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Found 10 papers, 3 papers with code
PointInfinity: Resolution-Invariant Point Diffusion Models
We present PointInfinity, an efficient family of point cloud diffusion models.
ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders
This co-design of self-supervised learning techniques and architectural improvement results in a new model family called ConvNeXt V2, which significantly improves the performance of pure ConvNets on various recognition benchmarks, including ImageNet classification, COCO detection, and ADE20K segmentation.
Ranked #45 on
Semantic Segmentation
on ADE20K
Exploring Long-Sequence Masked Autoencoders
Masked Autoencoding (MAE) has emerged as an effective approach for pre-training representations across multiple domains.
RGB-D Local Implicit Function for Depth Completion of Transparent Objects
Key to our approach is a local implicit neural representation built on ray-voxel pairs that allows our method to generalize to unseen objects and achieve fast inference speed.
Self-Supervised Real-to-Sim Scene Generation
Synthetic data is emerging as a promising solution to the scalability issue of supervised deep learning, especially when real data are difficult to acquire or hard to annotate.
Sim2SG: Sim-to-Real Scene Graph Generation for Transfer Learning
However, neural network models trained on synthetic data, do not perform well on real data because of the domain gap.
Semi-Supervised StyleGAN for Disentanglement Learning
Disentanglement learning is crucial for obtaining disentangled representations and controllable generation.
Solving Markov Decision Processes with Reachability Characterization from Mean First Passage Times
A new mechanism for efficiently solving the Markov decision processes (MDPs) is proposed in this paper.
Accelerating Goal-Directed Reinforcement Learning by Model Characterization
Then, we leverage this approximate model along with a notion of reachability using Mean First Passage Times to perform Model-Based reinforcement learning.
Reachability and Differential based Heuristics for Solving Markov Decision Processes
The solution convergence of Markov Decision Processes (MDPs) can be accelerated by prioritized sweeping of states ranked by their potential impacts to other states.
