Name
Title | ||
|---|---|---|
Amplitude Spectrum Transformation for Open Compound Domain Adaptive Semantic Segmentation. |
Abstract | ||
|---|---|---|
Open compound domain adaptation (OCDA) has emerged as a practical adaptation setting which considers a single labeled source domain against a compound of multi-modal unlabeled target data in order to generalize better on novel unseen domains. We hypothesize that an improved disentanglement of domain-related and task-related factors of dense intermediate layer features can greatly aid OCDA. Prior-arts attempt this indirectly by employing adversarial domain discriminators on the spatial CNN output. However, we find that latent features derived from the Fourier-based amplitude spectrum of deep CNN features hold a more tractable mapping with domain discrimination. Motivated by this, we propose a novel feature space Amplitude Spectrum Transformation (AST). During adaptation, we employ the AST auto-encoder for two purposes. First, carefully mined source-target instance pairs undergo a simulation of cross-domain feature stylization (AST-Sim) at a particular layer by altering the AST-latent. Second, AST operating at a later layer is tasked to normalize (AST-Norm) the domain content by fixing its latent to a mean prototype. Our simplified adaptation technique is not only clustering-free but also free from complex adversarial alignment. We achieve leading performance against the prior arts on the OCDA scene segmentation benchmarks. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1609/aaai.v36i2.20008 | AAAI Conference on Artificial Intelligence |
Keywords | DocType | Citations |
Computer Vision (CV),Machine Learning (ML),Domain(s) Of Application (APP) | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jogendra Nath Kundu | 1 | 14 | 6.29 |
| Akshay Kulkarni | 2 | 0 | 1.69 |
| Suvaansh Bhambri | 3 | 0 | 1.01 |
| Varun Jampani | 4 | 184 | 19.44 |
| R. Venkatesh Babu | 5 | 1046 | 84.83 |