[2311.13254] Unified Domain Adaptive Semantic Segmentation

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a summary:The adaptive semantic fragmentation of a non-supervisory field (UDA-SS) aims to transfer supervision from the source field called a targeted field. Most of the current UDA-SS works are usually thinking about the images, while recent attempts to process videos have extended through time dimension modeling. Although two lines for research share the main challenges-overcoming the conversion of the basic field distribution, their studies are largely independent, which leads to fragmented visions, the lack of a complete understanding, and lost opportunities for cross-pollination of ideas. This fragmentation prevents the unification of methods, which leads to excessive efforts and transmission of knowledge below the optimal level through the fields of images and video. Under this observation, we defend the unification of the UDA-SS study through video and image scenarios, providing more comprehensive understanding, synergy developments, and sharing of effective knowledge. To achieve this purpose, we explore the unified UDA-SS from the perspective of general data enlargement, as a unified conceptual framework, enable the improvement of circular, the possibility of mutual pollination of ideas, and eventually contribute to general progress and the practical impact of this field of research. Specifically, we suggest a quadmix quadmix method, which is characterized by the processing of distinctive points features and features of features through four directional paths to mix within the field in a feature space. To deal with time transformations with videos, we merge the assembly of the advantages of visual flow over the spatial and temporal dimensions to align the micro -field. Wide experiences show that our way outperforms the latest work through large margins on four difficult UDA-SS standards. The source code and our models will be released in this URL https.