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Found 18 papers, 5 papers with code
Ray-driven Spectral CT Reconstruction Based on Neural Base-Material Fields
In spectral CT reconstruction, the basis materials decomposition involves solving a large-scale nonlinear system of integral equations, which is highly ill-posed mathematically.
Practitioners' Challenges and Perceptions of CI Build Failure Predictions at Atlassian
Continuous Integration (CI) build failures could significantly impact the software development process and teams, such as delaying the release of new features and reducing developers' productivity.
Fine-grained Visible Watermark Removal
Visible watermark removal aims to erase the watermark from watermarked image and recover the background image, which is a challenging task due to the diverse watermarks.
Matching entropy based disparity estimation from light field
A major challenge for matching-based depth estimation is to prevent mismatches in occlusion and smooth regions.
Motion-aware Memory Network for Fast Video Salient Object Detection
Moreover, inspired by the boundary supervision commonly used in image salient object detection (ISOD), we design a motion-aware loss for predicting object boundary motion and simultaneously perform multitask learning for VSOD and object motion prediction, which can further facilitate the model to extract spatiotemporal features accurately and maintain the object integrity.
Human-centric Image Cropping with Partition-aware and Content-preserving Features
Image cropping aims to find visually appealing crops in an image, which is an important yet challenging task.
Evolution of Popularity Bias: Empirical Study and Debiasing
and how to debias in this long-term dynamic process?
A Non-sequential Approach to Deep User Interest Model for CTR Prediction
The framework can partition data into custom designed time buckets to capture the interactions among information aggregated in different time buckets.
Popularity-Opportunity Bias in Collaborative Filtering
This paper connects equal opportunity to popularity bias in implicit recommenders to introduce the problem of popularity-opportunity bias.
Context-aware deep model compression for edge cloud computing
While deep neural networks (DNNs) have led to a paradigm shift, its exorbitant computational requirement has always been a roadblock in its deployment to the edge, such as wearable devices and smartphones.
Ranked #169 on
Image Classification
on CIFAR-10
Mutual Information Maximization for Effective Lip Reading
By combining these two advantages together, the proposed method is expected to be both discriminative and robust for effective lip reading.
Ranked #8 on
Lipreading
on CAS-VSR-W1k (LRW-1000)
Learning to Hash with Graph Neural Networks for Recommender Systems
In this work, we investigate the problem of hashing with graph neural networks (GNNs) for high quality retrieval, and propose a simple yet effective discrete representation learning framework to jointly learn continuous and discrete codes.
Elastic Bulk Synchronous Parallel Model for Distributed Deep Learning
To ameliorate this shortcoming of classic BSP, we propose ELASTICBSP a model that aims to relax its strict synchronization requirement.
Attenuating Random Noise in Seismic Data by a Deep Learning Approach
In the geophysical field, seismic noise attenuation has been considered as a critical and long-standing problem, especially for the pre-stack data processing.
Generative Inpainting Network Applications on Seismic Image Compression and Non-Uniform Sampling
The use of deep learning models as priors for compressive sensing tasks presents new potential for inexpensive seismic data acquisition.
Scene Classification in Indoor Environments for Robots using Context Based Word Embeddings
Scene Classification has been addressed with numerous techniques in computer vision literature.
Dynamic Stale Synchronous Parallel Distributed Training for Deep Learning
In this paper, we present a distributed paradigm on the parameter server framework called Dynamic Stale Synchronous Parallel (DSSP) which improves the state-of-the-art Stale Synchronous Parallel (SSP) paradigm by dynamically determining the staleness threshold at the run time.
Spectral-Spatial Classification of Hyperspectral Image Using Autoencoders
Hyperspectral image (HSI) classification is a hot topic in the remote sensing community.
