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Found 124 papers, 48 papers with code
一种非结构化数据表征增强的术后风险预测模型(An Unstructured Data Representation Enhanced Model for Postoperative Risk Prediction)
“准确的术后风险预测对临床资源规划和应急方案准备以及降低患者的术后风险和死亡率具有积极作用。术后风险预测目前主要基于术前和术中的患者基本信息、实验室检查、生命体征等结构化数据, 而蕴含丰富语义信息的非结构化术前诊断的价值还有待验证。针对该问题, 本文提出一种非结构化数据表征增强的术后风险预测模型, 利用自注意力机制, 精巧的将结构化数据与术前诊断数据进行信息加权融合。基于临床数据, 将本文方法与术后风险预测常用的统计机器学习模型以及最新的深度神经网络进行对比, 本文方法不仅提升了术后风险预测的性能, 同时也为预测模型带来了良好的可解释性。”
How to Enable LLM with 3D Capacity? A Survey of Spatial Reasoning in LLM
3D spatial understanding is essential in real-world applications such as robotics, autonomous vehicles, virtual reality, and medical imaging.
Intelligent Resource Allocation Optimization for Cloud Computing via Machine Learning
With the rapid expansion of cloud computing applications, optimizing resource allocation has become crucial for improving system performance and cost efficiency.
Cloud Computing Energy Consumption Prediction Based on Kernel Extreme Learning Machine Algorithm Improved by Vector Weighted Average Algorithm
With the rapid expansion of cloud computing infrastructure, energy consumption has become a critical challenge, driving the need for accurate and efficient prediction models.
Research on Edge Computing and Cloud Collaborative Resource Scheduling Optimization Based on Deep Reinforcement Learning
This study addresses the challenge of resource scheduling optimization in edge-cloud collaborative computing using deep reinforcement learning (DRL).
Design and implementation of a distributed security threat detection system integrating federated learning and multimodal LLM
Traditional security protection methods struggle to address sophisticated attack vectors in large-scale distributed systems, particularly when balancing detection accuracy with data privacy concerns.
Research on Enhancing Cloud Computing Network Security using Artificial Intelligence Algorithms
Cloud computing environments are increasingly vulnerable to security threats such as distributed denial-of-service (DDoS) attacks and SQL injection.
Machine Learning-Based Cloud Computing Compliance Process Automation
Cloud computing adoption across industries has revolutionized enterprise operations while introducing significant challenges in compliance management.
Flat U-Net: An Efficient Ultralightweight Model for Solar Filament Segmentation in Full-disk H$α$ Images
Solar filaments are one of the most prominent features observed on the Sun, and their evolutions are closely related to various solar activities, such as flares and coronal mass ejections.
Effective Black-Box Multi-Faceted Attacks Breach Vision Large Language Model Guardrails
It comprises three complementary attack facets: Visual Attack that exploits the multimodal nature of VLLMs to inject toxic system prompts through images; Alignment Breaking Attack that manipulates the model's alignment mechanism to prioritize the generation of contrasting responses; and Adversarial Signature that deceives content moderators by strategically placing misleading information at the end of the response.
STAIR: Improving Safety Alignment with Introspective Reasoning
Ensuring the safety and harmlessness of Large Language Models (LLMs) has become equally critical as their performance in applications.
AdaF^2M^2: Comprehensive Learning and Responsive Leveraging Features in Recommendation System
To achieve better feature representation learning and leveraging on real-world data, we propose a model-agnostic framework AdaF^2M^2, short for Adaptive Feature Modeling with Feature Mask.
Long-Term Interest Clock: Fine-Grained Time Perception in Streaming Recommendation System
Recently, Interest Clock has achieved remarkable performance in streaming recommendation systems.
Bringing Characters to New Stories: Training-Free Theme-Specific Image Generation via Dynamic Visual Prompting
The stories and characters that captivate us as we grow up shape unique fantasy worlds, with images serving as the primary medium for visually experiencing these realms.
Efficient Auto-Labeling of Large-Scale Poultry Datasets (ALPD) Using Semi-Supervised Models, Active Learning, and Prompt-then-Detect Approach
The rapid growth of AI in poultry farming has highlighted the challenge of efficiently labeling large, diverse datasets.
A Generalizable Anomaly Detection Method in Dynamic Graphs
Anomaly detection aims to identify deviations from normal patterns within data.
BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning
This reformulation enables the use of first-order optimization algorithms in calculating the backward pass gradients, allowing our framework to potentially utilize any state-of-the-art solver.
IKUN: Initialization to Keep snn training and generalization great with sUrrogate-stable variaNce
Weight initialization significantly impacts the convergence and performance of neural networks.
Video-RAG: Visually-aligned Retrieval-Augmented Long Video Comprehension
Existing large video-language models (LVLMs) struggle to comprehend long videos correctly due to limited context.
ARN-LSTM: A Multi-Stream Fusion Model for Skeleton-based Action Recognition
This paper presents the ARN-LSTM architecture, a novel multi-stream action recognition model designed to address the challenge of simultaneously capturing spatial motion and temporal dynamics in action sequences.
Efficient Mixture-of-Expert for Video-based Driver State and Physiological Multi-task Estimation in Conditional Autonomous Driving
In this study, we propose a novel multi-task DMS, termed VDMoE, which leverages RGB video input to monitor driver states non-invasively.
From Pixels to Tokens: Revisiting Object Hallucinations in Large Vision-Language Models
Hallucinations in large vision-language models (LVLMs) are a significant challenge, i. e., generating objects that are not presented in the visual input, which impairs their reliability.
Defense-as-a-Service: Black-box Shielding against Backdoored Graph Models
With the trend of large graph learning models, business owners tend to employ a model provided by a third party to deliver business services to users.
Collaborative Evolving Strategy for Automatic Data-Centric Development
By leveraging the strong complex problem-solving capabilities of large language models (LLMs), we propose an LLM-based autonomous agent, equipped with a strategy named Collaborative Knowledge-STudying-Enhanced Evolution by Retrieval (Co-STEER), to simultaneously address all the challenges.
Are Large Language Models a Good Replacement of Taxonomies?
Unfortunately, there lacks a comprehensive benchmark that evaluates the LLMs over a wide range of taxonomies from common to specialized domains and at levels from root to leaf so that we can draw a confident conclusion.
Graph Neural Backdoor: Fundamentals, Methodologies, Applications, and Future Directions
Graph Neural Networks (GNNs) have significantly advanced various downstream graph-relevant tasks, encompassing recommender systems, molecular structure prediction, social media analysis, etc.
MultiTrust: A Comprehensive Benchmark Towards Trustworthy Multimodal Large Language Models
Despite the superior capabilities of Multimodal Large Language Models (MLLMs) across diverse tasks, they still face significant trustworthiness challenges.
CRAG -- Comprehensive RAG Benchmark
To bridge this gap, we introduce the Comprehensive RAG Benchmark (CRAG), a factual question answering benchmark of 4, 409 question-answer pairs and mock APIs to simulate web and Knowledge Graph (KG) search.
AutoBreach: Universal and Adaptive Jailbreaking with Efficient Wordplay-Guided Optimization
Despite the widespread application of large language models (LLMs) across various tasks, recent studies indicate that they are susceptible to jailbreak attacks, which can render their defense mechanisms ineffective.
Efficient Black-box Adversarial Attacks via Bayesian Optimization Guided by a Function Prior
In this paper, we propose a Prior-guided Bayesian Optimization (P-BO) algorithm that leverages the surrogate model as a global function prior in black-box adversarial attacks.
The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition
In the realm of autonomous driving, robust perception under out-of-distribution conditions is paramount for the safe deployment of vehicles.
PhysMLE: Generalizable and Priors-Inclusive Multi-task Remote Physiological Measurement
Additionally, we introduced prior knowledge from physiology among tasks to overcome the imbalance of label space under real-world multi-task physiological measurement.
Towards Data-Centric Automatic R&D
We appeal to future work to take developing techniques for tackling automatic R&D into consideration, thus bringing the opportunities of the potential revolutionary upgrade to human productivity.
FaceCat: Enhancing Face Recognition Security with a Unified Diffusion Model
Face anti-spoofing (FAS) and adversarial detection (FAD) have been regarded as critical technologies to ensure the safety of face recognition systems.
MoMA: Multimodal LLM Adapter for Fast Personalized Image Generation
This approach effectively synergizes reference image and text prompt information to produce valuable image features, facilitating an image diffusion model.
Embodied Active Defense: Leveraging Recurrent Feedback to Counter Adversarial Patches
The vulnerability of deep neural networks to adversarial patches has motivated numerous defense strategies for boosting model robustness.
