TRACER: Extreme Attention Guided Salient Object Tracing Network (Student Abstract)


  • Min Seok Lee Korea University, Seoul, Korea
  • WooSeok Shin Korea University, Seoul, Korea
  • Sung Won Han Korea University, Seoul, Korea



Computer Vision, Segmentation, Salient Object Detection, Attention Mechanism, Adaptive Pixel Intensity


Existing studies on salient object detection (SOD) focus on extracting distinct objects with edge features and aggregating multi-level features to improve SOD performance. However, both performance gain and computational efficiency cannot be achieved, which has motivated us to study the inefficiencies in existing encoder-decoder structures to avoid this trade-off. We propose TRACER which excludes multi-decoder structures and minimizes the learning parameters usage by employing attention guided tracing modules (ATMs), as shown in Fig. 1.




How to Cite

Lee, M. S., Shin, W., & Han, S. W. (2022). TRACER: Extreme Attention Guided Salient Object Tracing Network (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12993-12994.