Search Results for author:
Found 123 papers, 40 papers with code
Beyond 3DMM Space: Towards Fine-grained 3D Face Reconstruction
However, most of their training data is constructed by 3D Morphable Model, whose space spanned is only a small part of the shape space.
XGLUE: A New Benchmark Datasetfor Cross-lingual Pre-training, Understanding and Generation
In this paper, we introduce XGLUE, a new benchmark dataset to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora, and evaluate their performance across a diverse set of cross-lingual tasks.
LODE: Deep Local Deblurring and A New Benchmark
While recent deep deblurring algorithms have achieved remarkable progress, most existing methods focus on the global deblurring problem, where the image blur mostly arises from severe camera shake.
Improving Video-Text Retrieval by Multi-Stream Corpus Alignment and Dual Softmax Loss
In this paper, we propose a multi-stream Corpus Alignment network with single gate Mixture-of-Experts (CAMoE) and a novel Dual Softmax Loss (DSL) to solve the two heterogeneity.
Ranked #1 on
Video Retrieval
on MSVD
(using extra training data)
LinEasyBO: Scalable Bayesian Optimization Approach for Analog Circuit Synthesis via One-Dimensional Subspaces
A large body of literature has proved that the Bayesian optimization framework is especially efficient and effective in analog circuit synthesis.
Adaptive Label Smoothing To Regularize Large-Scale Graph Training
Graph neural networks (GNNs), which learn the node representations by recursively aggregating information from its neighbors, have become a predominant computational tool in many domains.
Efficient Visual Recognition with Deep Neural Networks: A Survey on Recent Advances and New Directions
Visual recognition is currently one of the most important and active research areas in computer vision, pattern recognition, and even the general field of artificial intelligence.
Two-step actuarial valuations
We introduce the class of actuarial-consistent valuation methods for insurance liabilities which depend on both financial and actuarial risks, which imposes that all actuarial risks are priced via standard actuarial principles.
Density-Based Dynamic Curriculum Learning for Intent Detection
To handle this problem, we propose a density-based dynamic curriculum learning model.
RAIN: Reinforced Hybrid Attention Inference Network for Motion Forecasting
Motion forecasting plays a significant role in various domains (e. g., autonomous driving, human-robot interaction), which aims to predict future motion sequences given a set of historical observations.
Opinion Prediction with User Fingerprinting
Experimental results show novel insights that were previously unknown such as better predictions for an increase in dynamic history length, the impact of the nature of the article on performance, thereby laying the foundation for further research.
An Efficient Asynchronous Batch Bayesian Optimization Approach for Analog Circuit Synthesis
A new strategy is proposed to better balance the exploration and exploitation and guarantee the diversity of the query points.
An Efficient Batch Constrained Bayesian Optimization Approach for Analog Circuit Synthesis via Multi-objective Acquisition Ensemble
After achieving the first feasible point, we favor the feasible region by adopting a specially designed penalization term to the acquisition function ensemble.
A Scalable 256-Elements E-Band Phased-Array Transceiver for Broadband Communication
For E-band wireless communications, a high gain steerable antenna with sub-arrays is desired to reduce the implementation complexity.
Probabilistic Model Distillation for Semantic Correspondence
We address this problem with the use of a novel Probabilistic Model Distillation (PMD) approach which transfers knowledge learned by a probabilistic teacher model on synthetic data to a static student model with the use of unlabeled real image pairs.
Model-Based Counterfactual Synthesizer for Interpretation
To address these limitations, we propose a Model-based Counterfactual Synthesizer (MCS) framework for interpreting machine learning models.
From Paraphrasing to Semantic Parsing: Unsupervised Semantic Parsing via Synchronous Semantic Decoding
During synchronous decoding: the utterance paraphrasing is constrained by the structure of the logical form, therefore the canonical utterance can be paraphrased controlledly; the semantic decoding is guided by the semantics of the canonical utterance, therefore its logical form can be generated unsupervisedly.
MlTr: Multi-label Classification with Transformer
The task of multi-label image classification is to recognize all the object labels presented in an image.
Ranked #4 on
Multi-Label Classification
on MS-COCO
MST: Masked Self-Supervised Transformer for Visual Representation
More importantly, the masked tokens together with the remaining tokens are further recovered by a global image decoder, which preserves the spatial information of the image and is more friendly to the downstream dense prediction tasks.
