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Found 20 papers, 10 papers with code
HiGraphDTI: Hierarchical Graph Representation Learning for Drug-Target Interaction Prediction
Specifically, HiGraphDTI learns hierarchical drug representations from triple-level molecular graphs to thoroughly exploit chemical information embedded in atoms, motifs, and molecules.
Enhancing Quality of Compressed Images by Mitigating Enhancement Bias Towards Compression Domain
However, these methods exhibit a pervasive enhancement bias towards the compression domain, inadvertently regarding it as more realistic than the raw domain.
Pixel Adapter: A Graph-Based Post-Processing Approach for Scene Text Image Super-Resolution
Additionally, we introduce the MLP-based Sequential Residual Block (MSRB) for robust feature extraction from text images, and a Local Contour Awareness loss ($\mathcal{L}_{lca}$) to enhance the model's perception of details.
Uncertainty Guided Adaptive Warping for Robust and Efficient Stereo Matching
A novel Uncertainty Guided Adaptive Correlation (UGAC) module is introduced to robustly adapt the same model for different scenarios.
Improving Grounded Language Understanding in a Collaborative Environment by Interacting with Agents Through Help Feedback
We explore multiple types of help players can give to the AI to guide it and analyze the impact of this help in AI behavior, resulting in performance improvements.
PIRNet: Privacy-Preserving Image Restoration Network via Wavelet Lifting
In this paper, we propose a novel method namely PIRNet, which operates privacy-preserving image restoration in the steganographic domain.
Neural Characteristic Function Learning for Conditional Image Generation
The emergence of conditional generative adversarial networks (cGANs) has revolutionised the way we approach and control the generation, by means of adversarially learning joint distributions of data and auxiliary information.
DAQE: Enhancing the Quality of Compressed Images by Exploiting the Inherent Characteristic of Defocus
Image defocus is inherent in the physics of image formation caused by the optical aberration of lenses, providing plentiful information on image quality.
PointVector: A Vector Representation In Point Cloud Analysis
In point cloud analysis, point-based methods have rapidly developed in recent years.
Ranked #1 on
3D Semantic Segmentation
on OpenTrench3D
Making Large Language Models Interactive: A Pioneer Study on Supporting Complex Information-Seeking Tasks with Implicit Constraints
Current interactive systems with natural language interfaces lack the ability to understand a complex information-seeking request which expresses several implicit constraints at once, and there is no prior information about user preferences e. g.,"find hiking trails around San Francisco which are accessible with toddlers and have beautiful scenery in summer", where output is a list of possible suggestions for users to start their exploration.
Joint Learning of Visual-Audio Saliency Prediction and Sound Source Localization on Multi-face Videos
In this paper, we propose a multitask learning method for visual-audio saliency prediction and sound source localization on multi-face video by leveraging visual, audio and face information.
Deep Homography for Efficient Stereo Image Compression
To fully explore the mutual information across two stereo images, we use a deep regression model to estimate the homography matrix, i. e., H matrix.
LAU-Net: Latitude Adaptive Upscaling Network for Omnidirectional Image Super-Resolution
In addition, we propose a deep reinforcement learning scheme with a latitude adaptive reward, in order to automatically select optimal upscaling factors for different latitude bands.
HiNet: Deep Image Hiding by Invertible Network
Capacity, invisibility and security are three primary challenges in image hiding task.
Multi-level Wavelet-based Generative Adversarial Network for Perceptual Quality Enhancement of Compressed Video
In this paper, we focus on enhancing the perceptual quality of compressed video.
Semi-Supervised Learning Approach to Discover Enterprise User Insights from Feedback and Support
In this paper, we proposed and developed an innovative Semi-Supervised Learning approach by utilizing Deep Learning and Topic Modeling to have a better understanding of the user voice. This approach combines a BERT-based multiclassification algorithm through supervised learning combined with a novel Probabilistic and Semantic Hybrid Topic Inference (PSHTI) Model through unsupervised learning, aiming at automating the process of better identifying the main topics or areas as well as the sub-topics from the textual feedback and support. There are three major break-through: 1.
Wavelet Domain Style Transfer for an Effective Perception-distortion Tradeoff in Single Image Super-Resolution
In this paper, we propose a novel method based on wavelet domain style transfer (WDST), which achieves a better PD tradeoff than the GAN based methods.
Deep Convolutional Neural Network for Multi-modal Image Restoration and Fusion
In this paper, we propose a novel deep convolutional neural network to solve the general multi-modal image restoration (MIR) and multi-modal image fusion (MIF) problems.
Multimodal Image Super-resolution via Joint Sparse Representations induced by Coupled Dictionaries
This paper proposes a new approach to construct a high-resolution (HR) version of a low-resolution (LR) image given another HR image modality as reference, based on joint sparse representations induced by coupled dictionaries.
Reducing Complexity of HEVC: A Deep Learning Approach
Therefore, this paper proposes a deep learning approach to predict the CU partition for reducing the HEVC complexity at both intra- and inter-modes, which is based on convolutional neural network (CNN) and long- and short-term memory (LSTM) network.
