Search Results for author:
Found 23 papers, 6 papers with code
Dynamic texture and scene classification by transferring deep image features
Moreover we explore two different implementations of the TCoF scheme, i. e., the \textit{spatial} TCoF and the \textit{temporal} TCoF, in which the mean-removed frames and the difference between two adjacent frames are used as the inputs of the ConvNet, respectively.
HEp-2 Cell Classification via Fusing Texture and Shape Information
Indirect Immunofluorescence (IIF) HEp-2 cell image is an effective evidence for diagnosis of autoimmune diseases.
HEp-2 Cell Classification: The Role of Gaussian Scale Space Theory as A Pre-processing Approach
We validate the GSS pre-processing under the Local Binary Pattern (LBP) and the Bag-of-Words (BoW) frameworks.
Towards Reading Hidden Emotions: A comparative Study of Spontaneous Micro-expression Spotting and Recognition Methods
For ME recognition, the performance of previous studies is low.
Probing the Intra-Component Correlations within Fisher Vector for Material Classification
Fisher vector (FV) has become a popular image representation.
Two decades of local binary patterns: A survey
In recent years very discriminative and computationally efficient local texture descriptors based on local binary patterns (LBP) have been developed, which has led to significant progress in applying texture methods to different problems and applications.
PCANet-II: When PCANet Meets the Second Order Pooling
Moreover, it is easy to combine other discriminative and robust cues by using the second order pooling.
Texture Classification in Extreme Scale Variations using GANet
Because extreme scale variations are not necessarily present in most standard texture databases, to support the proposed extreme-scale aspects of texture understanding we are developing a new dataset, the Extreme Scale Variation Textures (ESVaT), to test the performance of our framework.
Deep Learning for Generic Object Detection: A Survey
Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images.
Extended Local Binary Patterns for Efficient and Robust Spontaneous Facial Micro-Expression Recognition
It is computationally efficient and only marginally increases the cost of computing LBPTOP, yet is extremely effective for ME recognition.
Dynamic Group Convolution for Accelerating Convolutional Neural Networks
In this paper, we propose dynamic group convolution (DGC) that adaptively selects which part of input channels to be connected within each group for individual samples on the fly.
FTBNN: Rethinking Non-linearity for 1-bit CNNs and Going Beyond
Binary neural networks (BNNs), where both weights and activations are binarized into 1 bit, have been widely studied in recent years due to its great benefit of highly accelerated computation and substantially reduced memory footprint that appeal to the development of resource constrained devices.
Deep Learning for Scene Classification: A Survey
Scene classification, aiming at classifying a scene image to one of the predefined scene categories by comprehending the entire image, is a longstanding, fundamental and challenging problem in computer vision.
Pixel Difference Networks for Efficient Edge Detection
A faster version of PiDiNet with less than 0. 1M parameters can still achieve comparable performance among state of the arts with 200 FPS.
Ranked #2 on
Edge Detection
on BRIND
Dynamic Binary Neural Network by learning channel-wise thresholds
The experimental results prove that our method is an effective and straightforward way to reduce information loss and enhance performance of BNNs.
Challenges of Artificial Intelligence -- From Machine Learning and Computer Vision to Emotional Intelligence
We present the basics of emotional intelligence and our own research on the topic.
Deep Learning for Visual Speech Analysis: A Survey
Visual speech, referring to the visual domain of speech, has attracted increasing attention due to its wide applications, such as public security, medical treatment, military defense, and film entertainment.
SVNet: Where SO(3) Equivariance Meets Binarization on Point Cloud Representation
Precisely, the presence of scalar features makes the major part of the network binarizable, while vector features serve to retain rich structural information and ensure SO(3) equivariance.
Boosting Binary Neural Networks via Dynamic Thresholds Learning
For ViTs, DyBinaryCCT presents the superiority of the convolutional embedding layer in fully binarized ViTs and achieves 56. 1% on the ImageNet dataset, which is nearly 9% higher than the baseline.
From Local Binary Patterns to Pixel Difference Networks for Efficient Visual Representation Learning
LBP is a successful hand-crafted feature descriptor in computer vision.
Boosting Convolutional Neural Networks with Middle Spectrum Grouped Convolution
This paper proposes a novel module called middle spectrum grouped convolution (MSGC) for efficient deep convolutional neural networks (DCNNs) with the mechanism of grouped convolution.
Few-shot Class-incremental Learning: A Survey
In our in-depth examination, we delve into various facets of FSCIL, encompassing the problem definition, the discussion of the primary challenges of unreliable empirical risk minimization and the stability-plasticity dilemma, general schemes, and relevant problems of IL and Few-shot Learning (FSL).
Class-Incremental Object Detection
Few-Shot Class-Incremental Learning
+4
Lightweight Pixel Difference Networks for Efficient Visual Representation Learning
With PDC and Bi-PDC, we further present two lightweight deep networks named \emph{Pixel Difference Networks (PiDiNet)} and \emph{Binary PiDiNet (Bi-PiDiNet)} respectively to learn highly efficient yet more accurate representations for visual tasks including edge detection and object recognition.
