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Reinforcement learning algorithms: A brief survey
Reinforcement Learning (RL) is a machine learning (ML) technique to learn sequential
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …
Artificial intelligence for multimodal data integration in oncology
In oncology, the patient state is characterized by a whole spectrum of modalities, ranging
from radiology, histology, and genomics to electronic health records. Current artificial …
from radiology, histology, and genomics to electronic health records. Current artificial …
GhostNetv2: Enhance cheap operation with long-range attention
Light-weight convolutional neural networks (CNNs) are specially designed for applications
on mobile devices with faster inference speed. The convolutional operation can only capture …
on mobile devices with faster inference speed. The convolutional operation can only capture …
Motiondiffuse: Text-driven human motion generation with diffusion model
Human motion modeling is important for many modern graphics applications, which typically
require professional skills. In order to remove the skill barriers for laymen, recent motion …
require professional skills. In order to remove the skill barriers for laymen, recent motion …
Video probabilistic diffusion models in projected latent space
Despite the remarkable progress in deep generative models, synthesizing high-resolution
and temporally coherent videos still remains a challenge due to their high-dimensionality …
and temporally coherent videos still remains a challenge due to their high-dimensionality …
Languagebind: Extending video-language pretraining to n-modality by language-based semantic alignment
The video-language (VL) pretraining has achieved remarkable improvement in multiple
downstream tasks. However, the current VL pretraining framework is hard to extend to …
downstream tasks. However, the current VL pretraining framework is hard to extend to …
Expanding language-image pretrained models for general video recognition
Contrastive language-image pretraining has shown great success in learning visual-textual
joint representation from web-scale data, demonstrating remarkable “zero-shot” …
joint representation from web-scale data, demonstrating remarkable “zero-shot” …
Cogvideo: Large-scale pretraining for text-to-video generation via transformers
Large-scale pretrained transformers have created milestones in text (GPT-3) and text-to-
image (DALL-E and CogView) generation. Its application to video generation is still facing …
image (DALL-E and CogView) generation. Its application to video generation is still facing …
S4nd: Modeling images and videos as multidimensional signals with state spaces
Visual data such as images and videos are typically modeled as discretizations of inherently
continuous, multidimensional signals. Existing continuous-signal models attempt to exploit …
continuous, multidimensional signals. Existing continuous-signal models attempt to exploit …
St-adapter: Parameter-efficient image-to-video transfer learning
Capitalizing on large pre-trained models for various downstream tasks of interest have
recently emerged with promising performance. Due to the ever-growing model size, the …
recently emerged with promising performance. Due to the ever-growing model size, the …