Papers › Learning multiple visual domains with residual adapters
Learning multiple visual domains with residual adapters
Sylvestre-Alvise Rebuffi, Hakan Bilen, Andrea Vedaldi
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.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Continual Learning | visual domain decathlon (10 tasks) | Res. adapt. (large) | decathlon discipline (Score) | 3131 | #6 of 14 | Archive leaderboard | report |
| Continual Learning | visual domain decathlon (10 tasks) | Res. adapt. finetune all | decathlon discipline (Score) | 2643 | #9 of 14 | Archive leaderboard | report |
| Continual Learning | visual domain decathlon (10 tasks) | Res. adapt. decay | decathlon discipline (Score) | 2621 | #10 of 14 | Archive leaderboard | report |
| Continual Learning | visual domain decathlon (10 tasks) | Res. adapt. dom-pred | decathlon discipline (Score) | 2503 | #12 of 14 | Archive leaderboard | report |
| Continual Learning | visual domain decathlon (10 tasks) | Res. adapt. | decathlon discipline (Score) | 2118 | #13 of 14 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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