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Found 13 papers, 6 papers with code
FormalGeo: An Extensible Formalized Framework for Olympiad Geometric Problem Solving
In this paper, we have constructed a consistent formal plane geometry system.
'Tax-free' 3DMM Conditional Face Generation
This results in a new model that effectively removes the quality tax between 3DMM conditioned face GANs and the unconditional StyleGAN.
MISC210K: A Large-Scale Dataset for Multi-Instance Semantic Correspondence
Semantic correspondence have built up a new way for object recognition.
Correspondence Transformers With Asymmetric Feature Learning and Matching Flow Super-Resolution
The asymmetric feature learning module exploits a biased cross-attention mechanism to encode token features of source images with their target counterparts.
Weakly Supervised Learning of Semantic Correspondence through Cascaded Online Correspondence Refinement
Following the spirit of multiple instance learning (MIL), we decompose the weakly supervised correspondence learning problem into three stages: image-level matching, region-level matching, and pixel-level matching.
Hierarchical Visual Categories Modeling: A Joint Representation Learning and Density Estimation Framework for Out-of-Distribution Detection
We use one fold as the in-distribution dataset and the others as out-of-distribution datasets to evaluate the proposed method.
DPCNet: Dual Path Multi-Excitation Collaborative Network for Facial Expression Representation Learning in Videos
To obtain consistent prediction probabilities from the dual path, we further propose a dual path regularization loss, aiming to minimize the divergence between the distributions of two-path embeddings.
Ranked #13 on
Dynamic Facial Expression Recognition
on DFEW
Dynamic Facial Expression Recognition
Representation Learning
FERV39k: A Large-Scale Multi-Scene Dataset for Facial Expression Recognition in Videos
Current benchmarks for facial expression recognition (FER) mainly focus on static images, while there are limited datasets for FER in videos.
Machine-learning non-stationary noise out of gravitational wave detectors
Signal extraction out of background noise is a common challenge in high precision physics experiments, where the measurement output is often a continuous data stream.
On the Ca-strong 1991bg-like type Ia supernova 2016hnk: evidence for a Chandrasekhar-mass explosion
Our modeling suggests that the narrow [Ca II] features observed in the nebular spectrum are associated with $^{48}$Ca from electron capture during the explosion, which is expected to occur only in white dwarfs that explode near or at the $M_{\rm Ch}$ limit.
Solar and Stellar Astrophysics High Energy Astrophysical Phenomena
Sequentially Aggregated Convolutional Networks
We thus exploit the aggregation nature of shortcut connections at a finer architectural level and place them within wide convolutional layers.
Densely Connected High Order Residual Network for Single Frame Image Super Resolution
Deep convolutional neural networks (DCNN) have been widely adopted for research on super resolution recently, however previous work focused mainly on stacking as many layers as possible in their model, in this paper, we present a new perspective regarding to image restoration problems that we can construct the neural network model reflecting the physical significance of the image restoration process, that is, embedding the a priori knowledge of image restoration directly into the structure of our neural network model, we employed a symmetric non-linear colorspace, the sigmoidal transfer, to replace traditional transfers such as, sRGB, Rec. 709, which are asymmetric non-linear colorspaces, we also propose a "reuse plus patch" method to deal with super resolution of different scaling factors, our proposed methods and model show generally superior performance over previous work even though our model was only roughly trained and could still be underfitting the training set.
Using Kernel Methods and Model Selection for Prediction of Preterm Birth
We describe an application of machine learning to the problem of predicting preterm birth.