AnimateDiff-Lightning: Cross-Model Diffusion Distillation
We present AnimateDiff-Lightning for lightning-fast video generation.
GuardT2I: Defending Text-to-Image Models from Adversarial Prompts
Recent advancements in Text-to-Image (T2I) models have raised significant safety concerns about their potential misuse for generating inappropriate or Not-Safe-For-Work (NSFW) contents, despite existing countermeasures such as NSFW classifiers or model fine-tuning for inappropriate concept removal.
BSPA: Exploring Black-box Stealthy Prompt Attacks against Image Generators
It allows users to design specific prompts to generate realistic images through some black-box APIs.
SDXL-Lightning: Progressive Adversarial Diffusion Distillation
We propose a diffusion distillation method that achieves new state-of-the-art in one-step/few-step 1024px text-to-image generation based on SDXL.
Diffusion Models are Certifiably Robust Classifiers
Experimental results show the superior certified robustness of these Noised Diffusion Classifiers (NDCs).
On the Promises and Challenges of Multimodal Foundation Models for Geographical, Environmental, Agricultural, and Urban Planning Applications
The advent of large language models (LLMs) has heightened interest in their potential for multimodal applications that integrate language and vision.
Towards Transferable Targeted 3D Adversarial Attack in the Physical World
However, the field of transferable targeted 3D adversarial attacks remains vacant.
YOLO-OB: An improved anchor-free real-time multiscale colon polyp detector in colonoscopy
These problems reduce the performance of polyp detection and also lower the model's training and detection efficiency.
Evil Geniuses: Delving into the Safety of LLM-based Agents
Rapid advancements in large language models (LLMs) have revitalized in LLM-based agents, exhibiting impressive human-like behaviors and cooperative capabilities in various scenarios.
MagicPose: Realistic Human Poses and Facial Expressions Retargeting with Identity-aware Diffusion
In this work, we propose MagicPose, a diffusion-based model for 2D human pose and facial expression retargeting.
Leveraging Large Language Model for Automatic Evolving of Industrial Data-Centric R&D Cycle
In the wake of relentless digital transformation, data-driven solutions are emerging as powerful tools to address multifarious industrial tasks such as forecasting, anomaly detection, planning, and even complex decision-making.
How Robust is Google's Bard to Adversarial Image Attacks?
By attacking white-box surrogate vision encoders or MLLMs, the generated adversarial examples can mislead Bard to output wrong image descriptions with a 22% success rate based solely on the transferability.
MVDream: Multi-view Diffusion for 3D Generation
We introduce MVDream, a diffusion model that is able to generate consistent multi-view images from a given text prompt.
Root Pose Decomposition Towards Generic Non-rigid 3D Reconstruction with Monocular Videos
The key idea of our method, Root Pose Decomposition (RPD), is to maintain a per-frame root pose transformation, meanwhile building a dense field with local transformations to rectify the root pose.
AdvFAS: A robust face anti-spoofing framework against adversarial examples
Ensuring the reliability of face recognition systems against presentation attacks necessitates the deployment of face anti-spoofing techniques.
Knowledge-Driven Multi-Agent Reinforcement Learning for Computation Offloading in Cybertwin-Enabled Internet of Vehicles
By offloading computation-intensive tasks of vehicles to roadside units (RSUs), mobile edge computing (MEC) in the Internet of Vehicles (IoV) can relieve the onboard computation burden.
Unleashing the Power of Self-Supervised Image Denoising: A Comprehensive Review
The advent of deep learning has brought a revolutionary transformation to image denoising techniques.
LogKG: Log Failure Diagnosis Through Knowledge Graph
To fully use the information represented by KG, we propose a failure-oriented log representation (FOLR) method to extract the failure-related patterns.
Improving Opinion-based Question Answering Systems Through Label Error Detection and Overwrite
Importantly, LEDO is computationally efficient compared to methods that require loss function change, and cost-effective as the resulting data can be used in the same continuous training pipeline for production.
On Evaluating Adversarial Robustness of Large Vision-Language Models
Large vision-language models (VLMs) such as GPT-4 have achieved unprecedented performance in response generation, especially with visual inputs, enabling more creative and adaptable interaction than large language models such as ChatGPT.
Unifying gradient regularization for Heterogeneous Graph Neural Networks
Grug provides a unified framework integrating graph topology and node features, based on which we conduct a detailed theoretical analysis of their effectiveness.
