Labeling LiDAR point clouds for training autonomous driving is extremely...
Neural Radiance Fields (NeRFs) aim to synthesize novel views of objects ...
Despite the dramatic success in image generation, Generative Adversarial...
Zero-shot human-AI coordination holds the promise of collaborating with
...
Designing better deep networks and better reinforcement learning (RL)
al...
Incremental semantic segmentation(ISS) is an emerging task where old mod...
Vision transformers (ViTs) have pushed the state-of-the-art for various
...
In recent years, reinforcement learning has faced several challenges in ...
An imperfect-information game is a type of game with asymmetric informat...
Multi-agent reinforcement learning (MARL) has received increasing attent...
We present Coordinated Proximal Policy Optimization (CoPPO), an algorith...
Vision transformers (ViTs) have pushed the state-of-the-art for various
...
Model-based reinforcement learning (RL) is more sample efficient than
mo...
Model-based reinforcement learning is a framework in which an agent lear...
Universal lesion detection (ULD) on computed tomography (CT) images is a...
Point cloud super-resolution is a fundamental problem for 3D reconstruct...
In this paper, we aim at automatically searching an efficient network
ar...
Modern approaches for semantic segmentation usually employ dilated
convo...
Zero-shot Learning (ZSL) aims to recognize objects of the unseen classes...
Zero-shot learning (ZSL) aims to recognize unseen image categories by
le...
Image processing and pixel-wise dense prediction have been advanced by
h...
In this paper, we consider the problem of leveraging existing fully labe...
Macro-management is an important problem in StarCraft, which has been st...
Image cropping aims at improving the aesthetic quality of images by adju...
Recent advances in generative adversarial networks (GANs) have shown
pro...