An Open-Source Software Toolkit & Benchmark Suite for the Evaluation and Adaptation of Multimodal Action Models
원문 초록
Recent innovations in multimodal action models represent a promising direction for developing general-purpose agentic systems, combining visual understanding, language comprehension, and action generation. We introduce MultiNet - a novel, fully open-source benchmark and surrounding software ecosystem designed to rigorously evaluate and adapt models across vision, language, and action domains. We establish standardized evaluation protocols for assessing vision-language models (VLMs) and vision-language-action models (VLAs), and provide open source software to download relevant data, models, and evaluations. Additionally, we provide a composite dataset with over 1.3 trillion tokens of image captioning, visual question answering, commonsense reasoning, robotic control, digital game-play, simulated locomotion/manipulation, and many more tasks. The MultiNet benchmark, framework, toolkit, and evaluation harness have been used in downstream research on the limitations of VLA generalization.
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HalLoc: Token-level Localization of Hallucinations for Vision Language Models
Hallucination at a Glance: Controlled Visual Edits and Fine-Grained Multimodal Learning
Light as Deception: GPT-driven Natural Relighting Against Vision-Language Pre-training Models
Describe Anything Model for Visual Question Answering on Text-rich Images
Revisiting CroPA: A Reproducibility Study and Enhancements for Cross-Prompt Adversarial Transferability in Vision-Language Models
Parallels Between VLA Model Post-Training and Human Motor Learning: Progress, Challenges, and Trends
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