SEED-Bench is a new benchmark of 19K multiple-choice questions for evaluating generative comprehension in multimodal LLMs across 12 image and video dimensions.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Seer, a transformer-based PIDM pre-trained on large robotic datasets like DROID, outperforms prior methods on simulation and real-world robotic manipulation benchmarks with gains up to 43%.
InternVideo combines masked video modeling and video-language contrastive learning into a single foundation model that reaches state-of-the-art results on 39 video datasets including 91.1% top-1 on Kinetics-400.
citing papers explorer
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SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension
SEED-Bench is a new benchmark of 19K multiple-choice questions for evaluating generative comprehension in multimodal LLMs across 12 image and video dimensions.
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Predictive Inverse Dynamics Models are Scalable Learners for Robotic Manipulation
Seer, a transformer-based PIDM pre-trained on large robotic datasets like DROID, outperforms prior methods on simulation and real-world robotic manipulation benchmarks with gains up to 43%.
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InternVideo: General Video Foundation Models via Generative and Discriminative Learning
InternVideo combines masked video modeling and video-language contrastive learning into a single foundation model that reaches state-of-the-art results on 39 video datasets including 91.1% top-1 on Kinetics-400.