We lifted 1 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| liuzywen/tritransnet | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| structure_loss | Ran | liuzywen/tritransnet/TriTransNet_train.py pointer only (licence: NONE) · get_code("797eafbb72dabcfd") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Salient object detection is the pixel-level dense prediction task which can highlight the prominent object in the scene. Recently U-Net framework is widely used, and continuous convolution and pooling operations generate multi-level features which are complementary with each other. In view of the more contribution of high-level features for the performance, we propose a triplet transformer embedding module to enhance them by learning long-range dependencies across layers. It is the first to use three transformer encoders with shared weights to enhance multi-level features. By further designing scale adjustment module to process the input, devising three-stream decoder to process the output and attaching depth features to color features for the multi-modal fusion, the proposed triplet transformer embedding network (TriTransNet) achieves the state-of-the-art performance in RGB-D salient object detection, and pushes the performance to a new level. Experimental results demonstrate the effectiveness of the proposed modules and the competition of TriTransNet.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2108.03990")
get_code_for_paper("2108.03990")
have("2108.03990")
Connect an agent — have() is free.