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Found 20 papers, 6 papers with code
先秦词网构建及梵汉对比研究(The Construction of Pre-Qin Ancient Chinese WordNet and Cross Language Comparative Study between Ancient Sanskrit WordNet and Pre-Qin Ancient Chinese WordNet)
“先秦汉语在汉语史研究上具有重要地位, 然而以往的研究始终没有形成结构化的先秦词汇资源, 难以满足古汉语信息处理和跨语言对比的研究需要。国际上以英文词网(WordNet)的义类架构为基础, 已经建立了数十种语言的词网, 已经成为多语言自然语言处理和跨语言对比的基础资源。本文综述了国内外各种词网的构建情况, 特别是古代语言的词网和汉语词网, 然后详细介绍了先秦词网的构建和校正过程, 构建起了涵盖43591个词语、61227个义项、17975个义类的先秦汉语词网。本文还通过与古梵语词网的跨语言对比, 尝试分析这两种古老语言在词汇上的共性和差异, 初步验证先秦词网的有效性。”
Few-shot Forgery Detection via Guided Adversarial Interpolation
Realistic visual media synthesis is becoming a critical societal issue with the surge of face manipulation models; new forgery approaches emerge at an unprecedented pace.
X-Learner: Learning Cross Sources and Tasks for Universal Visual Representation
2) Squeeze Stage: X-Learner condenses the model to a reasonable size and learns the universal and generalizable representation for various tasks transferring.
Wasserstein Flow Meets Replicator Dynamics: A Mean-Field Analysis of Representation Learning in Actor-Critic
Specifically, we consider a version of AC where the actor and critic are represented by overparameterized two-layer neural networks and are updated with two-timescale learning rates.
INTERN: A New Learning Paradigm Towards General Vision
Enormous waves of technological innovations over the past several years, marked by the advances in AI technologies, are profoundly reshaping the industry and the society.
Video Temporal Relationship Mining for Data-Efficient Person Re-identification
This paper is a technical report to our submission to the ICCV 2021 VIPriors Re-identification Challenge.
ForgeryNet: A Versatile Benchmark for Comprehensive Forgery Analysis
To counter this emerging threat, we construct the ForgeryNet dataset, an extremely large face forgery dataset with unified annotations in image- and video-level data across four tasks: 1) Image Forgery Classification, including two-way (real / fake), three-way (real / fake with identity-replaced forgery approaches / fake with identity-remained forgery approaches), and n-way (real and 15 respective forgery approaches) classification.
Probing $μ$eV ALPs with future LHAASO observation of AGN $γ$-ray spectra
For $g_{a\gamma}\gtrsim 2\times$$10^{-11} \rm GeV^{-1}$ with $m_{a}\lesssim0. 5\,\mu$eV, the flux at around 100\, TeV predicted by the ALP model can be enhanced more than an order of magnitude than that from the standard absorption, and could be detected by LHAASO.
High Energy Astrophysical Phenomena Cosmology and Nongalactic Astrophysics High Energy Physics - Phenomenology
Maximum a posteriori signal recovery for optical coherence tomography angiography image generation and denoising
Optical coherence tomography angiography (OCTA) is a novel and clinically promising imaging modality to image retinal and sub-retinal vasculature.
RANDOM MASK: Towards Robust Convolutional Neural Networks
We next investigate the adversarial examples which 'fool' a CNN with Random Mask.
1st place solution for AVA-Kinetics Crossover in AcitivityNet Challenge 2020
This technical report introduces our winning solution to the spatio-temporal action localization track, AVA-Kinetics Crossover, in ActivityNet Challenge 2020.
Spatio-Temporal Action Localization
Temporal Action Localization
Actor-Context-Actor Relation Network for Spatio-Temporal Action Localization
We propose to explicitly model the Actor-Context-Actor Relation, which is the relation between two actors based on their interactions with the context.
Ranked #1 on
Action Recognition
on AVA v2.1
Defective Convolutional Networks
Robustness of convolutional neural networks (CNNs) has gained in importance on account of adversarial examples, i. e., inputs added as well-designed perturbations that are imperceptible to humans but can cause the model to predict incorrectly.
Defective Convolutional Layers Learn Robust CNNs
We first show that predictions made by the defective CNN are less dependent on textural information, but more on shape information, and further find that adversarial examples generated by the defective CNN appear to have semantic shapes.
Gram-Gauss-Newton Method: Learning Overparameterized Neural Networks for Regression Problems
First-order methods such as stochastic gradient descent (SGD) are currently the standard algorithm for training deep neural networks.
Deep Neural Network Based Hyperspectral Pixel Classification With Factorized Spectral-Spatial Feature Representation
Secondly, in order to utilize the local spatial correlation among pixels, we share the previous subnetwork as a spectral feature extractor for each pixel in a patch of image, after which the spectral features of all pixels in a patch are combined and feeded into the subsequent classification subnetwork.
A Deep Optimization Approach for Image Deconvolution
In blind image deconvolution, priors are often leveraged to constrain the solution space, so as to alleviate the under-determinacy.
Spectral Image Visualization Using Generative Adversarial Networks
The adversarial loss pushes our solution to the natural image distribution using a discriminator network that is trained to differentiate between false-color images and natural-color images.
Visualization of Hyperspectral Images Using Moving Least Squares
The matching pixels between a pair of HSI and RGB image can be reused to display other HSIs captured b the same imaging sensor with natural colors.
Predicting Aesthetic Score Distribution through Cumulative Jensen-Shannon Divergence
Thus, a novel CNN based on the Cumulative distribution with Jensen-Shannon divergence (CJS-CNN) is presented to predict the aesthetic score distribution of human ratings, with a new reliability-sensitive learning method based on the kurtosis of the score distribution, which eliminates the requirement of the original full data of human ratings (without normalization).
