Search Results for author:
Found 116 papers, 54 papers with code
Complicate Then Simplify: A Novel Way to Explore Pre-trained Models for Text Classification
However such way might not fully explore the knowledge in PTMs as it is constrained by the difficulty of the task.
Ultra Lowrate Image Compression with Semantic Residual Coding and Compression-aware Diffusion
Existing multimodal large model-based image compression frameworks often rely on a fragmented integration of semantic retrieval, latent compression, and generative models, resulting in suboptimal performance in both reconstruction fidelity and coding efficiency.
Pay More Attention to the Robustness of Prompt for Instruction Data Mining
Instruction tuning has emerged as a paramount method for tailoring the behaviors of LLMs.
MAVERIX: Multimodal Audio-Visual Evaluation Reasoning IndeX
Frontier models have either been language-only or have primarily focused on vision and language modalities.
Histomorphology-driven multi-instance learning for breast cancer WSI classification
With the incorporation of histomorphological information, our framework strengthens the model's ability to capture key and fine-grained pathological patterns, thereby enhancing WSI classification performance.
Enabling Versatile Controls for Video Diffusion Models
Despite substantial progress in text-to-video generation, achieving precise and flexible control over fine-grained spatiotemporal attributes remains a significant unresolved challenge in video generation research.
AA-CLIP: Enhancing Zero-shot Anomaly Detection via Anomaly-Aware CLIP
To address this problem, we propose Anomaly-Aware CLIP (AA-CLIP), which enhances CLIP's anomaly discrimination ability in both text and visual spaces while preserving its generalization capability.
Optimizing Robustness and Accuracy in Mixture of Experts: A Dual-Model Approach
To push the boundaries of robustness and accuracy, we propose a novel joint training strategy JTDMoE for the dual-model.
UniUIR: Considering Underwater Image Restoration as An All-in-One Learner
Existing underwater image restoration (UIR) methods generally only handle color distortion or jointly address color and haze issues, but they often overlook the more complex degradations that can occur in underwater scenes.
A Low-cost and Ultra-lightweight Binary Neural Network for Traffic Signal Recognition
Herein, we propose an ultra-lightweight binary neural network (BNN) model designed for hardware deployment, and conduct image classification research based on the German Traffic Sign Recognition Benchmark (GTSRB) dataset.
MobileNetV2: A lightweight classification model for home-based sleep apnea screening
For sleep stage classification, in UCDDB dataset, the ROC-AUC exceeded 0. 85 across all stages, with recall for Sleep reaching 0. 906 and specificity for REM and Wake states at 0. 956 and 0. 937, respectively.
FGP: Feature-Gradient-Prune for Efficient Convolutional Layer Pruning
To reduce computational overhead while maintaining model performance, model pruning techniques have been proposed.
QARM: Quantitative Alignment Multi-Modal Recommendation at Kuaishou
In recent years, with the significant evolution of multi-modal large models, many recommender researchers realized the potential of multi-modal information for user interest modeling.
Joint multi-dimensional dynamic attention and transformer for general image restoration
Outdoor images often suffer from severe degradation due to rain, haze, and noise, impairing image quality and challenging high-level tasks.
Similarity and Dissimilarity Guided Co-association Matrix Construction for Ensemble Clustering
Finally, the adversarial relationship between the similarity matrix and the dissimilarity matrix is utilized to construct a promoted CA matrix for ensemble clustering.
EMWaveNet: Physically Explainable Neural Network Based on Electromagnetic Propagation for SAR Target Recognition
Deep learning technologies have significantly improved performance in the field of synthetic aperture radar (SAR) image target recognition compared to traditional methods.
All-in-One Image Coding for Joint Human-Machine Vision with Multi-Path Aggregation
Experimental results show that MPA achieves performance comparable to state-of-the-art methods in both task-specific and multi-objective optimization across human viewing and machine analysis tasks.
Perceive-IR: Learning to Perceive Degradation Better for All-in-One Image Restoration
Existing All-in-One image restoration methods often fail to perceive degradation types and severity levels simultaneously, overlooking the importance of fine-grained quality perception.
RSTeller: Scaling Up Visual Language Modeling in Remote Sensing with Rich Linguistic Semantics from Openly Available Data and Large Language Models
Abundant, well-annotated multimodal data in remote sensing are pivotal for aligning complex visual remote sensing (RS) scenes with human language, enabling the development of specialized vision language models across diverse RS interpretation tasks.
