in-context-learning
kamisaiko_-_model_vistral_o_inContextLearning-ggufmodel_vistral_o_inContextLearningdeberta-v3-small_deepseek_abstracts_in_context_learning_3-shotxlm-roberta-base_deepseek_news_in_context_learning_3-shotdeberta-v3-small_deepseek_qa_in_context_learning_3-shotdeberta-v3-small_deepseek_news_in_context_learning_3-shotxlm-roberta-base_deepseek_abstracts_in_context_learning_3-shotxlm-roberta-base_deepseek_qa_in_context_learning_3-shot
X2I-in-context-learning
X2I Dataset
Project Page: https://vectorspacelab.github.io/OmniGen/
Github: https://github.com/VectorSpaceLab/OmniGen
Paper: https://arxiv.org/abs/2409.11340
Model: https://huggingface.co/Shitao/OmniGen-v1
To achieve robust multi-task processing capabilities, it is essential to train the OmniGen on large-scale and diverse datasets. However, in the field of unified image generation, a readily available dataset has yet to emerge. For this reason, we have curated a large-scale… See the full description on the dataset page: https://huggingface.co/datasets/yzwang/X2I-in-context-learning.in-context-learning-cosmos3-output
Physical-ICL × Cosmos3 — generated outputs
Video-generation outputs from NVIDIA Cosmos3-Nano (Diffusers Cosmos3OmniPipeline,
image-to-video) on the Physical-ICL dataset (Vincwng/Physical-ICL, subset
physiq_prelim, 66 query samples). This studies physical in-context learning: does
showing a demonstration change how the model continues a query scene?
Total generated: 247 videos across 66 query tasks, in 6 configurations.
Configurations
Every configuration uses the… See the full description on the dataset page: https://huggingface.co/datasets/yqi19/in-context-learning-cosmos3-output.incontext-learning-proj
InContextLearning-PromptTargetingchatbot-in-context-learningunderstanding-generalization-and-forgetting-in-in-context-continual-learning-reprofine-tuning-without-forgetting-in-context-learning-a-theoretical-analysis-of-linear-reprorepro-in-context-continual-learningrepro-fine-tuning-without-forgetting-in-context-learning-a-theoretical-analysis-of-linear-attent