When Source-Free Domain Adaptation Meets Label Propagation

Source-free domain adaptation, where only a pre-trained source model is used to adapt to the target distribution, is a more general approach to achieving domain adaptation. However, it can be challenging to capture the inherent structure of the target features accurately due to the lack of supervised information on the target domain. To tackle this problem, we propose a novel approach called Adaptive Local Transfer (ALT) that tries to achieve efficient feature clustering from the perspective of label propagation. ALT divides the target data into inner and outlier samples based on the adaptive threshold of the learning state, and applies a customized learning strategy to best fits the data property. Specifically, inner samples are utilized for learning intra-class structure thanks to their relatively well-clustered properties. The low-density outlier samples are regularized by input consistency to achieve high accuracy with respect to the ground truth labels. In this way, local clustering can be prevented from forming spurious clusters while effectively propagating label information among subpopulations. Empirical evidence demonstrates that ALT outperforms the state of the arts on three public benchmarks: Office-31, Office-Home, and VisDA.

Semi-Supervised Representative Region Texture Extraction of Façade

Researches of analysis and parsing around fa\c{c}ades to enrich the 3D feature of fa\c{c}ade models by semantic information raised some attention in the community, whose main idea is to generate higher resolution components with similar shapes and textures to increase the overall resolution at the expense of reconstruction accuracy. While this approach works well for components like windows and doors, there is no solution for fa\c{c}ade background at present. In this paper, we introduce the concept of representative region texture, which can be used in the above modeling approach by tiling the representative texture around the fa\c{c}ade region, and propose a semi-supervised way to do representative region texture extraction from a fa\c{c}ade image. Our method does not require any additional labelled data to train as long as the semantic information is given, while a traditional end-to-end model requires plenty of data to increase its performance. Our method can extract texture from any repetitive images, not just fa\c{c}ade, which is not capable in an end-to-end model as it relies on the distribution of training set. Clustering with weighted distance is introduced to further increase the robustness to noise or an imprecise segmentation, and make the extracted texture have a higher resolution and more suitable for tiling. We verify our method on various fa\c{c}ade images, and the result shows our method has a significant performance improvement compared to only a random crop on fa\c{c}ade. We also demonstrate some application scenarios and proposed a fa\c{c}ade modeling workflow with the representative region texture, which has a better visual resolution for a regular fa\c{c}ade.