CAT: Cross Attention in Vision Transformer
In this paper, we propose a new attention mechanism in Transformer termed Cross Attention, which alternates attention inner the image patch instead of the whole image to capture local information and apply attention between image patches which are divided from single-channel feature maps capture global information.
Calibrating multi-dimensional complex ODE from noisy data via deep neural networks
Our method is able to recover the ODE system without being subject to the curse of dimensionality and complicated ODE structure.
Towards Compact CNNs via Collaborative Compression
Channel pruning and tensor decomposition have received extensive attention in convolutional neural network compression.
ModelPS: An Interactive and Collaborative Platform for Editing Pre-trained Models at Scale
AI engineering has emerged as a crucial discipline to democratize deep neural network (DNN) models among software developers with a diverse background.
CT-Net: Complementary Transfering Network for Garment Transfer with Arbitrary Geometric Changes
Garment transfer shows great potential in realistic applications with the goal of transfering outfits across different people images.
GODIVA: Generating Open-DomaIn Videos from nAtural Descriptions
Generating videos from text is a challenging task due to its high computational requirements for training and infinite possible answers for evaluation.
Mutual Graph Learning for Camouflaged Object Detection
Automatically detecting/segmenting object(s) that blend in with their surroundings is difficult for current models.
Improving Authorship Verification using Linguistic Divergence
It is also one of the first to use Deep Language Models in this setting.
ACDnet: An action detection network for real-time edge computing based on flow-guided feature approximation and memory aggregation
Interpreting human actions requires understanding the spatial and temporal context of the scenes.
CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning
Semi-supervised learning on class-imbalanced data, although a realistic problem, has been under studied.
Superresolving second-order correlation imaging using synthesized colored noise speckles
We present a novel method to synthesize non-trivial speckles that can enable superresolving second-order correlation imaging.
Optics Image and Video Processing
Sample canonical correlation coefficients of high-dimensional random vectors with finite rank correlations
If the entries of $\mathbf x$, $\mathbf y$ and $\mathbf z$ are i. i. d.
Probability
Cascade Network with Guided Loss and Hybrid Attention for Finding Good Correspondences
We then propose a hybrid attention block to extract feature, which integrates the Bayesian attentive context normalization (BACN) and channel-wise attention (CA).
Generative Counterfactuals for Neural Networks via Attribute-Informed Perturbation
With the wide use of deep neural networks (DNN), model interpretability has become a critical concern, since explainable decisions are preferred in high-stake scenarios.
Uncertainty-Guided Transformer Reasoning for Camouflaged Object Detection
Spotting objects that are visually adapted to their surroundings is challenging for both humans and AI.
Possible evidence of hydrogen emission in the first-overtone and multi-mode RR Lyrae variables
The nature of shock waves in non-fundamental mode RR Lyrae stars remains a mystery because of limited spectroscopic observations.
Solar and Stellar Astrophysics
A Polynomial Roth Theorem for Corners in Finite Fields
We prove a Roth type theorem for polynomial corners in the finite field setting.
Classical Analysis and ODEs Combinatorics Number Theory
Pattern-aware Data Augmentation for Query Rewriting in Voice Assistant Systems
Query rewriting (QR) systems are widely used to reduce the friction caused by errors in a spoken language understanding pipeline.
Multi-Aspect Sentiment Analysis with Latent Sentiment-Aspect Attribution
In this paper, we introduce a new framework called the sentiment-aspect attribution module (SAAM).
Predicting Personal Opinion on Future Events with Fingerprints
We evaluate the proposed model on this dataset.
Bayesian Multi-type Mean Field Multi-agent Imitation Learning
We demonstrate the performance of our algorithm through benchmarking with three state-of-the-art multi-agent imitation learning algorithms on several tasks, including solving a multi-agent traffic optimization problem in a real-world transportation network.
Fault-Aware Robust Control via Adversarial Reinforcement Learning
Robots have limited adaptation ability compared to humans and animals in the case of damage.
PAMS: Quantized Super-Resolution via Parameterized Max Scale
Specifically, most state-of-the-art SR models without batch normalization have a large dynamic quantization range, which also serves as another cause of performance drop.