Enhancing Adaptive Deep Networks for Image Classification via Uncertainty-aware Decision Fusion
Based on this observation, we introduce the Collaborative Decision Making (CDM) module, which fuses the multiple classifier heads to enhance the inference performance of adaptive deep networks.
Multi-Agent Continuous Control with Generative Flow Networks
Generative Flow Networks (GFlowNets) aim to generate diverse trajectories from a distribution in which the final states of the trajectories are proportional to the reward, serving as a powerful alternative to reinforcement learning for exploratory control tasks.
3D-UGCN: A Unified Graph Convolutional Network for Robust 3D Human Pose Estimation from Monocular RGB Images
Human pose estimation remains a multifaceted challenge in computer vision, pivotal across diverse domains such as behavior recognition, human-computer interaction, and pedestrian tracking.
Probing many-body Bell correlation depth with superconducting qubits
As an illustrating example, we variationally prepare the low-energy state of a two-dimensional honeycomb model with 73 qubits and certify its Bell correlations by measuring an energy that surpasses the corresponding classical bound with up to 48 standard deviations.
MC-MKE: A Fine-Grained Multimodal Knowledge Editing Benchmark Emphasizing Modality Consistency
Our benchmark facilitates independent correction of misreading and misrecognition errors by editing the corresponding knowledge component.
ContraSolver: Self-Alignment of Language Models by Resolving Internal Preference Contradictions
While substantial advancements have been made in developing large language models (LLMs), achieving control over their behavior can be difficult.
Anomaly Detection Utilizing a Riemann Metric for Robust Myoelectric Pattern Recognition
Traditional myoelectric pattern recognition (MPR) systems excel within controlled laboratory environments but they are interfered when confronted with anomaly or novel motions not encountered during the training phase.
Fast networked data selection via distributed smoothed quantile estimation
Leveraging the piecewise linearity of the local objective functions in quantile estimation, we characterize the iteration complexity required to achieve top-$k$ selection, a challenging task due to the lack of strong convexity.
DMPlug: A Plug-in Method for Solving Inverse Problems with Diffusion Models
However, such interleaving methods struggle to produce final results that look like natural objects of interest (i. e., manifold feasibility) and fit the measurement (i. e., measurement feasibility), especially for nonlinear IPs.
DFGNN: Dual-frequency Graph Neural Network for Sign-aware Feedback
Based on the two observations, we propose a novel model that models positive and negative feedback from a frequency filter perspective called Dual-frequency Graph Neural Network for Sign-aware Recommendation (DFGNN).
StyleSeg V2: Towards Robust One-shot Segmentation of Brain Tissue via Optimization-free Registration Error Perception
One-shot segmentation of brain tissue requires training registration-segmentation (reg-seg) dual-model iteratively, where reg-model aims to provide pseudo masks of unlabeled images for seg-model by warping a carefully-labeled atlas.
ID-centric Pre-training for Recommendation
Specifically, in pre-training stage, besides the ID-based sequential model for recommendation, we also build a Cross-domain ID-matcher (CDIM) learned by both behavioral and modality information.
DPA-Net: Structured 3D Abstraction from Sparse Views via Differentiable Primitive Assembly
We present a differentiable rendering framework to learn structured 3D abstractions in the form of primitive assemblies from sparse RGB images capturing a 3D object.
Content-Based Collaborative Generation for Recommender Systems
In this paper, we propose content-based collaborative generation for recommender systems, namely ColaRec.
Projected Gradient Descent for Spectral Compressed Sensing via Symmetric Hankel Factorization
Current spectral compressed sensing methods via Hankel matrix completion employ symmetric factorization to demonstrate the low-rank property of the Hankel matrix.
Enhancing Jailbreak Attacks with Diversity Guidance
Therefore, we propose DPP-based Stochastic Trigger Searching (DSTS), a new optimization algorithm for jailbreak attacks.
Benchmarking Knowledge Boundary for Large Language Models: A Different Perspective on Model Evaluation
In recent years, substantial advancements have been made in the development of large language models, achieving remarkable performance across diverse tasks.
UFO: A UI-Focused Agent for Windows OS Interaction
We introduce UFO, an innovative UI-Focused agent to fulfill user requests tailored to applications on Windows OS, harnessing the capabilities of GPT-Vision.
