Averaging and temporally interpolating text latents in VLAs enables 83% success on novel task combinations in the libero-ood benchmark where SOTA models achieve under 15%.
Sigmoid loss for language image pre-training
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Omni-Encoder unifies visual and audio encoding at symmetrical 25 fps using a Transformer with three new components, yielding gains on fine-grained motion tasks while matching baselines on audio-visual benchmarks.
ALLaVA creates 1.3M GPT4V-synthesized samples enabling 4B VLMs to achieve competitive results on 17 benchmarks and match 7B/13B models on some tasks.
Show-o2 unifies text, image, and video understanding and generation in a single autoregressive-plus-flow-matching model built on 3D causal VAE representations.
citing papers explorer
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VLAs are Confined yet Capable of Generalizing to Novel Instructions
Averaging and temporally interpolating text latents in VLAs enables 83% success on novel task combinations in the libero-ood benchmark where SOTA models achieve under 15%.
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OmniEncoder: See, Hear, and Feel Continuous Motion Like Humans With One Encoder
Omni-Encoder unifies visual and audio encoding at symmetrical 25 fps using a Transformer with three new components, yielding gains on fine-grained motion tasks while matching baselines on audio-visual benchmarks.
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ALLaVA: Harnessing GPT4V-Synthesized Data for Lite Vision-Language Models
ALLaVA creates 1.3M GPT4V-synthesized samples enabling 4B VLMs to achieve competitive results on 17 benchmarks and match 7B/13B models on some tasks.
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Show-o2: Improved Native Unified Multimodal Models
Show-o2 unifies text, image, and video understanding and generation in a single autoregressive-plus-flow-matching model built on 3D causal VAE representations.