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Found 27 papers, 6 papers with code
Adversarial Nonnegative Matrix Factorization
Nonnegative Matrix Factorization (NMF) has become an increasingly important research topic in machine learning.
A sustainable development perspective on urban-scale roof greening priorities and benefits
Greenspaces are tightly linked to human well-being.
Diff-Reg v1: Diffusion Matching Model for Registration Problem
Establishing reliable correspondences is essential for registration tasks such as 3D and 2D3D registration.
Diff-PCR: Diffusion-Based Correspondence Searching in Doubly Stochastic Matrix Space for Point Cloud Registration
They typically compute candidate correspondences based on distances in the point feature space.
Training Neural Networks on RAW and HDR Images for Restoration Tasks
Our results indicate that neural networks train significantly better on HDR and RAW images represented in display-encoded color spaces, which offer better perceptual uniformity than linear spaces.
SGNet: Salient Geometric Network for Point Cloud Registration
These high-order features are then propagated to dense points and utilized by a Sinkhorn matching module to identify key correspondences for successful registration.
Urban GeoBIM construction by integrating semantic LiDAR point clouds with as-designed BIM models
In this paper, we propose a complementary strategy that integrates LiDAR point clouds with as-designed BIM models for reconstructing urban scenes.
AnyFlow: Arbitrary Scale Optical Flow with Implicit Neural Representation
To apply optical flow in practice, it is often necessary to resize the input to smaller dimensions in order to reduce computational costs.
Graph Matching Optimization Network for Point Cloud Registration
Point Cloud Registration is a fundamental and challenging problem in 3D computer vision.
Curriculum Temperature for Knowledge Distillation
In this paper, we propose a simple curriculum-based technique, termed Curriculum Temperature for Knowledge Distillation (CTKD), which controls the task difficulty level during the student's learning career through a dynamic and learnable temperature.
Denoising Multi-Similarity Formulation: A Self-paced Curriculum-Driven Approach for Robust Metric Learning
To efficiently solve this nonconvex quadratic problem, we propose a doubly stochastic projection coordinate gradient algorithm.
Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: Report
While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints.
Sampling Through the Lens of Sequential Decision Making
Due to the growth of large datasets and model complexity, we want to learn and adapt the sampling process while training a representation.
Open Source MagicData-RAMC: A Rich Annotated Mandarin Conversational(RAMC) Speech Dataset
As a Mandarin speech dataset designed for dialog scenarios with high quality and rich annotations, MagicData-RAMC enriches the data diversity in the Mandarin speech community and allows extensive research on a series of speech-related tasks, including automatic speech recognition, speaker diarization, topic detection, keyword search, text-to-speech, etc.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Efficient divide-and-conquer registration of UAV and ground LiDAR point clouds through canopy shape context
Finally, we implement coarse alignment of ULS and ground LiDAR datasets by combining the results of ground alignment and image matching and finish fine registration.
Noise Is Also Useful: Negative Correlation-Steered Latent Contrastive Learning
While in metric space, we utilize weakly-supervised contrastive learning to excavate these negative correlations hidden in noisy data.
Adaptive Hierarchical Similarity Metric Learning with Noisy Labels
Since these noisy labels often cause severe performance degradation, it is crucial to enhance the robustness and generalization ability of DML.
Unsupervised Hyperbolic Metric Learning
Learning feature embedding directly from images without any human supervision is a very challenging and essential task in the field of computer vision and machine learning.
Multi-object Tracking with a Hierarchical Single-branch Network
Recent Multiple Object Tracking (MOT) methods have gradually attempted to integrate object detection and instance re-identification (Re-ID) into a united network to form a one-stage solution.
Learning Better Visual Data Similarities via New Grouplet Non-Euclidean Embedding
However, pair-wise methods involve expensive training costs, while proxy-based methods are less accurate in characterizing the relationships between data points.
Shape-aware Generative Adversarial Networks for Attribute Transfer
Generative adversarial networks (GANs) have been successfully applied to transfer visual attributes in many domains, including that of human face images.
Pipelines for Procedural Information Extraction from Scientific Literature: Towards Recipes using Machine Learning and Data Science
This system meets computational information and knowledge management (CIKM) requirements of metadata-driven payload extraction, named entity extraction, and relationship extraction from text.
Curvilinear Distance Metric Learning
To address this issue, we first reveal that the traditional linear distance metric is equivalent to the cumulative arc length between the data pair's nearest points on the learned straight measurer lines.
Bilevel Distance Metric Learning for Robust Image Recognition
Metric learning, aiming to learn a discriminative Mahalanobis distance matrix M that can effectively reflect the similarity between data samples, has been widely studied in various image recognition problems.
Hate Speech Detection: A Solved Problem? The Challenging Case of Long Tail on Twitter
Our methods are evaluated on the largest collection of hate speech datasets based on Twitter, and are shown to be able to outperform the best performing method by up to 5 percentage points in macro-average F1, or 8 percentage points in the more challenging case of identifying hateful content.
A Computational Model of the Short-Cut Rule for 2D Shape Decomposition
We propose and implement a computational model for the short-cut rule and apply it to the problem of shape decomposition.
Nuclear Norm based Matrix Regression with Applications to Face Recognition with Occlusion and Illumination Changes
Compared with the current regression methods, the proposed Nuclear Norm based Matrix Regression (NMR) model is more robust for alleviating the effect of illumination, and more intuitive and powerful for removing the structural noise caused by occlusion.
