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Found 18 papers, 7 papers with code
1st Place Solution for ICCV 2023 OmniObject3D Challenge: Sparse-View Reconstruction
In this report, we present the 1st place solution for ICCV 2023 OmniObject3D Challenge: Sparse-View Reconstruction.
Arbitrary-Scale Point Cloud Upsampling by Voxel-Based Network with Latent Geometric-Consistent Learning
Recently, arbitrary-scale point cloud upsampling mechanism became increasingly popular due to its efficiency and convenience for practical applications.
DocMSU: A Comprehensive Benchmark for Document-level Multimodal Sarcasm Understanding
Multimodal Sarcasm Understanding (MSU) has a wide range of applications in the news field such as public opinion analysis and forgery detection.
Image2PCI -- A Multitask Learning Framework for Estimating Pavement Condition Indices Directly from Images
The Pavement Condition Index (PCI) is a widely used metric for evaluating pavement performance based on the type, extent and severity of distresses detected on a pavement surface.
Rethinking the Approximation Error in 3D Surface Fitting for Point Cloud Normal Estimation
Most existing approaches for point cloud normal estimation aim to locally fit a geometric surface and calculate the normal from the fitted surface.
FBNet: Feedback Network for Point Cloud Completion
The rapid development of point cloud learning has driven point cloud completion into a new era.
Point Cloud Upsampling via Cascaded Refinement Network
In this manner, the proposed cascaded refinement network can be easily optimized without extra learning strategies.
Scale Attention for Learning Deep Face Representation: A Study Against Visual Scale Variation
Such multi-shot scheme brings inference burden, and the predefined scales inevitably have gap from real data.
A Hybrid Complex-valued Neural Network Framework with Applications to Electroencephalogram (EEG)
In this article, we present a new EEG signal classification framework by integrating the complex-valued and real-valued Convolutional Neural Network(CNN) with discrete Fourier transform (DFT).
Matching recovery threshold for correlated random graphs
For two correlated graphs which are independently sub-sampled from a common Erd\H{o}s-R\'enyi graph $\mathbf{G}(n, p)$, we wish to recover their \emph{latent} vertex matching from the observation of these two graphs \emph{without labels}.
Detection threshold for correlated Erdős-Rényi graphs via densest subgraphs
The problem of detecting edge correlation between two Erd\H{o}s-R\'enyi random graphs on $n$ unlabeled nodes can be formulated as a hypothesis testing problem: under the null hypothesis, the two graphs are sampled independently; under the alternative, the two graphs are independently sub-sampled from a parent graph which is Erd\H{o}s-R\'enyi $\mathbf{G}(n, p)$ (so that their marginal distributions are the same as the null).
Boosting Semi-Supervised Face Recognition with Noise Robustness
This paper presents an effective solution to semi-supervised face recognition that is robust to the label noise aroused by the auto-labelling.
Multi-Agent Semi-Siamese Training for Long-tail and Shallow Face Learning
However, unlike the existing public face datasets, in many real-world scenarios of face recognition, the depth of training dataset is shallow, which means only two face images are available for each ID.
Towards NIR-VIS Masked Face Recognition
In this paper, we aim to address the challenge of NIR-VIS masked face recognition from the perspectives of training data and training method.
The Elements of End-to-end Deep Face Recognition: A Survey of Recent Advances
To start with, we present an overview of the end-to-end deep face recognition.
Scene Text Detection with Selected Anchor
Object proposal technique with dense anchoring scheme for scene text detection were applied frequently to achieve high recall.
NPCFace: Negative-Positive Collaborative Training for Large-scale Face Recognition
However, the correlation between hard positive and hard negative is overlooked, and so is the relation between the margins in positive and negative logits.
Semi-Siamese Training for Shallow Face Learning
Extensive experiments on various benchmarks of face recognition show the proposed method significantly improves the training, not only in shallow face learning, but also for conventional deep face data.