Logic-guided Semantic Representation Learning for Zero-Shot Relation Classification
To recognize unseen relations at test time, we explore the problem of zero-shot relation classification.
Analysis of Information Transfer from Heterogeneous Sources via Precise High-dimensional Asymptotics
A fundamental question in transfer learning is whether combining the data of both tasks works better than using only the target task's data (equivalently, whether a "positive information transfer" happens).
Linear-time Temporal Logic with Team Semantics: Expressivity and Complexity
Logics for hyperproperties have so far been mostly obtained by extending temporal logics like LTL and QPTL with trace quantification, resulting in HyperLTL and HyperQPTL.
Logic in Computer Science Computational Complexity
Towards Fast, Accurate and Stable 3D Dense Face Alignment
Firstly, on the basis of a lightweight backbone, we propose a meta-joint optimization strategy to dynamically regress a small set of 3DMM parameters, which greatly enhances speed and accuracy simultaneously.
Ranked #1 on
Face Alignment
on AFLW
RelativeNAS: Relative Neural Architecture Search via Slow-Fast Learning
Despite the remarkable successes of Convolutional Neural Networks (CNNs) in computer vision, it is time-consuming and error-prone to manually design a CNN.
LaSOT: A High-quality Large-scale Single Object Tracking Benchmark
The average video length of LaSOT is around 2, 500 frames, where each video contains various challenge factors that exist in real world video footage, such as the targets disappearing and re-appearing.
Cascade Graph Neural Networks for RGB-D Salient Object Detection
Current works either simply distill prior knowledge from the corresponding depth map for handling the RGB-image or blindly fuse color and geometric information to generate the coarse depth-aware representations, hindering the performance of RGB-D saliency detectors. In this work, we introduceCascade Graph Neural Networks(Cas-Gnn), a unified framework which is capable of comprehensively distilling and reasoning the mutual benefits between these two data sources through a set of cascade graphs, to learn powerful representations for RGB-D salient object detection.
Ranked #4 on
RGB-D Salient Object Detection
on NJU2K
RGB-D Salient Object Detection
RGB Salient Object Detection
+1
PIC-Net: Point Cloud and Image Collaboration Network for Large-Scale Place Recognition
Place recognition is one of the hot research fields in automation technology and is still an open issue, Camera and Lidar are two mainstream sensors used in this task, Camera-based methods are easily affected by illumination and season changes, LIDAR cannot get the rich data as the image could , In this paper, we propose the PIC-Net (Point cloud and Image Collaboration Network), which use attention mechanism to fuse the features of image and point cloud, and mine the complementary information between the two.
Machine Learning Explanations to Prevent Overtrust in Fake News Detection
Combating fake news and misinformation propagation is a challenging task in the post-truth era.
Cascade Network with Guided Loss and Hybrid Attention for Two-view Geometry
We then propose a hybrid attention block to extract feature, which integrates the bayesian attentive context normalization (BACN) and channel-wise attention (CA).
ReMOTS: Self-Supervised Refining Multi-Object Tracking and Segmentation
We aim to improve the performance of Multiple Object Tracking and Segmentation (MOTS) by refinement.
Ranked #1 on
Multi-Object Tracking
on MOTS20
Multi-Object Tracking
Multi-Object Tracking and Segmentation
+1
Birds of a Feather Flock Together: Satirical News Detection via Language Model Differentiation
In this work, we propose a method that differentiates the satirical news and true news.
An Embarrassingly Simple Approach for Trojan Attack in Deep Neural Networks
In this paper, we investigate a specific security problem called trojan attack, which aims to attack deployed DNN systems relying on the hidden trigger patterns inserted by malicious hackers.
Defending SVMs against Poisoning Attacks: the Hardness and DBSCAN Approach
For the data sanitization defense, we link it to the intrinsic dimensionality of data; in particular, we provide a sampling theorem in doubling metrics for explaining the effectiveness of DBSCAN (as a density-based outlier removal method) for defending against poisoning attacks.
MSDU-net: A Multi-Scale Dilated U-net for Blur Detection
Blur detection is the separation of blurred and clear regions of an image, which is an important and challenging task in computer vision.
