We lifted 8 functions out of this paper's own repositories and ran 6 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 |
|---|---|---|
| JialianW/GRiT | canonical | 6 of 8 |
| Function | Status | Where it lives |
|---|---|---|
| get_rel_pos | Ran | JialianW/GRiT/grit/modeling/backbone/utils.py pointer only (licence: MIT) · get_code("edb9869f131321d2") |
| get_vit_lr_decay_rate | Ran | JialianW/GRiT/grit/custom_solver.py pointer only (licence: MIT) · get_code("08bf7ecdb2fffe84") |
| qk2attn | Ran | JialianW/GRiT/grit/modeling/text/modeling_bert.py pointer only (licence: MIT) · get_code("070e8bf494b411b6") |
| url_to_filename | Ran | JialianW/GRiT/grit/modeling/text/file_utils.py pointer only (licence: MIT) · get_code("5eff22fa0a651276") |
| window_partition | Ran | JialianW/GRiT/grit/modeling/backbone/utils.py pointer only (licence: MIT) · get_code("750facb8c52e8864") |
| window_unpartition | Ran | JialianW/GRiT/grit/modeling/backbone/utils.py pointer only (licence: MIT) · get_code("535942f820542fb9") |
| cached_path | Not yet run | JialianW/GRiT/grit/modeling/text/file_utils.py pointer only (licence: MIT) · get_code("4e91fb4390008086") |
| filename_to_url | Not yet run | JialianW/GRiT/grit/modeling/text/file_utils.py pointer only (licence: MIT) · get_code("a6aab787fcf8b10d") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
This paper presents a Generative RegIon-to-Text transformer, GRiT, for object understanding. The spirit of GRiT is to formulate object understanding as <region, text> pairs, where region locates objects and text describes objects. For example, the text in object detection denotes class names while that in dense captioning refers to descriptive sentences. Specifically, GRiT consists of a visual encoder to extract image features, a foreground object extractor to localize objects, and a text decoder to generate open-set object descriptions. With the same model architecture, GRiT can understand objects via not only simple nouns, but also rich descriptive sentences including object attributes or actions. Experimentally, we apply GRiT to object detection and dense captioning tasks. GRiT achieves 60.4 AP on COCO 2017 test-dev for object detection and 15.5 mAP on Visual Genome for dense captioning. Code is available at https://github.com/JialianW/GRiT
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2212.00280")
get_code_for_paper("2212.00280")
have("2212.00280")
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