Large Language Models (LLMs) have revolutionized natural language processing, but their application to speech-based tasks remains challenging due to the complexities of integrating audio and text modalities.
PiML (read $\pi$-ML, /`pai`em`el/) is an integrated and open-access Python toolbox for interpretable machine learning model development and model diagnostics.
Due to the absence of the text encoding as contrastive target, SuperClass does not require a text encoder and does not need to maintain a large batch size as CLIP does.
When pretrained on Objects365, D-FINE-L / X attains 57. 1% / 59. 3% AP, surpassing all existing real-time detectors.
Ranked #1 on Real-Time Object Detection on MS COCO (using extra training data)
Tasks involving multiple text-rich images are especially challenging, as they require not only understanding the content of individual images but reasoning about inter-relationships and logical flows across multiple visual inputs.
Our encoder-decoder architecture recursively processes 2D image tokens, distilling them into 1D latent tokens over multiple iterations of recurrent rollouts.
To create rich visualizations, data analysts often need to iterate back and forth among data processing and chart specification to achieve their goals.
To the best of our knowledge, Hallo2, proposed in this paper, is the first method to achieve 4K resolution and generate hour-long, audio-driven portrait image animations enhanced with textual prompts.
It can understand visual, auditory, and textual modalities, directly output audio, and support flexible duplex interaction.