Acme: A Research Framework for Distributed Reinforcement Learning
Ultimately, we show that the design decisions behind Acme lead to agents that can be scaled both up and down and that, for the most part, greater levels of parallelization result in agents with equivalent performance, just faster.
Learning to Detect Head Movement in Unconstrained Remote Gaze Estimation in the Wild
To better demonstrate the advantage of our methods, we further propose a new benchmark dataset with the most rich distribution of head-gaze combination reflecting real-world scenarios.
XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation
In this paper, we introduce XGLUE, a new benchmark dataset that can be used to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora and evaluate their performance across a diverse set of cross-lingual tasks.
EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning
In this paper, we propose a generic trajectory forecasting framework (named EvolveGraph) with explicit relational structure recognition and prediction via latent interaction graphs among multiple heterogeneous, interactive agents.
Ranked #7 on
Trajectory Prediction
on Stanford Drone
Time Series Data Augmentation for Deep Learning: A Survey
In this paper, we systematically review different data augmentation methods for time series.
Hybrid Graph Neural Networks for Crowd Counting
In this paper, we present a novel network structure called Hybrid Graph Neural Network (HyGnn) which targets to relieve the problem by interweaving the multi-scale features for crowd density as well as its auxiliary task (localization) together and performing joint reasoning over a graph.
Tricritical physics in two-dimensional $p$-wave superfluids
When strong quantum fluctuations near resonance are taken into account, the line of continuous phase transitions terminates at two multicritical points near resonance, between which the transitions are expected to be first-order ones.
Quantum Gases
Relational State-Space Model for Stochastic Multi-Object Systems
Real-world dynamical systems often consist of multiple stochastic subsystems that interact with each other.
Game Design for Eliciting Distinguishable Behavior
The ability to inferring latent psychological traits from human behavior is key to developing personalized human-interacting machine learning systems.
Dually Supervised Feature Pyramid for Object Detection and Segmentation
Feature pyramid architecture has been broadly adopted in object detection and segmentation to deal with multi-scale problem.
Bayesian Optimization Approach for Analog Circuit Synthesis Using Neural Network
Bayesian optimization with Gaussian process as surrogate model has been successfully applied to analog circuit synthesis.
Detecting Unknown Behaviors by Pre-defined Behaviours: An Bayesian Non-parametric Approach
An automatic mouse behavior recognition system can considerably reduce the workload of experimenters and facilitate the analysis process.
Using Panoramic Videos for Multi-person Localization and Tracking in a 3D Panoramic Coordinate
3D panoramic multi-person localization and tracking are prominent in many applications, however, conventional methods using LiDAR equipment could be economically expensive and also computationally inefficient due to the processing of point cloud data.
TracKlinic: Diagnosis of Challenge Factors in Visual Tracking
For the analysis component, given the tracking results on all sequences, it investigates the behavior of the tracker under each individual factor and generates the report automatically.
XDeep: An Interpretation Tool for Deep Neural Networks
XDeep is an open-source Python package developed to interpret deep models for both practitioners and researchers.
A Hierarchical Mixture Density Network
One of the complicated relationships among three correlated variables could be a two-layer hierarchical many-to-many mapping.
TruNet: Short Videos Generation from Long Videos via Story-Preserving Truncation
In this work, we introduce a new problem, named as {\em story-preserving long video truncation}, that requires an algorithm to automatically truncate a long-duration video into multiple short and attractive sub-videos with each one containing an unbroken story.
Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks
Recently, increasing attention has been drawn to the internal mechanisms of convolutional neural networks, and the reason why the network makes specific decisions.
Contextual Local Explanation for Black Box Classifiers
We introduce a new model-agnostic explanation technique which explains the prediction of any classifier called CLE.
GLA-Net: An Attention Network with Guided Loss for Mismatch Removal
To establish the link between Fn-score and loss, we propose to guide the loss with the Fn-score directly.
Fairness in Deep Learning: A Computational Perspective
Deep learning is increasingly being used in high-stake decision making applications that affect individual lives.
Learning Credible Deep Neural Networks with Rationale Regularization
Recent explainability related studies have shown that state-of-the-art DNNs do not always adopt correct evidences to make decisions.
Exploring Deep Multimodal Fusion of Text and Photo for Hate Speech Classification
Interactions among users on social network platforms are usually positive, constructive and insightful.
