LLMs have demonstrated remarkable abilities at interacting with humans
t...
Offline reinforcement learning (RL) is challenged by the distributional ...
Recently, Offline Reinforcement Learning (RL) has achieved remarkable
pr...
Offline reinforcement learning (RL) aims to learn optimal policies from
...
Diffusion model (DM), as a powerful generative model, recently achieved ...
With the advance of language models, privacy protection is receiving mor...
Offline reinforcement learning (RL) is challenged by the distributional ...
Offline reinforcement learning (RL) aims at learning an effective policy...
Deep reinforcement learning (RL) algorithms suffer severe performance
de...
Deep long-tailed learning, one of the most challenging problems in visua...
Vision transformers (ViTs) have been successfully applied in image
class...
Deep reinforcement learning (RL) agents trained in a limited set of
envi...
Training a neural network model that can quickly adapt to a new task is
...
Most existing object instance detection and segmentation models only wor...
Solving long-tail large vocabulary object detection with deep learning b...
Remarkable progress has been made in object instance detection and
segme...
The long-tail distribution of the visual world poses great challenges fo...
Deep Reinforcement Learning (Deep RL) has been receiving increasingly mo...
Recent progress in image recognition has stimulated the deployment of vi...
Many activities of interest are rare events, with only a few labeled exa...
This work aims to solve the challenging few-shot object detection proble...
The question why deep learning algorithms generalize so well has attract...