcited by 1,674
  5. 2022Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learningcited by 886
  6. 2023LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attentioncited by 776
  7. 2021Domain Generalization: A Surveycited by 760
  8. 2021MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classificationcited by 682
  9. 2022Vision Transformer Adapter for Dense Predictionscited by 564
  10. 2021Image Segmentation Using Text and Image Promptscited by 509
  11. 2021A Survey of Transformerscited by 399
  12. 2023SVDiff: Compact Parameter Space for Diffusion Fine-Tuningcited by 338
38 more
  1. 2023AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuningcited by 337
  2. 2021VL-Adapter: Parameter-Efficient Transfer Learning for Vision-and-Language Taskscited by 319
  3. 2021DyTox: Transformers for Continual Learning with DYnamic TOken eXpansioncited by 291
  4. 2024MM-LLMs: Recent Advances in MultiModal Large Language Modelscited by 277
  5. 2023Personalize Segment Anything Model with One Shotcited by 250
  6. 2024Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimizationcited by 238
  7. 2022LST: Ladder Side-Tuning for Parameter and Memory Efficient Transfer Learningcited by 228
  8. 2023Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessmentcited by 227
  9. 2021Training Neural Networks with Fixed Sparse Maskscited by 212
  10. 2023Class-Incremental Learning: A Surveycited by 209
  11. 2022ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learningcited by 192
  12. 2023Revisiting Class-Incremental Learning with Pre-Trained Models: Generalizability and Adaptivity are All You Needcited by 183
  13. 2022Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Modelscited by 179
  14. 2023Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuningcited by 171
  15. 2021Noisy Channel Language Model Prompting for Few-Shot Text Classificationcited by 163
  16. 2021Pretrained Transformers as Universal Computation Enginescited by 162
  17. 2022A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunitiescited by 162
  18. 2022LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Taskscited by 146
  19. 2022Lifting the Curse of Multilinguality by Pre-training Modular Transformerscited by 145
  20. 2021Composable Sparse Fine-Tuning for Cross-Lingual Transfercited by 144
  21. 2023Prompting Large Language Models with Speech Recognition Abilitiescited by 139
  22. 2022Efficient Few-Shot Learning Without Promptscited by 135
  23. 2023Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Surveycited by 134
  24. 2022FacT: Factor-Tuning for Lightweight Adaptation on Vision Transformercited by 133
  25. 2023A Unified Continual Learning Framework with General Parameter-Efficient Tuningcited by 124
  26. 2021MSeg: A Composite Dataset for Multi-domain Semantic Segmentationcited by 122
  27. 2022Self-Sustaining Representation Expansion for Non-Exemplar Class-Incremental Learningcited by 118
  28. 2022Federated Learning with Partial Model Personalizationcited by 114
  29. 2023Parameter-Efficient Orthogonal Finetuning via Butterfly Factorizationcited by 112
  30. 2024Low-Rank Few-Shot Adaptation of Vision-Language Modelscited by 103
  31. 2024Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resourcescited by 93
  32. 2023Soft Merging of Experts with Adaptive Routingcited by 88
  33. 2023AdapterSoup: Weight Averaging to Improve Generalization of Pretrained Language Modelscited by 84
  34. 2022Head2Toe: Utilizing Intermediate Representations for Better Transfer Learningcited by 83
  35. 2023Modular Deep Learningcited by 82
  36. 2023Efficient Emotional Adaptation for Audio-Driven Talking-Head Generationcited by 81
  37. 2021Cross-domain Few-shot Learning with Task-specific Adapterscited by 79
  38. 2022Efficient Methods for Natural Language Processing: A Surveycited by 78

50 most cited of 553 All 553 (JSON)