J Kim, Percy Liang, Siddharth Karamcheti, Dorsa Sadigh, Ashwin Balakrishna, Suraj Nair, C Kim, M Lin, S Lin, P Lue, A Krueger, O Keefe, and 2 more
We lifted 5 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 |
|---|---|---|
| tri-ml/prismatic-vlms | canonical | 1 of 1 |
| tri-ml/vlm-evaluation | canonical | 0 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| initialize_overwatch | Ran | tri-ml/prismatic-vlms/prismatic/overwatch/overwatch.py code served (permissive licence) · get_code("6e9a835262e7ec57") |
| ImageProcessor | Not yet run | tri-ml/vlm-evaluation/vlm_eval/models/prismatic.py pointer only (licence: NOASSERTION) · get_code("08e6aeaa327cb1f2") |
| PrismaticVLM | Not yet run | tri-ml/vlm-evaluation/vlm_eval/models/prismatic.py pointer only (licence: NOASSERTION) · get_code("1c76ba920eb87f9b") |
| Tokenizer | Not yet run | tri-ml/vlm-evaluation/vlm_eval/models/prismatic.py pointer only (licence: NOASSERTION) · get_code("e0633b98873dbfc7") |
| VLM | Not yet run | tri-ml/vlm-evaluation/vlm_eval/models/prismatic.py pointer only (licence: NOASSERTION) · get_code("a2488e8a8252030f") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Visually-conditioned language models (VLMs) have seen growing adoption in applications such as visual dialogue, scene understanding, and robotic task planning; adoption that has fueled a wealth of new models such as LLaVa, Instruct-BLIP, and PaLI-3. Despite the volume of new releases, key design decisions around image preprocessing, architecture, and optimization are underexplored, making it challenging to understand what factors account for model performance -a challenge further complicated by the lack of objective, consistent evaluations. To address these gaps, we first compile a suite of standardized evaluations spanning visual question answering, object localization, and challenge sets that probe properties such as hallucination; evaluations that provide fine-grained insight VLM capabilities. Second, we rigorously investigate VLMs along key design axes, including pretrained visual representations and training from base vs. instruct-tuned language models, amongst others. We couple our analysis with three resource contributions: (1) a unified framework for evaluating VLMs, (2) optimized, flexible training code, and (3) checkpoints for all models, including a family of VLMs at the 7-13B scale that strictly outperform InstructBLIP and LLaVa v1.5, the state-of-the-art in open VLMs. * Prismatic (adj) -relating to or having the form of a prism. Like a geometric prism, our VLMs share a common structure, but are characterized by different "faces" -the individual design axes we explore in this work.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2402.07865")
get_code_for_paper("2402.07865")
have("2402.07865")
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