Search Results for author:
Found 40 papers, 13 papers with code
ICDAR 2015 Text Reading in the Wild Competition
Recently, text detection and recognition in natural scenes are becoming increasing popular in the computer vision community as well as the document analysis community.
Incidental Scene Text Understanding: Recent Progresses on ICDAR 2015 Robust Reading Competition Challenge 4
Different from focused texts present in natural images, which are captured with user's intention and intervention, incidental texts usually exhibit much more diversity, variability and complexity, thus posing significant difficulties and challenges for scene text detection and recognition algorithms.
Exploiting Local Structures with the Kronecker Layer in Convolutional Networks
In this paper, we propose and study a technique to reduce the number of parameters and computation time in convolutional neural networks.
DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients
We propose DoReFa-Net, a method to train convolutional neural networks that have low bitwidth weights and activations using low bitwidth parameter gradients.
Scene Text Detection via Holistic, Multi-Channel Prediction
Recently, scene text detection has become an active research topic in computer vision and document analysis, because of its great importance and significant challenge.
Ranked #6 on
Scene Text Detection
on COCO-Text
Effective Quantization Methods for Recurrent Neural Networks
In addition, we propose balanced quantization methods for weights to further reduce performance degradation.
Training Bit Fully Convolutional Network for Fast Semantic Segmentation
Fully convolutional neural networks give accurate, per-pixel prediction for input images and have applications like semantic segmentation.
EAST: An Efficient and Accurate Scene Text Detector
Previous approaches for scene text detection have already achieved promising performances across various benchmarks.
Ranked #3 on
Scene Text Detection
on COCO-Text
Curved Text Detection
Optical Character Recognition (OCR)
+1
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (e. g., 10-150 MFLOPs).
Ranked #79 on
Person Re-Identification
on DukeMTMC-reID
Learning to Run with Actor-Critic Ensemble
We introduce an Actor-Critic Ensemble(ACE) method for improving the performance of Deep Deterministic Policy Gradient(DDPG) algorithm.
Learning Delicate Local Representations for Multi-Person Pose Estimation
To tackle this problem, we propose an efficient attention mechanism - Pose Refine Machine (PRM) to make a trade-off between local and global representations in output features and further refine the keypoint locations.
Ranked #1 on
Keypoint Detection
on COCO test-challenge
DPGN: Distribution Propagation Graph Network for Few-shot Learning
To combine the distribution-level relations and instance-level relations for all examples, we construct a dual complete graph network which consists of a point graph and a distribution graph with each node standing for an example.
Ranked #2 on
Few-Shot Learning
on Mini-ImageNet - 1-Shot Learning
Component-wise Adaptive Trimming For Robust Mixture Regression
Existing robust mixture regression methods suffer from outliers as they either conduct parameter estimation in the presence of outliers, or rely on prior knowledge of the level of outlier contamination.
A Review of Automated Diagnosis of COVID-19 Based on Scanning Images
The pandemic of COVID-19 has caused millions of infections, which has led to a great loss all over the world, socially and economically.
Feature Space Singularity for Out-of-Distribution Detection
Our analysis of the phenomenon reveals why our algorithm works.
Ranked #1 on
Out-of-Distribution Detection
on MS-1M vs. IJB-C
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
RPPLNS: Pay-per-last-N-shares with a Randomised Twist
"Pay-per-last-$N$-shares" (PPLNS) is one of the most common payout strategies used by mining pools in Proof-of-Work (PoW) cryptocurrencies.
Fairness
Computer Science and Game Theory
Cryptography and Security
EventZoom: Learning To Denoise and Super Resolve Neuromorphic Events
EventZoom is trained in a noise-to-noise fashion where the two ends of the network are unfiltered noisy events, enforcing noise-free event restoration.
Machine Learning Applications in Forecasting of COVID-19 Based on Patients' Individual Symptoms
Predicting the COVID-19 outbreak has been studied by many researchers in recent years.
Nanorobot queue: Cooperative treatment of cancer based on team member communication and image processing
Although nanorobots have been used as clinical prescriptions for work such as gastroscopy, and even photoacoustic tomography technology has been proposed to control nanorobots to deliver drugs at designated delivery points in real time, and there are cases of eliminating "superbacteria" in blood through nanorobots, most technologies are immature, either with low efficiency or low accuracy, Either it can not be mass produced, so the most effective way to treat cancer diseases at this stage is through chemotherapy and radiotherapy.
EvUnroll: Neuromorphic Events Based Rolling Shutter Image Correction
This paper proposes to use neuromorphic events for correcting rolling shutter (RS) images as consecutive global shutter (GS) frames.