Retrosynthesis prediction enhanced by in-silico reaction data augmentation
As a response, we exploit easy-to-access unpaired data (i. e., one component of product-reactant(s) pair) for generating in-silico paired data to facilitate model training.
UniVG: Towards UNIfied-modal Video Generation
This cannot fully meet the needs of real-world application scenarios, as users are likely to input images and text conditions in a flexible manner, either individually or in combination.
Plug-in Diffusion Model for Sequential Recommendation
To address this issue, this paper presents a novel Plug-in Diffusion Model for Recommendation (PDRec) framework, which employs the diffusion model as a flexible plugin to jointly take full advantage of the diffusion-generating user preferences on all items.
Understanding LLMs: A Comprehensive Overview from Training to Inference
Low-cost training and deployment of LLMs represent the future development trend.
Learning Surface Scattering Parameters From SAR Images Using Differentiable Ray Tracing
Furthermore, a differentiable ray tracing (DRT) engine based on SAR images was constructed for CSVBSDF surface scattering parameter learning.
Negative Pre-aware for Noisy Cross-modal Matching
Since clean samples are easier distinguished by GMM with increasing noise, the memory bank can still maintain high quality at a high noise ratio.
Cross-modal retrieval with noisy correspondence
Image-text matching
+3
TaskWeaver: A Code-First Agent Framework
TaskWeaver provides support for rich data structures, flexible plugin usage, and dynamic plugin selection, and leverages LLM coding capabilities for complex logic.
Semantically Grounded QFormer for Efficient Vision Language Understanding
Consequently, instead of using QFormer latents as inputs to the LLM, we alter the framework by using the latents to directly condition the LLM latent space for image-to-text generation.
Holistic Evaluation of GPT-4V for Biomedical Imaging
GPT-4V represents a breakthrough in artificial general intelligence (AGI) for computer vision, with applications in the biomedical domain.
An Empirical Study of Instruction-tuning Large Language Models in Chinese
This paper will release a powerful Chinese LLMs that is comparable to ChatGLM.
Multiagent Reinforcement Learning with an Attention Mechanism for Improving Energy Efficiency in LoRa Networks
Long Range (LoRa) wireless technology, characterized by low power consumption and a long communication range, is regarded as one of the enabling technologies for the Industrial Internet of Things (IIoT).
MulMarker: a comprehensive framework for identifying multi-gene prognostic signatures
Prognostic signatures play an important role in clinical research, offering insights into the potential health outcomes of patients and guiding therapeutic decisions.
FashionNTM: Multi-turn Fashion Image Retrieval via Cascaded Memory
Extensive evaluation results show that our proposed method outperforms the previous state-of-the-art algorithm by 50. 5%, on Multi-turn FashionIQ -- the only existing multi-turn fashion dataset currently, in addition to having a relative improvement of 12. 6% on Multi-turn Shoes -- an extension of the single-turn Shoes dataset that we created in this work.
TVPR: Text-to-Video Person Retrieval and a New Benchmark
To the best of our knowledge, MFGF is the first successful attempt to use video for text-based person retrieval task and has achieved state-of-the-art performance on TVPReid dataset.
Improved NL2SQL based on Multi-layer Expert Network
The Natural Language to SQL (NL2SQL) technique is used to convert natural language queries into executable SQL statements.
Feature Representation Learning for NL2SQL Generation Based on Coupling and Decoupling
To address this issue, we propose the Clause Feature Correlation Decoupling and Coupling (CFCDC) model, which uses a feature representation decoupling method to separate the SELECT and WHERE clauses at the parameter level.
Meta Generative Flow Networks with Personalization for Task-Specific Adaptation
Multi-task reinforcement learning and meta-reinforcement learning have been developed to quickly adapt to new tasks, but they tend to focus on tasks with higher rewards and more frequent occurrences, leading to poor performance on tasks with sparse rewards.
PVPUFormer: Probabilistic Visual Prompt Unified Transformer for Interactive Image Segmentation
To tackle this problem, this paper proposes a simple yet effective Probabilistic Visual Prompt Unified Transformer (PVPUFormer) for interactive image segmentation, which allows users to flexibly input diverse visual prompts with the probabilistic prompt encoding and feature post-processing to excavate sufficient and robust prompt features for performance boosting.
Collaborative Group: Composed Image Retrieval via Consensus Learning from Noisy Annotations
Triplet ambiguity refers to a type of semantic ambiguity that arises between the reference image, the relative caption, and the target image.
