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Found 34 papers, 9 papers with code
NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29. 00dB on DIV2K validation set.
CE-based white-box adversarial attacks will not work using super-fitting
This is especially dangerous for some systems with high-security requirements, so this paper proposes a new defense method by using the model super-fitting state to improve the model's adversarial robustness (i. e., the accuracy under adversarial attacks).
Rethinking Classifier and Adversarial Attack
Various defense models have been proposed to resist adversarial attack algorithms, but existing adversarial robustness evaluation methods always overestimate the adversarial robustness of these models (i. e., not approaching the lower bound of robustness).
IMOT: General-Purpose, Fast and Robust Estimation for Spatial Perception Problems with Outliers
Spatial perception problems are the fundamental building blocks of robotics and computer vision.
Robust Table Detection and Structure Recognition from Heterogeneous Document Images
We introduce a new table detection and structure recognition approach named RobusTabNet to detect the boundaries of tables and reconstruct the cellular structure of the table from heterogeneous document images.
Event-Based Fusion for Motion Deblurring with Cross-modal Attention
Traditional frame-based cameras inevitably suffer from motion blur due to long exposure times.
Persia: An Open, Hybrid System Scaling Deep Learning-based Recommenders up to 100 Trillion Parameters
Specifically, in order to ensure both the training efficiency and the training accuracy, we design a novel hybrid training algorithm, where the embedding layer and the dense neural network are handled by different synchronization mechanisms; then we build a system called Persia (short for parallel recommendation training system with hybrid acceleration) to support this hybrid training algorithm.
Practical, Fast and Robust Point Cloud Registration for 3D Scene Stitching and Object Localization
3D point cloud registration ranks among the most fundamental problems in remote sensing, photogrammetry, robotics and geometric computer vision.
TriVoC: Efficient Voting-based Consensus Maximization for Robust Point Cloud Registration with Extreme Outlier Ratios
Correspondence-based point cloud registration is a cornerstone in robotics perception and computer vision, which seeks to estimate the best rigid transformation aligning two point clouds from the putative correspondences.
DANIEL: A Fast and Robust Consensus Maximization Method for Point Cloud Registration with High Outlier Ratios
In this paper, we present a novel time-efficient RANSAC-type consensus maximization solver, named DANIEL (Double-layered sAmpliNg with consensus maximization based on stratIfied Element-wise compatibiLity), for robust registration.
Conditional DETR for Fast Training Convergence
Our approach, named conditional DETR, learns a conditional spatial query from the decoder embedding for decoder multi-head cross-attention.
Separation Guided Speaker Diarization in Realistic Mismatched Conditions
We propose a separation guided speaker diarization (SGSD) approach by fully utilizing a complementarity of speech separation and speaker clustering.
On the Connection between Local Attention and Dynamic Depth-wise Convolution
Sparse connectivity: there is no connection across channels, and each position is connected to the positions within a small local window.
ViBERTgrid: A Jointly Trained Multi-Modal 2D Document Representation for Key Information Extraction from Documents
In this paper, we propose a new multi-modal backbone network by concatenating a BERTgrid to an intermediate layer of a CNN model, where the input of CNN is a document image and the BERTgrid is a grid of word embeddings, to generate a more powerful grid-based document representation, named ViBERTgrid.
Aerial-PASS: Panoramic Annular Scene Segmentation in Drone Videos
A lightweight panoramic annular semantic segmentation neural network model is designed to achieve high-accuracy and real-time scene parsing.
Ranked #47 on
Semantic Segmentation
on Cityscapes val
ICOS: Efficient and Highly Robust Rotation Search and Point Cloud Registration with Correspondences
Rotation search and point cloud registration are two fundamental problems in robotics and computer vision, which aim to estimate the rotation and the transformation between the 3D vector sets and point clouds, respectively.
RANSIC: Fast and Highly Robust Estimation for Rotation Search and Point Cloud Registration using Invariant Compatibility
Correspondence-based rotation search and point cloud registration are two fundamental problems in robotics and computer vision.