Robust Classification via a Single Diffusion Model
As RDC does not require training on particular adversarial attacks, we demonstrate that it is more generalizable to defend against multiple unseen threats.
Ranked #2 on
Adversarial Defense
on CIFAR-10
SAM for Poultry Science
This study aims to assess the zero-shot segmentation performance of SAM on representative chicken segmentation tasks, including part-based segmentation and the use of infrared thermal images, and to explore chicken-tracking tasks by using SAM as a segmentation tool.
Common Diffusion Noise Schedules and Sample Steps are Flawed
We discover that common diffusion noise schedules do not enforce the last timestep to have zero signal-to-noise ratio (SNR), and some implementations of diffusion samplers do not start from the last timestep.
Towards Effective Adversarial Textured 3D Meshes on Physical Face Recognition
The goal of this work is to develop a more reliable technique that can carry out an end-to-end evaluation of adversarial robustness for commercial systems.
PAniC-3D: Stylized Single-view 3D Reconstruction from Portraits of Anime Characters
We propose PAniC-3D, a system to reconstruct stylized 3D character heads directly from illustrated (p)ortraits of (ani)me (c)haracters.
Benchmarking Robustness of 3D Object Detection to Common Corruptions in Autonomous Driving
3D object detection is an important task in autonomous driving to perceive the surroundings.
A Recipe for Watermarking Diffusion Models
Diffusion models (DMs) have demonstrated advantageous potential on generative tasks.
Rethinking Model Ensemble in Transfer-based Adversarial Attacks
It is widely recognized that deep learning models lack robustness to adversarial examples.
A Comprehensive Study on Robustness of Image Classification Models: Benchmarking and Rethinking
In our benchmark, we evaluate the robustness of 55 typical deep learning models on ImageNet with diverse architectures (e. g., CNNs, Transformers) and learning algorithms (e. g., normal supervised training, pre-training, adversarial training) under numerous adversarial attacks and out-of-distribution (OOD) datasets.
Detecting and measuring human gastric peristalsis using magnetically controlled capsule endoscope
In this paper, we develop algorithms to detect and measure human gastric peristalsis (contraction wave) using video sequences acquired by MCCE.
Benchmarking Robustness of 3D Object Detection to Common Corruptions
3D object detection is an important task in autonomous driving to perceive the surroundings.
Shifted Diffusion for Text-to-image Generation
Unlike the baseline diffusion model used in DALL-E 2, our method seamlessly encodes prior knowledge of the pre-trained CLIP model in its diffusion process by designing a new initialization distribution and a new transition step of the diffusion.
Ranked #3 on
Text-to-Image Generation
on Multi-Modal-CelebA-HQ
RGBD1K: A Large-scale Dataset and Benchmark for RGB-D Object Tracking
To demonstrate the benefits of training on a larger RGB-D data set in general, and RGBD1K in particular, we develop a transformer-based RGB-D tracker, named SPT, as a baseline for future visual object tracking studies using the new dataset.
A Challenging Benchmark of Anime Style Recognition
Given two images of different anime roles, anime style recognition (ASR) aims to learn abstract painting style to determine whether the two images are from the same work, which is an interesting but challenging problem.
Controllable Evaluation and Generation of Physical Adversarial Patch on Face Recognition
It is therefore imperative to develop a framework that can enable a comprehensive evaluation of the vulnerability of face recognition in the physical world.
Robustness and Accuracy Could Be Reconcilable by (Proper) Definition
The trade-off between robustness and accuracy has been widely studied in the adversarial literature.
DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR
We present in this paper a novel query formulation using dynamic anchor boxes for DETR (DEtection TRansformer) and offer a deeper understanding of the role of queries in DETR.
Ranked #101 on
Object Detection
on COCO minival
DDG-DA: Data Distribution Generation for Predictable Concept Drift Adaptation
To handle concept drift, previous methods first detect when/where the concept drift happens and then adapt models to fit the distribution of the latest data.
SemanticStyleGAN: Learning Compositional Generative Priors for Controllable Image Synthesis and Editing
When combined with editing methods designed for StyleGANs, it can achieve a more fine-grained control to edit synthesized or real images.
An Improved Reinforcement Learning Model Based on Sentiment Analysis
The authors combined the deep Q network in reinforcement learning with the sentiment quantitative indicator ARBR to build a high-frequency stock trading model for the share market.
