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 |
|---|---|---|
| gligen/GLIGEN | extension | 3 of 3 |
| ux-decoder/segment-everything-everywhere-all-at-once | pwc_unofficial | 1 of 3 |
| copy not recorded | — | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| alpha_generator | Ran | this paper's copy was not recorded; identical code first harvested from YangLing0818/RealCompo pointer only · get_code("b3e1f7172e4955ae") |
| alpha_generator | Ran | gligen/GLIGEN/demo/gligen/task_grounded_generation.py code served (permissive licence) · get_code("a8b7b8f06a868b6f") |
| draw_box | Ran | gligen/GLIGEN/demo/gligen/task_grounded_generation.py code served (permissive licence) · get_code("5a613694f83a9173") |
| project | Ran | gligen/GLIGEN/gligen_inference.py code served (permissive licence) · get_code("e49923c3510ee0bc") |
| register_norm_module | Ran | ux-decoder/segment-everything-everywhere-all-at-once/utils/model.py code served (permissive licence) · get_code("e4f6733fa1ab1999") |
| filter_images_with_only_crowd_annotations | Not yet run | ux-decoder/segment-everything-everywhere-all-at-once/datasets/build.py code served (permissive licence) · get_code("a21a61e41d986b6a") |
| load_semseg | Not yet run | ux-decoder/segment-everything-everywhere-all-at-once/datasets/semseg_loader.py code served (permissive licence) · get_code("eb73ad202b73f933") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
LLaVA-Interactive is a research prototype for multimodal human-AI interaction. The system can have multi-turn dialogues with human users by taking multimodal user inputs and generating multimodal responses. Importantly, LLaVA-Interactive goes beyond language prompt, where visual prompt is enabled to align human intents in the interaction. The development of LLaVA-Interactive is extremely cost-efficient as the system combines three multimodal skills of pre-built AI models without additional model training: visual chat of LLaVA, image segmentation from SEEM, as well as image generation and editing from GLIGEN. A diverse set of application scenarios is presented to demonstrate the promises of LLaVA-Interactive and to inspire future research in multimodal interactive systems.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2311.00571")
get_code_for_paper("2311.00571")
have("2311.00571")
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