Annotation-Free Cardiac Vessel Segmentation via Knowledge Transfer from Retinal Images
Segmenting coronary arteries is challenging, as classic unsupervised methods fail to produce satisfactory results and modern supervised learning (deep learning) requires manual annotation which is often time-consuming and can some time be infeasible.
Make Skeleton-based Action Recognition Model Smaller, Faster and Better
Although skeleton-based action recognition has achieved great success in recent years, most of the existing methods may suffer from a large model size and slow execution speed.
Evaluating Explanation Without Ground Truth in Interpretable Machine Learning
Interpretable Machine Learning (IML) has become increasingly important in many real-world applications, such as autonomous cars and medical diagnosis, where explanations are significantly preferred to help people better understand how machine learning systems work and further enhance their trust towards systems.
XFake: Explainable Fake News Detector with Visualizations
In this demo paper, we present the XFake system, an explainable fake news detector that assists end-users to identify news credibility.
Reconstructing Perceived Images from Brain Activity by Visually-guided Cognitive Representation and Adversarial Learning
In addition, we introduce a novel three-stage learning approach which enables the (cognitive) encoder to gradually distill useful knowledge from the paired (visual) encoder during the learning process.
Data Augmentation for Object Detection via Progressive and Selective Instance-Switching
For guiding our IS to obtain better object performance, we explore issues of instance imbalance and class importance in datasets, which frequently occur and bring adverse effect on detection performance.
Task Decomposition and Synchronization for Semantic Biomedical Image Segmentation
In this paper, we propose to decompose the single segmentation task into three subsequent sub-tasks, including (1) pixel-wise image segmentation, (2) prediction of the class labels of the objects within the image, and (3) classification of the scene the image belonging to.
Optimal Control of Complex Systems through Variational Inference with a Discrete Event Decision Process
However, such real-world complex system control is difficult to achieve because of high-dimensional and non-linear system dynamics, and the exploding state and action spaces for the decision maker.
Clustered Object Detection in Aerial Images
The key components in ClusDet include a cluster proposal sub-network (CPNet), a scale estimation sub-network (ScaleNet), and a dedicated detection network (DetecNet).
On Attribution of Recurrent Neural Network Predictions via Additive Decomposition
REAT decomposes the final prediction of a RNN into additive contribution of each word in the input text.
Low-bit Quantization of Neural Networks for Efficient Inference
Recent machine learning methods use increasingly large deep neural networks to achieve state of the art results in various tasks.
Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection
To demonstrate the superiority and generality of the proposed method, we evaluate the proposed method on five crack datasets and compare it with state-of-the-art crack detection, edge detection, semantic segmentation methods.
Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads
With widespread advances in machine learning, a number of large enterprises are beginning to incorporate machine learning models across a number of products.
Distributed, Parallel, and Cluster Computing
Efficient Image Retrieval via Decoupling Diffusion into Online and Offline Processing
Diffusion is commonly used as a ranking or re-ranking method in retrieval tasks to achieve higher retrieval performance, and has attracted lots of attention in recent years.
Ranked #1 on
Image Retrieval
on Par6k
Attention Driven Person Re-identification
Person re-identification (ReID) is a challenging task due to arbitrary human pose variations, background clutters, etc.
LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking
In this paper, we present LaSOT, a high-quality benchmark for Large-scale Single Object Tracking.
Contour Knowledge Transfer for Salient Object Detection
Our goal is to overcome this limitation by automatically converting an existing deep contour detection model into a salient object detection model without using any manual salient object masks.
Using Randomness to Improve Robustness of Machine-Learning Models Against Evasion Attacks
Machine learning models have been widely used in security applications such as intrusion detection, spam filtering, and virus or malware detection.
Attending Sentences to detect Satirical Fake News
Satirical news detection is important in order to prevent the spread of misinformation over the Internet.
Adversarial Learning for Fine-grained Image Search
Fine-grained image search is still a challenging problem due to the difficulty in capturing subtle differences regardless of pose variations of objects from fine-grained categories.
ModaNet: A Large-Scale Street Fashion Dataset with Polygon Annotations
Understanding clothes from a single image has strong commercial and cultural impacts on modern societies.