Unrestricted Adversarial Attacks on ImageNet Competition
Many works have investigated the adversarial attacks or defenses under the settings where a bounded and imperceptible perturbation can be added to the input.
Adversarial Attacks on ML Defense Models Competition
Due to the vulnerability of deep neural networks (DNNs) to adversarial examples, a large number of defense techniques have been proposed to alleviate this problem in recent years.
Model-Agnostic Meta-Attack: Towards Reliable Evaluation of Adversarial Robustness
The vulnerability of deep neural networks to adversarial examples has motivated an increasing number of defense strategies for promoting model robustness.
Adversarial Semantic Contour for Object Detection
To address this issue, we propose a novel method of Adversarial Semantic Contour (ASC) guided by object contour as prior.
PIVQGAN: Posture and Identity Disentangled Image-to-Image Translation via Vector Quantization
The VQSN module facilitates a more delicate separation of posture and identity, while the training scheme ensures the VQSN module learns the pose-related representations.
Using Pause Information for More Accurate Entity Recognition
Entity tags in human-machine dialog are integral to natural language understanding (NLU) tasks in conversational assistants.
Query2Label: A Simple Transformer Way to Multi-Label Classification
The use of Transformer is rooted in the need of extracting local discriminative features adaptively for different labels, which is a strongly desired property due to the existence of multiple objects in one image.
Ranked #1 on
Multi-Label Classification
on PASCAL VOC 2012
Boosting Transferability of Targeted Adversarial Examples via Hierarchical Generative Networks
Transfer-based adversarial attacks can evaluate model robustness in the black-box setting.
Semantic StyleGAN
SemanticStyleGAN presents a method where a generator is trained to model local semantic parts separately and synthesizes images in a compositional way.
Accumulative Poisoning Attacks on Real-time Data
Collecting training data from untrusted sources exposes machine learning services to poisoning adversaries, who maliciously manipulate training data to degrade the model accuracy.
Exploring Memorization in Adversarial Training
In this paper, we explore the memorization effect in adversarial training (AT) for promoting a deeper understanding of model capacity, convergence, generalization, and especially robust overfitting of the adversarially trained models.
Unsupervised Part Segmentation through Disentangling Appearance and Shape
We study the problem of unsupervised discovery and segmentation of object parts, which, as an intermediate local representation, are capable of finding intrinsic object structure and providing more explainable recognition results.
Adversarial Open Domain Adaptation for Sketch-to-Photo Synthesis
In this paper, we explore open-domain sketch-to-photo translation, which aims to synthesize a realistic photo from a freehand sketch with its class label, even if the sketches of that class are missing in the training data.
Ranked #1 on
Sketch-to-Image Translation
on Scribble
PointShuffleNet: Learning Non-Euclidean Features with Homotopy Equivalence and Mutual Information
In this paper, we introduce the homotopy equivalence relation (HER) to make the neural networks learn the data distribution from a high-dimension manifold.
LiBRe: A Practical Bayesian Approach to Adversarial Detection
Despite their appealing flexibility, deep neural networks (DNNs) are vulnerable against adversarial examples.
Black-box Detection of Backdoor Attacks with Limited Information and Data
Although deep neural networks (DNNs) have made rapid progress in recent years, they are vulnerable in adversarial environments.
Joint Network Topology Inference via Structured Fusion Regularization
Theoretically, we provide a theoretical analysis of the proposed graph estimator, which establishes a non-asymptotic bound of the estimation error under the high-dimensional setting and reflects the effect of several key factors on the convergence rate of our algorithm.
Incentive Mechanism Design for ROI-constrained Auto-bidding
Auto-bidding plays an important role in online advertising and has become a crucial tool for advertisers and advertising platforms to meet their performance objectives and optimize the efficiency of ad delivery.
Computer Science and Game Theory
Bag of Tricks for Adversarial Training
Adversarial training (AT) is one of the most effective strategies for promoting model robustness.
BayesAdapter: Being Bayesian, Inexpensively and Robustly, via Bayesian Fine-tuning
Despite their theoretical appealingness, Bayesian neural networks (BNNs) are falling far behind in terms of adoption in real-world applications compared with normal NNs, mainly due to their limited scalability in training, and low fidelity in their uncertainty estimates.
Qlib: An AI-oriented Quantitative Investment Platform
Quantitative investment aims to maximize the return and minimize the risk in a sequential trading period over a set of financial instruments.