Content-Based Image Retrieval
Image Retrieval with Multi-Modal Query
+2
GRACE: Loss-Resilient Real-Time Video through Neural Codecs
In real-time video communication, retransmitting lost packets over high-latency networks is not viable due to strict latency requirements.
Image-text Retrieval via Preserving Main Semantics of Vision
To address this issue, this paper presents a semantic optimization approach, implemented as a Visual Semantic Loss (VSL), to assist the model in focusing on an image's main content.
Triple Sequence Learning for Cross-domain Recommendation
To address this issue, we present a novel framework, termed triple sequence learning for cross-domain recommendation (Tri-CDR), which jointly models the source, target, and mixed behavior sequences to highlight the global and target preference and precisely model the triple correlation in CDR.
Federated Learning via Variational Bayesian Inference: Personalization, Sparsity and Clustering
data, we propose a clustered Bayesian FL model named cFedbayes by learning different prior distributions for different clients.
Robust one-shot estimation over shared networks in the presence of denial-of-service attacks
We consider the following scenario: multiple pairs of agents communicating strategically over shared communication networks in the presence of a jammer who may launch a denial-of-service.
Online Decomposition of Surface Electromyogram into Individual Motor Unit Activities Using Progressive FastICA Peel-off
For decomposing experimental SEMG data, the proposed online method was able to extract an average of 12. 00 +- 3. 46 MUs per trial, with a matching rate of 90. 38% compared with results from the expert-guided offline decomposition.
Surveillance Face Anti-spoofing
In order to promote relevant research and fill this gap in the community, we collect a large-scale Surveillance High-Fidelity Mask (SuHiFiMask) dataset captured under 40 surveillance scenes, which has 101 subjects from different age groups with 232 3D attacks (high-fidelity masks), 200 2D attacks (posters, portraits, and screens), and 2 adversarial attacks.
Efficient Visual Computing with Camera RAW Snapshots
Conventional cameras capture image irradiance on a sensor and convert it to RGB images using an image signal processor (ISP).
Avoiding spurious correlations via logit correction
Empirical studies suggest that machine learning models trained with empirical risk minimization (ERM) often rely on attributes that may be spuriously correlated with the class labels.
An Empirical Study of Automatic Post-Editing
In view of the importance of data augmentation in APE, we separately study the impact of the construction method of artificial corpora and artificial data domain on the performance of APE models.
CLAMP: Prompt-based Contrastive Learning for Connecting Language and Animal Pose
Motivated by the progress of visual-language research, we propose that pre-trained language models (e. g., CLIP) can facilitate animal pose estimation by providing rich prior knowledge for describing animal keypoints in text.
Personalized Federated Learning via Variational Bayesian Inference
Federated learning faces huge challenges from model overfitting due to the lack of data and statistical diversity among clients.
Personalized Prompt for Sequential Recommendation
Specifically, we build the personalized soft prefix prompt via a prompt generator based on user profiles and enable a sufficient training of prompts via a prompt-oriented contrastive learning with both prompt- and behavior-based augmentations.
Experimental quantum adversarial learning with programmable superconducting qubits
Our results reveal experimentally a crucial vulnerability aspect of quantum learning systems under adversarial scenarios and demonstrate an effective defense strategy against adversarial attacks, which provide a valuable guide for quantum artificial intelligence applications with both near-term and future quantum devices.
Robust remote estimation over the collision channel in the presence of an intelligent jammer
We consider a sensor-receiver pair communicating over a wireless channel in the presence of a jammer who may launch a denial-of-service attack.
Multi-view Multi-behavior Contrastive Learning in Recommendation
We argue that MBR models should: (1) model the coarse-grained commonalities between different behaviors of a user, (2) consider both individual sequence view and global graph view in multi-behavior modeling, and (3) capture the fine-grained differences between multiple behaviors of a user.
A two-step backward compatible fullband speech enhancement system
Speech enhancement methods based on deep learning have surpassed traditional methods.
SMDT: Selective Memory-Augmented Neural Document Translation
Existing document-level neural machine translation (NMT) models have sufficiently explored different context settings to provide guidance for target generation.
Large-Scale Video Panoptic Segmentation in the Wild: A Benchmark
In contrast, our large-scale VIdeo Panoptic Segmentation in the Wild (VIPSeg) dataset provides 3, 536 videos and 84, 750 frames with pixel-level panoptic annotations, covering a wide range of real-world scenarios and categories.
