Nov 23, 2023 · In our paper, we propose a new evaluation paradigm for MLLMs, which is evaluating MLLMs with \textit{per-sample criteria} using potent MLLM as ...
[2024.4.27] V3 data, benchmark reuslts, leaderboard and arxiv paper are updated. We keep all the per-sample criteria at evaluation private.
This discrepancy highlights the necessity for a more nuanced approach to evaluation: per-sample criteria. Per-sample criteria are designed to provide specific ...
Apr 29, 2024 · This paper introduces MLLM-Bench, a new benchmark for evaluating multi-modal large language models (LLMs) using GPT-4V. MLLM-Bench is designed ...
... Evaluation Benchmark of Multi-modal LLMs in Video Analysis. [ Project Page] ... MultiModal-GPT: A Vision and Language Model for Dialogue with Humans
Nov 23, 2023 · Existing automatic evaluation methodologies on multi-modal large language models rely on objective queries that have standard answers, ...
[PDF] SEED-Bench: Benchmarking Multimodal Large Language Models
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Current MLLM benchmarks only evaluate this capability level with a single image and text as inputs. Level L2: MLLMs at this capability level should be able to.
Nov 20, 2023 · Evaluation strategies are designed to test the VLMs that cannot generate single-choice answers. ChatGPT is used in this case, with an analysis ...
Nov 23, 2023 · In the pursuit of Artificial General Intelligence (AGI), the integration of vision in language models has marked a significant milestone.
In this notebook, we show how to use OpenAI GPT4V MultiModal LLM class/abstraction for image understanding/reasoning. We also show several functions we are now ...
Missing: MLLM- | Show results with:MLLM-