In recent years, diffusion-based text-to-music (TTM) generation has gained prominence, offering an innovative approach to synthesizing musical content from textual descriptions.
Ranked #1 on Music Generation on Song Describer Dataset
This poses a fundamental challenge in matching two images of the same place captured from different camera viewpoints: "the similarity of what overlaps can be dominated by the dissimilarity of what does not overlap".
The fusion of both visual and LiDAR measurements is based on a single unified voxel map where the LiDAR module constructs the geometric structure for registering new LiDAR scans and the visual module attaches image patches to the LiDAR points.
It can be used to obtain complete information, so that train-from-scratch models can achieve better results than state-of-the-art models pre-trained using large datasets, the comparison results are shown in Figure 1.
Ranked #7 on Real-Time Object Detection on MS COCO
We build our model based on the latest Llama-3. 1-8B-Instruct model.
Neural HMMs are a type of neural transducer recently proposed for sequence-to-sequence modelling in text-to-speech.
Ranked #11 on Text-To-Speech Synthesis on LJSpeech (using extra training data)
To this end, this paper proposes a novel text-guided video inpainting model that achieves better consistency, controllability and compatibility.
We integrate encoded textual instruction and image exemplar as a unified condition for diffusion model, enabling the editing of original image following multimodal instructions.
Our research introduces a novel approach for the long context bottleneck to accelerate LLM inference and reduce GPU memory consumption.
We present SoundStorm, a model for efficient, non-autoregressive audio generation.