Topological and Algebraic Structures of Atanassov's Intuitionistic Fuzzy-Values Space
By introducing a new operator for IFVs via the linear order based on a score function and an accuracy function, we show that such an operator is a strong negation on IFVs.
V2iFi: in-Vehicle Vital Sign Monitoring via Compact RF Sensing
Given the significant amount of time people spend in vehicles, health issues under driving condition have become a major concern.
Personalized Transfer of User Preferences for Cross-domain Recommendation
Specifically, a meta network fed with users' characteristic embeddings is learned to generate personalized bridge functions to achieve personalized transfer of preferences for each user.
A Causal U-net based Neural Beamforming Network for Real-Time Multi-Channel Speech Enhancement
In this work, a new causal U-net based multiple-in-multiple-out structure is proposed for real-time multi-channel speech enhancement.
Sparse Personalized Federated Learning
By incorporating an approximated L1-norm and the correlation between client models and global model into standard FL loss function, the performance on statistical diversity data is improved and the communicational and computational loads required in the network are reduced compared with non-sparse FL.
Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising
Besides, MetaHeac has been successfully deployed in WeChat for the promotion of both contents and advertisements, leading to great improvement in the quality of marketing.
Transfer-Meta Framework for Cross-domain Recommendation to Cold-Start Users
With the advantage of meta learning which has good generalization ability to novel tasks, we propose a transfer-meta framework for CDR (TMCDR) which has a transfer stage and a meta stage.
Learning to Warm Up Cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks
The cold item ID embedding has two main problems: (1) A gap is existing between the cold ID embedding and the deep model.
Optimal exit decision of venture capital under time-inconsistent preferences
Based on venture capitalists' understanding of future preferences, we consider four types of venture capitalists, namely time-consistent venture capitalists, venture capitalists who only realize critical time point inconsistency, naive venture capitalists and sophisticated venture capitalists, of which the latter three are time-inconsistent.
Enhancement of Superconductivity Linked with Linear-in-Temperature/Field Resistivity in Ion-Gated FeSe Films
Iron selenide (FeSe) - the structurally simplest iron-based superconductor, has attracted tremendous interest in the past years.
Superconductivity
Understanding WeChat User Preferences and "Wow" Diffusion
"Top Stories" is a novel friend-enhanced recommendation engine in WeChat, in which users can read articles based on preferences of both their own and their friends.
Graph Representation Learning
Social and Information Networks
UPRec: User-Aware Pre-training for Recommender Systems
Existing sequential recommendation methods rely on large amounts of training data and usually suffer from the data sparsity problem.
FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine Translation
Previous research for adapting a general neural machine translation (NMT) model into a specific domain usually neglects the diversity in translation within the same domain, which is a core problem for domain adaptation in real-world scenarios.
Generalized Relation Learning with Semantic Correlation Awareness for Link Prediction
The current methods for the link prediction taskhavetwonaturalproblems:1)the relation distributions in KGs are usually unbalanced, and 2) there are many unseen relations that occur in practical situations.
Intra-Correlation Encoding for Chinese Sentence Intention Matching
Sentence intention matching is vital for natural language understanding.
Learning to Build User-tag Profile in Recommendation System (UTPM)
User profiling is one of the most important components in recommendation systems, where a user is profiled using demographic (e. g. gender, age, and location) and user behavior information (e. g. browsing and search history).
MLBF-Net: A Multi-Lead-Branch Fusion Network for Multi-Class Arrhythmia Classification Using 12-Lead ECG
MLBF-Net is composed of three components: 1) multiple lead-specific branches for learning the diversity of multi-lead ECG; 2) cross-lead features fusion by concatenating the output feature maps of all branches for learning the integrity of multi-lead ECG; 3) multi-loss co-optimization for all the individual branches and the concatenated network.
Deep Learning Guided Building Reconstruction from Satellite Imagery-derived Point Clouds
For points that belong to the same roof shape, a multi-cue, hierarchical RANSAC approach is proposed for efficient and reliable segmenting and reconstructing the building point cloud.
Learning Contextualized Sentence Representations for Document-Level Neural Machine Translation
In this paper, we propose a new framework to model cross-sentence dependencies by training neural machine translation (NMT) to predict both the target translation and surrounding sentences of a source sentence.
