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Found 20 papers, 8 papers with code
An automated approach to extracting positive and negative clinical research results
Failure is common in clinical trials since the successful failures presented in negative results always indicate the ways that should not be taken.
Soft BPR Loss for Dynamic Hard Negative Sampling in Recommender Systems
In recommender systems, leveraging Graph Neural Networks (GNNs) to formulate the bipartite relation between users and items is a promising way.
Your Contrastive Learning Is Secretly Doing Stochastic Neighbor Embedding
Contrastive learning, especially Self-Supervised Contrastive Learning (SSCL), has achieved great success in extracting powerful features from unlabeled data, enabling comparable performance to the supervised counterpart.
HuMMan: Multi-Modal 4D Human Dataset for Versatile Sensing and Modeling
4D human sensing and modeling are fundamental tasks in vision and graphics with numerous applications.
WegFormer: Transformers for Weakly Supervised Semantic Segmentation
Although convolutional neural networks (CNNs) have achieved remarkable progress in weakly supervised semantic segmentation (WSSS), the effective receptive field of CNN is insufficient to capture global context information, leading to sub-optimal results.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Differentiable and Scalable Generative Adversarial Models for Data Imputation
DIM leverages a new masking Sinkhorn divergence function to make an arbitrary generative adversarial imputation model differentiable, while for such a differentiable imputation model, SSE can estimate an appropriate sample size to ensure the user-specified imputation accuracy of the final model.
Smooth Nested Simulation: Bridging Cubic and Square Root Convergence Rates in High Dimensions
Nested simulation concerns estimating functionals of a conditional expectation via simulation.
Understanding Square Loss in Training Overparametrized Neural Network Classifiers
Comparing to cross-entropy, square loss has comparable generalization error but noticeable advantages in robustness and model calibration.
Segmenting Transparent Object in the Wild with Transformer
This work presents a new fine-grained transparent object segmentation dataset, termed Trans10K-v2, extending Trans10K-v1, the first large-scale transparent object segmentation dataset.
Ranked #3 on
Semantic Segmentation
on Trans10K
Neural Network Gaussian Process Considering Input Uncertainty for Composite Structures Assembly
Developing machine learning enabled smart manufacturing is promising for composite structures assembly process.
Regularization Matters: A Nonparametric Perspective on Overparametrized Neural Network
Overparametrized neural networks trained by gradient descent (GD) can provably overfit any training data.
High-Dimensional Non-Parametric Density Estimation in Mixed Smooth Sobolev Spaces
Density estimation plays a key role in many tasks in machine learning, statistical inference, and visualization.
Scene Text Image Super-Resolution in the Wild
For example, it outperforms LapSRN by over 5% and 8%on the recognition accuracy of ASTER and CRNN.
Segmenting Transparent Objects in the Wild
To address this important problem, this work proposes a large-scale dataset for transparent object segmentation, named Trans10K, consisting of 10, 428 images of real scenarios with carefully manual annotations, which are 10 times larger than the existing datasets.
Ranked #4 on
Semantic Segmentation
on Trans10K
Uncertainty Quantification for Bayesian Optimization
Although the outputs of Bayesian optimization are random according to the Gaussian process assumption, quantification of this uncertainty is rarely studied in the literature.
PGU-net+: Progressive Growing of U-net+ for Automated Cervical Nuclei Segmentation
The experimental results show that the PGU-net+ has superior accuracy than the previous state-of-the-art methods on cervical nuclei segmentation.
TextSR: Content-Aware Text Super-Resolution Guided by Recognition
Nonetheless, most of the previous methods may not work well in recognizing text with low resolution which is often seen in natural scene images.
Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network
Recently, some methods have been proposed to tackle arbitrary-shaped text detection, but they rarely take the speed of the entire pipeline into consideration, which may fall short in practical applications. In this paper, we propose an efficient and accurate arbitrary-shaped text detector, termed Pixel Aggregation Network (PAN), which is equipped with a low computational-cost segmentation head and a learnable post-processing.
Ranked #6 on
Scene Text Detection
on SCUT-CTW1500
Automated Segmentation of Pulmonary Lobes using Coordination-Guided Deep Neural Networks
The proposed model is trained and evaluated on a few publicly available datasets and has achieved the state-of-the-art accuracy with a mean Dice coefficient index of 0. 947 $\pm$ 0. 044.
AI Challenger : A Large-scale Dataset for Going Deeper in Image Understanding
Significant progress has been achieved in Computer Vision by leveraging large-scale image datasets.
