Trending Research
Aria: An Open Multimodal Native Mixture-of-Experts Model
rhymes-ai/aria
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Information comes in diverse modalities.
Depth Pro: Sharp Monocular Metric Depth in Less Than a Second
We present a foundation model for zero-shot metric monocular depth estimation.
MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering
We introduce MLE-bench, a benchmark for measuring how well AI agents perform at machine learning engineering.
Posterior-Mean Rectified Flow: Towards Minimum MSE Photo-Realistic Image Restoration
ohayonguy/PMRF
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Photo-realistic image restoration algorithms are typically evaluated by distortion measures (e. g., PSNR, SSIM) and by perceptual quality measures (e. g., FID, NIQE), where the desire is to attain the lowest possible distortion without compromising on perceptual quality.
Ranked #1 on
Blind Face Restoration
on CelebA-Test
(FID metric)
Deciphering Cross-Modal Alignment in Large Vision-Language Models with Modality Integration Rate
shikiw/modality-integration-rate
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We present the Modality Integration Rate (MIR), an effective, robust, and generalized metric to indicate the multi-modal pre-training quality of Large Vision Language Models (LVLMs).
LongWriter: Unleashing 10,000+ Word Generation from Long Context LLMs
thudm/longwriter
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By incorporating this dataset into model training, we successfully scale the output length of existing models to over 10, 000 words while maintaining output quality.
Scaling Proprioceptive-Visual Learning with Heterogeneous Pre-trained Transformers
liruiw/HPT
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Previous robot learning methods often collect data to train with one specific embodiment for one task, which is expensive and prone to overfitting.
"Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models
We hope that our study can facilitate the research community and LLM vendors in promoting safer and regulated LLMs.
LLaMA-Omni: Seamless Speech Interaction with Large Language Models
We build our model based on the latest Llama-3. 1-8B-Instruct model.
VPTQ: Extreme Low-bit Vector Post-Training Quantization for Large Language Models
microsoft/vptq
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Due to the redundancy in LLM weights, recent research has focused on pushing weight-only quantization to extremely low-bit (even down to 2 bits).
