Papers › Depthwise Convolution is All You Need for Learning Multiple Visual Domains
Depthwise Convolution is All You Need for Learning Multiple Visual Domains
Yunhui Guo, Yandong Li, Rogerio Feris, Liqiang Wang, Tajana Rosing
There is a growing interest in designing models that can deal with images from different visual domains. If there exists a universal structure in different visual domains that can be captured via a common parameterization, then we can use a single model for all domains rather than one model per domain. A model aware of the relationships between different domains can also be trained to work on new domains with less resources. However, to identify the reusable structure in a model is not easy. In this paper, we propose a multi-domain learning architecture based on depthwise separable convolution. The proposed approach is based on the assumption that images from different domains share cross-channel correlations but have domain-specific spatial correlations. The proposed model is compact and has minimal overhead when being applied to new domains. Additionally, we introduce a gating mechanism to promote soft sharing between different domains. We evaluate our approach on Visual Decathlon Challenge, a benchmark for testing the ability of multi-domain models. The experiments show that our approach can achieve the highest score while only requiring 50% of the parameters compared with the state-of-the-art approaches.
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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) | Depthwise Soft Sharing | decathlon discipline (Score) | 3507 | #2 of 14 | Archive leaderboard | report |
| Continual Learning | visual domain decathlon (10 tasks) | Depthwise Sharing | decathlon discipline (Score) | 3234 | #4 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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