We lifted 8 functions out of this paper's own repositories and ran 7 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 |
|---|---|---|
| dvlab-research/minigemini | canonical | 7 of 8 |
| Function | Status | Where it lives |
|---|---|---|
| create_one_query | Ran | dvlab-research/minigemini/mgm/eval/model_math_vista.py code served (permissive licence) · get_code("269bb26a4378dd51") |
| expand2square | Ran | dvlab-research/minigemini/mgm/mm_utils.py code served (permissive licence) · get_code("592b3c1a88f93d7c") |
| get_chunk | Ran | dvlab-research/minigemini/mgm/eval/model_math_vista.py code served (permissive licence) · get_code("42a46570620cd9fa") |
| is_none | Ran | dvlab-research/minigemini/mgm/eval/model_vqa_mmbench.py code served (permissive licence) · get_code("bae18947b56f2be1") |
| load_image | Ran | dvlab-research/minigemini/predict.py code served (permissive licence) · get_code("9b3c1cb391672ccb") |
| load_image_from_base64 | Ran | dvlab-research/minigemini/mgm/mm_utils.py code served (permissive licence) · get_code("c3ee9d07c900dd55") |
| split_list | Ran | dvlab-research/minigemini/mgm/eval/model_math_vista.py code served (permissive licence) · get_code("076c252c52cbb161") |
| process_images | Not yet run | dvlab-research/minigemini/mgm/mm_utils.py code served (permissive licence) · get_code("d6153bc4456b4b0c") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
In this work, we introduce Mini-Gemini, a simple and effective framework enhancing multi-modality Vision Language Models (VLMs). Despite the advancements in VLMs facilitating basic visual dialog and reasoning, a performance gap persists compared to advanced models like GPT-4 and Gemini. We try to narrow the gap by mining the potential of VLMs for better performance and any-to-any workflow from three aspects, i.e., high-resolution visual tokens, high-quality data, and VLM-guided generation. To enhance visual tokens, we propose to utilize an additional visual encoder for high-resolution refinement without increasing the visual token count. We further construct a high-quality dataset that promotes precise image comprehension and reasoning-based generation, expanding the operational scope of current VLMs. In general, Mini-Gemini further mines the potential of VLMs and empowers current frameworks with image understanding, reasoning, and generation simultaneously. Mini-Gemini supports a series of dense and MoE Large Language Models (LLMs) from 2B to 34B. It is demonstrated to achieve leading performance in several zero-shot benchmarks and even surpasses the developed private models. Code and models are available at https://github.com/dvlab-research/MiniGemini.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2403.18814")
get_code_for_paper("2403.18814")
have("2403.18814")
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