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Found 11 papers, 4 papers with code
A Gated MLP Architecture for Learning Topological Dependencies in Spatio-Temporal Graphs
The Cy2Mixer is composed of three blocks based on MLPs: A message-passing block for encapsulating spatial information, a cycle message-passing block for enriching topological information through cyclic subgraphs, and a temporal block for capturing temporal properties.
Ranked #2 on
Traffic Prediction
on PeMS04
DiffusionPoser: Real-time Human Motion Reconstruction From Arbitrary Sparse Sensors Using Autoregressive Diffusion
Unlike existing methods, our model grants users the flexibility to determine the number and arrangement of sensors tailored to the specific activity of interest, without the need for retraining.
FastSurf: Fast Neural RGB-D Surface Reconstruction using Per-Frame Intrinsic Refinement and TSDF Fusion Prior Learning
We introduce FastSurf, an accelerated neural radiance field (NeRF) framework that incorporates depth information for 3D reconstruction.
NoiseTransfer: Image Noise Generation with Contrastive Embeddings
Although several real-world noisy datasets have been presented, the number of train datasets (i. e., pairs of clean and real noisy images) is limited, and acquiring more real noise datasets is laborious and expensive.
Generating 3D Bio-Printable Patches Using Wound Segmentation and Reconstruction to Treat Diabetic Foot Ulcers
We introduce AiD Regen, a novel system that generates 3D wound models combining 2D semantic segmentation with 3D reconstruction so that they can be printed via 3D bio-printers during the surgery to treat diabetic foot ulcers (DFUs).
Cycled Compositional Learning between Images and Text
Since this one-way mapping is highly under-constrained, we couple it with an inverse relation learning with the Correction Network and introduce a cycled relation for given Image We participate in Fashion IQ 2020 challenge and have won the first place with the ensemble of our model.
CurlingNet: Compositional Learning between Images and Text for Fashion IQ Data
In order to learn an effective image-text composition for the data in the fashion domain, our model proposes two key components as follows.
Ranked #16 on
Image Retrieval
on Fashion IQ
Restore from Restored: Single Image Denoising with Pseudo Clean Image
However, these methods have limitations in using internal information available in a given test image.
Restore from Restored: Video Restoration with Pseudo Clean Video
We analyze the restoration performance of the fine-tuned video denoising networks with the proposed self-supervision-based learning algorithm, and demonstrate that the FCN can utilize recurring patches without requiring accurate registration among adjacent frames.
Self-Supervised Fast Adaptation for Denoising via Meta-Learning
Under certain statistical assumptions of noise, recent self-supervised approaches for denoising have been introduced to learn network parameters without true clean images, and these methods can restore an image by exploiting information available from the given input (i. e., internal statistics) at test time.
Muscle-actuated Human Simulation and Control
Many anatomical factors, such as bone geometry and muscle condition, interact to affect human movements.