Batch Bayesian Optimization via Multi-objective Acquisition Ensemble for Automated Analog Circuit Design
In each iteration, the multi-objective optimization of the multiple acquisition functions is performed to search for the Pareto front of the acquisition functions.
Privacy-Protective-GAN for Face De-identification
In this paper, we propose a new framework called Privacy-Protective-GAN (PP-GAN) that adapts GAN with novel verificator and regulator modules specially designed for the face de-identification problem to ensure generating de-identified output with retained structure similarity according to a single input.
Large-scale Bisample Learning on ID Versus Spot Face Recognition
In this paper, we propose a deep learning based large-scale bisample learning (LBL) method for IvS face recognition.
Trajectory Factory: Tracklet Cleaving and Re-connection by Deep Siamese Bi-GRU for Multiple Object Tracking
In this paper, we propose a novel tracklet processing method to cleave and re-connect tracklets on crowd or long-term occlusion by Siamese Bi-Gated Recurrent Unit (GRU).
Ranked #14 on
Multi-Object Tracking
on MOT16
LEARNING TO ORGANIZE KNOWLEDGE WITH N-GRAM MACHINES
Existing end-to-end deep QA models (Miller et al., 2016; Weston et al., 2014) need to read the entire text after observing the question, and therefore their complexity in responding a question is linear in the text size.
Depth-Based 3D Hand Pose Estimation: From Current Achievements to Future Goals
Official Torch7 implementation of "V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map", CVPR 2018
Ranked #4 on
Hand Pose Estimation
on HANDS 2017
Expectation Propagation with Stochastic Kinetic Model in Complex Interaction Systems
Technological breakthroughs allow us to collect data with increasing spatio-temporal resolution from complex interaction systems.
Learning to Organize Knowledge and Answer Questions with N-Gram Machines
Though deep neural networks have great success in natural language processing, they are limited at more knowledge intensive AI tasks, such as open-domain Question Answering (QA).
Satirical News Detection and Analysis using Attention Mechanism and Linguistic Features
Satirical news is considered to be entertainment, but it is potentially deceptive and harmful.
Towards the Success Rate of One: Real-time Unconstrained Salient Object Detection
In this work, we propose an efficient and effective approach for unconstrained salient object detection in images using deep convolutional neural networks.
TensorLog: Deep Learning Meets Probabilistic DBs
We present an implementation of a probabilistic first-order logic called TensorLog, in which classes of logical queries are compiled into differentiable functions in a neural-network infrastructure such as Tensorflow or Theano.
Object-Aware Dense Semantic Correspondence
To address these problems, this paper proposes an object-aware method to estimate per-pixel correspondences from semantic to low-level by learning a classifier for each selected discriminative grid cell and guiding the localization of every pixel under the semantic constraint.
Visual Search at eBay
We harness the availability of large image collection of eBay listings and state-of-the-art deep learning techniques to perform visual search at scale.
Good Semi-supervised Learning that Requires a Bad GAN
Semi-supervised learning methods based on generative adversarial networks (GANs) obtained strong empirical results, but it is not clear 1) how the discriminator benefits from joint training with a generator, and 2) why good semi-supervised classification performance and a good generator cannot be obtained at the same time.
Differentiable Learning of Logical Rules for Knowledge Base Reasoning
We propose a framework, Neural Logic Programming, that combines the parameter and structure learning of first-order logical rules in an end-to-end differentiable model.
Leveraging Multiple Domains for Sentiment Classification
In addition, the learned feature representation can be used as classifier since our model defines the meaning of feature value and arranges high-level features in a prefixed order, so it is not necessary to train another classifier on top of the new features.
Exploit All the Layers: Fast and Accurate CNN Object Detector With Scale Dependent Pooling and Cascaded Rejection Classifiers
In this paper, we investigate two new strategies to detect objects accurately and efficiently using deep convolutional neural network: 1) scale-dependent pooling and 2) layer-wise cascaded rejection classifiers.
Multi-Task Learning With Low Rank Attribute Embedding for Person Re-Identification
Since attributes are generally correlated, we introduce a low rank attribute embedding into the MTL formulation to embed original binary attributes to a continuous attribute space, where incorrect and incomplete attributes are rectified and recovered to better describe people.
Exploiting Local Features from Deep Networks for Image Retrieval
Deep convolutional neural networks have been successfully applied to image classification tasks.