A New Learning Paradigm for Stochastic Configuration Network: SCN+
This novel algorithm can leverage privileged information into SCN in the training stage, which provides a new method to train SCN.
Injecting Image Details into CLIP's Feature Space
Although CLIP-like Visual Language Models provide a functional joint feature space for image and text, due to the limitation of the CILP-like model's image input size (e. g., 224), subtle details are lost in the feature representation if we input high-resolution images (e. g., 2240).
Joint Optimization of Energy Consumption and Completion Time in Federated Learning
Federated Learning (FL) is an intriguing distributed machine learning approach due to its privacy-preserving characteristics.
Resource Allocation of Federated Learning for the Metaverse with Mobile Augmented Reality
The Metaverse has received much attention recently.
Mobile Augmented Reality with Federated Learning in the Metaverse
The Metaverse is deemed the next evolution of the Internet and has received much attention recently.
Hierarchical Visual Categories Modeling: A Joint Representation Learning and Density Estimation Framework for Out-of-Distribution Detection
We use one fold as the in-distribution dataset and the others as out-of-distribution datasets to evaluate the proposed method.
NBMOD: Find It and Grasp It in Noisy Background
In the past few years, researchers have proposed many methods to address the above-mentioned issues and achieved very good results on publicly available datasets such as the Cornell dataset and the Jacquard dataset.
Ranked #1 on
Robotic Grasping
on NBMOD
(using extra training data)
Few shot font generation via transferring similarity guided global style and quantization local style
To better capture the local styles, a cross-attention-based style transfer module is adopted to transfer the styles of reference glyphs to the components, where the components are self-learned discrete latent codes through vector quantization without manual definition.
Towards Joint Modeling of Dialogue Response and Speech Synthesis based on Large Language Model
This paper explores the potential of constructing an AI spoken dialogue system that "thinks how to respond" and "thinks how to speak" simultaneously, which more closely aligns with the human speech production process compared to the current cascade pipeline of independent chatbot and Text-to-Speech (TTS) modules.
Logical Bias Learning for Object Relation Prediction
Scene graph generation (SGG) aims to automatically map an image into a semantic structural graph for better scene understanding.
Resource Allocation for Semantic Communication under Physical-layer Security
The secrecy rate is the communication rate at which no information is disclosed to an eavesdropper.
EvPlug: Learn a Plug-and-Play Module for Event and Image Fusion
The learned fusion module integrates event streams with image features in the form of a plug-in, endowing the RGB-based model to be robust to HDR and fast motion scenes while enabling high temporal resolution inference.
Me LLaMA: Foundation Large Language Models for Medical Applications
In response to this challenge, this study introduces Me-LLaMA, a novel medical LLM family that includes foundation models - Me-LLaMA 13/70B, along with their chat-enhanced versions - Me-LLaMA 13/70B-chat, developed through continual pre-training and instruction tuning of LLaMA2 using large medical datasets.
Reading Relevant Feature from Global Representation Memory for Visual Object Tracking
Therefore, using all features in the template and memory can lead to redundancy and impair tracking performance.
Learning to Deblur Polarized Images
However, since the on-chip micro-polarizers block part of the light so that the sensor often requires a longer exposure time, the captured polarized images are prone to motion blur caused by camera shakes, leading to noticeable degradation in the computed DoP and AoP.
Differentially Private Worst-group Risk Minimization
We first present a new algorithm that achieves excess worst-group population risk of $\tilde{O}(\frac{p\sqrt{d}}{K\epsilon} + \sqrt{\frac{p}{K}})$, where $K$ is the total number of samples drawn from all groups and $d$ is the problem dimension.
ClickVOS: Click Video Object Segmentation
To address these limitations, we propose the setting named Click Video Object Segmentation (ClickVOS) which segments objects of interest across the whole video according to a single click per object in the first frame.
Complementing Event Streams and RGB Frames for Hand Mesh Reconstruction
Experiments on real-world data demonstrate that EvRGBHand can effectively solve the challenging issues when using either type of camera alone via retaining the merits of both, and shows the potential of generalization to outdoor scenes and another type of event camera.
OneVOS: Unifying Video Object Segmentation with All-in-One Transformer Framework
Contemporary Video Object Segmentation (VOS) approaches typically consist stages of feature extraction, matching, memory management, and multiple objects aggregation.
OneTracker: Unifying Visual Object Tracking with Foundation Models and Efficient Tuning
To evaluate the effectiveness of our general framework OneTracker, which is consisted of Foundation Tracker and Prompt Tracker, we conduct extensive experiments on 6 popular tracking tasks across 11 benchmarks and our OneTracker outperforms other models and achieves state-of-the-art performance.