USTC-NELSLIP System Description for DIHARD-III Challenge
This system description describes our submission system to the Third DIHARD Speech Diarization Challenge.
IRON: Invariant-based Highly Robust Point Cloud Registration
Once the scale is estimated, our second contribution is to relax the non-convex global registration problem into a convex Semi-Definite Program (SDP) in a certifiable way using Sum-of-Squares (SOS) Relaxation and show that the relaxation is tight.
The Post-impact Evolution of the X-ray Emitting Gas in SNR 1987A Viewed by XMM-Newton
In the last few years, the emission measure of the low-temperature plasma has been decreasing, indicating that the blast wave has left the main ER.
High Energy Astrophysical Phenomena
P-KDGAN: Progressive Knowledge Distillation with GANs for One-class Novelty Detection
The progressive learning of knowledge distillation is a two-step approach that continuously improves the performance of the student GAN and achieves better performance than single step methods.
Ranked #1 on
Unsupervised Anomaly Detection
on MNIST
Adaptive Operator Selection Based on Dynamic Thompson Sampling for MOEA/D
In particular, each arm of our bandit learning model represents a reproduction operator and is assigned with a prior reward distribution.
ReLaText: Exploiting Visual Relationships for Arbitrary-Shaped Scene Text Detection with Graph Convolutional Networks
The key idea is to decompose text detection into two subproblems, namely detection of text primitives and prediction of link relationships between nearby text primitive pairs.
Real-time Fusion Network for RGB-D Semantic Segmentation Incorporating Unexpected Obstacle Detection for Road-driving Images
Semantic segmentation has made striking progress due to the success of deep convolutional neural networks.
Ranked #13 on
Semantic Segmentation
on EventScape
Speaker detection in the wild: Lessons learned from JSALT 2019
This paper presents the problems and solutions addressed at the JSALT workshop when using a single microphone for speaker detection in adverse scenarios.
Audio and Speech Processing Sound
See Clearer at Night: Towards Robust Nighttime Semantic Segmentation through Day-Night Image Conversion
However, in face of adverse conditions such as the nighttime, semantic segmentation loses its accuracy significantly.
Ranked #4 on
Semantic Segmentation
on Nighttime Driving
Adversarial Balancing-based Representation Learning for Causal Effect Inference with Observational Data
The challenges for this problem are two-fold: on the one hand, we have to derive a causal estimator to estimate the causal quantity from observational data, where there exists confounding bias; on the other hand, we have to deal with the identification of CATE when the distribution of covariates in treatment and control groups are imbalanced.
Solving the Empirical Bayes Normal Means Problem with Correlated Noise
The Normal Means problem plays a fundamental role in many areas of modern high-dimensional statistics, both in theory and practice.
Mask R-CNN with Pyramid Attention Network for Scene Text Detection
In this paper, we present a new Mask R-CNN based text detection approach which can robustly detect multi-oriented and curved text from natural scene images in a unified manner.
Ranked #3 on
Scene Text Detection
on SCUT-CTW1500
Gradient-Free Learning Based on the Kernel and the Range Space
In this article, we show that solving the system of linear equations by manipulating the kernel and the range space is equivalent to solving the problem of least squares error approximation.
Deterministic Stretchy Regression
An extension of the regularized least-squares in which the estimation parameters are stretchable is introduced and studied in this paper.
An Anchor-Free Region Proposal Network for Faster R-CNN based Text Detection Approaches
The anchor mechanism of Faster R-CNN and SSD framework is considered not effective enough to scene text detection, which can be attributed to its IoU based matching criterion between anchors and ground-truth boxes.
Bayesian $l_0$-regularized Least Squares
To illustrate our methodology, we provide simulation evidence and a real data example on the statistical properties and computational efficiency of SBR versus direct posterior sampling using spike-and-slab priors.
Constrained Maximum Correntropy Adaptive Filtering
Most existing constrained adaptive filtering algorithms are developed under mean square error (MSE) criterion, which is an ideal optimality criterion under Gaussian noises.
