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Found 6 papers, 2 papers with code
LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback
Large Action Models (LAMs) for AI Agents offer incredible potential but face challenges due to the need for high-quality training data, especially for multi-steps tasks that involve planning, executing tool calls, and responding to feedback.
Pixel Motion as Universal Representation for Robot Control
We present LangToMo, a vision-language-action framework structured as a dual-system architecture that uses pixel motion forecasts as intermediate representations.
LatentCRF: Continuous CRF for Efficient Latent Diffusion
Latent Diffusion Models (LDMs) produce high-quality, photo-realistic images, however, the latency incurred by multiple costly inference iterations can restrict their applicability.
xGen-MM (BLIP-3): A Family of Open Large Multimodal Models
The framework comprises meticulously curated datasets, a training recipe, model architectures, and a resulting suite of LMMs.
Hybrid Random Features
We propose a new class of random feature methods for linearizing softmax and Gaussian kernels called hybrid random features (HRFs) that automatically adapt the quality of kernel estimation to provide most accurate approximation in the defined regions of interest.
Vi-MIX FOR SELF-SUPERVISED VIDEO REPRESENTATION
We find that our video mixing strategy: Vi-Mix, i. e. preliminary mixing of videos followed by CMMC across different modalities in a video, improves the qual- ity of learned video representations.