RobFR: Benchmarking Adversarial Robustness on Face Recognition
Based on large-scale evaluations, the commercial FR API services fail to exhibit acceptable performance on robustness evaluation, and we also draw several important conclusions for understanding the adversarial robustness of FR models and providing insights for the design of robust FR models.
Noise Robust Named Entity Understanding for Voice Assistants
Named Entity Recognition (NER) and Entity Linking (EL) play an essential role in voice assistant interaction, but are challenging due to the special difficulties associated with spoken user queries.
Towards Face Encryption by Generating Adversarial Identity Masks
As billions of personal data being shared through social media and network, the data privacy and security have drawn an increasing attention.
Boosting Adversarial Training with Hypersphere Embedding
Adversarial training (AT) is one of the most effective defenses against adversarial attacks for deep learning models.
Benchmarking Adversarial Robustness
Deep neural networks are vulnerable to adversarial examples, which becomes one of the most important research problems in the development of deep learning.
Design and Interpretation of Universal Adversarial Patches in Face Detection
We consider universal adversarial patches for faces -- small visual elements whose addition to a face image reliably destroys the performance of face detectors.
Leveraging User Engagement Signals For Entity Labeling in a Virtual Assistant
Personal assistant AI systems such as Siri, Cortana, and Alexa have become widely used as a means to accomplish tasks through natural language commands.
AiAds: Automated and Intelligent Advertising System for Sponsored Search
Sponsored search has more than 20 years of history, and it has been proven to be a successful business model for online advertising.
Face Anti-Spoofing: Model Matters, so Does Data
Face anti-spoofing is an important task in full-stack face applications including face detection, verification, and recognition.
TextContourNet: a Flexible and Effective Framework for Improving Scene Text Detection Architecture with a Multi-task Cascade
We propose a novel and effective framework for this and experimentally demonstrate that: (1) A CNN that can be effectively used to extract instance-level text contour from natural images.
Distractor Generation for Multiple Choice Questions Using Learning to Rank
We investigate how machine learning models, specifically ranking models, can be used to select useful distractors for multiple choice questions.
Adversarial Training for Community Question Answer Selection Based on Multi-scale Matching
We frame this task as a binary (relevant/irrelevant) classification problem, and present an adversarial training framework to alleviate label imbalance issue.
Text Extraction and Retrieval from Smartphone Screenshots: Building a Repository for Life in Media
Effective and efficient Information Extraction and Retrieval from digital screenshots is a crucial prerequisite to successful use of screen data.
Fast Predictive Simple Geodesic Regression
Deformable image registration and regression are important tasks in medical image analysis.
Learning non-parametric Markov networks with mutual information
We propose a method for learning Markov network structures for continuous data without invoking any assumptions about the distribution of the variables.
Prolongation of SMAP to Spatio-temporally Seamless Coverage of Continental US Using a Deep Learning Neural Network
The Soil Moisture Active Passive (SMAP) mission has delivered valuable sensing of surface soil moisture since 2015.
Learning to Extract Semantic Structure From Documents Using Multimodal Fully Convolutional Neural Networks
We present an end-to-end, multimodal, fully convolutional network for extracting semantic structures from document images.
Multi-Scale FCN With Cascaded Instance Aware Segmentation for Arbitrary Oriented Word Spotting in the Wild
Scene text detection has attracted great attention these years.
Learning to Extract Semantic Structure from Documents Using Multimodal Fully Convolutional Neural Network
We present an end-to-end, multimodal, fully convolutional network for extracting semantic structures from document images.
Quicksilver: Fast Predictive Image Registration - a Deep Learning Approach
A deep encoder-decoder network is used as the prediction model.
Fast Predictive Multimodal Image Registration
We introduce a deep encoder-decoder architecture for image deformation prediction from multimodal images.
Efficient Registration of Pathological Images: A Joint PCA/Image-Reconstruction Approach
Registration involving one or more images containing pathologies is challenging, as standard image similarity measures and spatial transforms cannot account for common changes due to pathologies.
Smart Library: Identifying Books in a Library using Richly Supervised Deep Scene Text Reading
Physical library collections are valuable and long standing resources for knowledge and learning.
Fast Predictive Image Registration
We present a method to predict image deformations based on patch-wise image appearance.
Using Word Embeddings in Twitter Election Classification
In this paper, using a Twitter election classification task that aims to detect election-related tweets, we investigate the impact of the background dataset used to train the embedding models, the context window size and the dimensionality of word embeddings on the classification performance.
