We lifted 7 functions out of this paper's own repositories and ran 5 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 |
|---|---|---|
| amazon-science/prompt-pretraining | canonical | 5 of 7 |
| Function | Status | Where it lives |
|---|---|---|
| align_loss | Ran | amazon-science/prompt-pretraining/align_uniform.py code served (permissive licence) · get_code("ebb1a59bc40babbb") |
| basic_clean | Ran | amazon-science/prompt-pretraining/clip/simple_tokenizer.py code served (permissive licence) · get_code("98f385d847636a3e") |
| get_pairs | Ran | amazon-science/prompt-pretraining/clip/simple_tokenizer.py code served (permissive licence) · get_code("d919ae32e5e4e616") |
| uniform_loss | Ran | amazon-science/prompt-pretraining/align_uniform.py code served (permissive licence) · get_code("8d3d5f756818e024") |
| whitespace_clean | Ran | amazon-science/prompt-pretraining/clip/simple_tokenizer.py code served (permissive licence) · get_code("9542161e9640b858") |
| build_model | Not yet run | amazon-science/prompt-pretraining/clip/model.py code served (permissive licence) · get_code("81ce35f8e245e82e") |
| load | Not yet run | amazon-science/prompt-pretraining/clip/clip.py code served (permissive licence) · get_code("3776e3e7d4b86f52") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
This work proposes POMP, a prompt pre-training method for vision-language models. Being memory and computation efficient, POMP enables the learned prompt to condense semantic information for a rich set of visual concepts with over twenty-thousand classes. Once pre-trained, the prompt with a strong transferable ability can be directly plugged into a variety of visual recognition tasks including image classification, semantic segmentation, and object detection, to boost recognition performances in a zero-shot manner. Empirical evaluation shows that POMP achieves state-of-the-art performances on 21 datasets, e.g., 67.0% average accuracy on 10 classification datasets (+3.1% compared to CoOp) and 84.4 hIoU on open-vocabulary Pascal VOC segmentation (+6.9 compared to ZSSeg). Our code is available at https://github.com/amazon-science/prompt-pretraining.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2304.04704")
get_code_for_paper("2304.04704")
have("2304.04704")
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