We lifted 3 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 |
|---|---|---|
| copy not recorded | — | 1 of 1 |
| CSAILVision/sceneparsing | pwc_unofficial | 0 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| conv3x3 | Ran | this paper's copy was not recorded; identical code first harvested from lixiaotong97/DSU pointer only · get_code("fac5364e2f53c6db") |
| intersectionAndUnion | Not yet run | CSAILVision/sceneparsing/evaluationCode/utils_eval.py code served (permissive licence) · get_code("4f52ae00d24f3674") |
| pixelAccuracy | Not yet run | CSAILVision/sceneparsing/evaluationCode/utils_eval.py code served (permissive licence) · get_code("0a301af8d49be592") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Scene parsing, or recognizing and segmenting objects and stuff in an image, is one of the key problems in computer vision. Despite the community's efforts in data collection, there are still few image datasets covering a wide range of scenes and object categories with dense and detailed annotations for scene parsing. In this paper, we introduce and analyze the ADE20K dataset, spanning diverse annotations of scenes, objects, parts of objects, and in some cases even parts of parts. A generic network design called Cascade Segmentation Module is then proposed to enable the segmentation networks to parse a scene into stuff, objects, and object parts in a cascade. We evaluate the proposed module integrated within two existing semantic segmentation networks, yielding significant improvements for scene parsing. We further show that the scene parsing networks trained on ADE20K can be applied to a wide variety of scenes and objects.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1608.05442")
get_code_for_paper("1608.05442")
have("1608.05442")
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