Unifying Specialist Image Embedding into Universal Image Embedding
Therefore, we propose to distill the knowledge in multiple specialists into a universal embedding to solve this problem.
Detecting and Simulating Artifacts in GAN Fake Images
By using the simulated images to train a spectrum based classifier, even without seeing the fake images produced by the targeted GAN model during training, our approach achieves state-of-the-art performances on detecting fake images generated by popular GAN models such as CycleGAN.
Real-time Attention Based Look-alike Model for Recommender System
Recently, deep learning models play more and more important roles in contents recommender systems.
Super Interaction Neural Network
Recent studies have demonstrated that the convolutional networks heavily rely on the quality and quantity of generated features.
Label Mapping Neural Networks with Response Consolidation for Class Incremental Learning
In this paper, it is the first time to discuss the difficulty without support of old classes in class incremental learning, which is called as softmax suppression problem.
Unsupervised Embedding Learning via Invariant and Spreading Instance Feature
This paper studies the unsupervised embedding learning problem, which requires an effective similarity measurement between samples in low-dimensional embedding space.
Exact Controllability for a Refined Stochastic Wave Equation
By means of a new global Carleman estimate, we establish the exact controllability of our stochastic wave equation with three controls.
Optimization and Control 93B05, 60H15, 93B07, 35B45
FARSA: Fully Automated Roadway Safety Assessment
An emerging approach for conducting such assessments in the United States is through the US Road Assessment Program (usRAP), which rates roads from highest risk (1 star) to lowest (5 stars).
Assessment of central serous chorioretinopathy (CSC) depicted on color fundus photographs using deep Learning
To investigate whether and to what extent central serous chorioretinopathy (CSC) depicted on color fundus photographs can be assessed using deep learning technology.
Optimal Feedback for Stochastic Linear Quadratic Control and Backward Stochastic Riccati Equations in Infinite Dimensions
It is a longstanding unsolved problem to characterize the optimal feedbacks for general SLQs (i. e., stochastic linear quadratic control problems) with random coefficients in infinite dimensions; while the same problem but in finite dimensions was just addressed in a recent work [36].
Optimization and Control Probability 60H15, 93E20, 60H25, 49J30
Second Order Optimality Conditions for Optimal Control Problems of Stochastic Evolution Equations
In this paper, we establish some second order necessary/sufficient optimality conditions for optimal control problems of stochastic evolution equations in infinite dimensions.
Optimization and Control Primary 93E20, Secondary, 60H07, 60H15
SCMA based resource management of D2D communications for maximum sum-revenue
This problem is NP-hard, so we propose a heuristic algorithm based on semi-definite relaxation (SDR) programming to solve it.
Signal Processing
Heated-Up Softmax Embedding
Metric learning aims at learning a distance which is consistent with the semantic meaning of the samples.
Tropical Principal Component Analysis and its Application to Phylogenetics
Principal component analysis is a widely-used method for the dimensionality reduction of a given data set in a high-dimensional Euclidean space.
Combinatorics Populations and Evolution
Learning Spread-out Local Feature Descriptors
We propose a simple, yet powerful regularization technique that can be used to significantly improve both the pairwise and triplet losses in learning local feature descriptors.
Learning discriminative and transformation covariant local feature detectors.
Specifically, we extend the covariant constraint proposed by Lenc and Vedaldi [8] by defining the concepts of “standard patch” and “canonical feature” and leverage these to train a novel robust covariant detector.
Learning Discriminative and Transformation Covariant Local Feature Detectors
Specifically, we extend the covariant constraint proposed by Lenc and Vedaldi by defining the concepts of "standard patch" and "canonical feature" and leverage these to train a novel robust covariant detector.
Spatio-temporal Aware Non-negative Component Representation for Action Recognition
This paper presents a novel mid-level representation for action recognition, named spatio-temporal aware non-negative component representation (STANNCR).
Fast Orthogonal Projection Based on Kronecker Product
We propose a family of structured matrices to speed up orthogonal projections for high-dimensional data commonly seen in computer vision applications.
Deep Transfer Network: Unsupervised Domain Adaptation
In this paper, we propose a new domain adaptation framework named Deep Transfer Network (DTN), where the highly flexible deep neural networks are used to implement such a distribution matching process.
Efficient classification using parallel and scalable compressed model and Its application on intrusion detection
In order to achieve high efficiency of classification in intrusion detection, a compressed model is proposed in this paper which combines horizontal compression with vertical compression.
