4 research outputs found
Graph-Segmenter: Graph Transformer with Boundary-aware Attention for Semantic Segmentation
The transformer-based semantic segmentation approaches, which divide the
image into different regions by sliding windows and model the relation inside
each window, have achieved outstanding success. However, since the relation
modeling between windows was not the primary emphasis of previous work, it was
not fully utilized. To address this issue, we propose a Graph-Segmenter,
including a Graph Transformer and a Boundary-aware Attention module, which is
an effective network for simultaneously modeling the more profound relation
between windows in a global view and various pixels inside each window as a
local one, and for substantial low-cost boundary adjustment. Specifically, we
treat every window and pixel inside the window as nodes to construct graphs for
both views and devise the Graph Transformer. The introduced boundary-aware
attention module optimizes the edge information of the target objects by
modeling the relationship between the pixel on the object's edge. Extensive
experiments on three widely used semantic segmentation datasets (Cityscapes,
ADE-20k and PASCAL Context) demonstrate that our proposed network, a Graph
Transformer with Boundary-aware Attention, can achieve state-of-the-art
segmentation performance
Recent Developments in Recommender Systems: A Survey
In this technical survey, we comprehensively summarize the latest
advancements in the field of recommender systems. The objective of this study
is to provide an overview of the current state-of-the-art in the field and
highlight the latest trends in the development of recommender systems. The
study starts with a comprehensive summary of the main taxonomy of recommender
systems, including personalized and group recommender systems, and then delves
into the category of knowledge-based recommender systems. In addition, the
survey analyzes the robustness, data bias, and fairness issues in recommender
systems, summarizing the evaluation metrics used to assess the performance of
these systems. Finally, the study provides insights into the latest trends in
the development of recommender systems and highlights the new directions for
future research in the field