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Found 69 papers, 10 papers with code
IMSurReal Too: IMS in the Surface Realization Shared Task 2020
We introduce the IMS contribution to the Surface Realization Shared Task 2020.
Identifying and Handling Cross-Treebank Inconsistencies in UD: A Pilot Study
The Universal Dependencies treebanks are a still-growing collection of treebanks for a wide range of languages, all annotated with a common inventory of dependency relations.
Optimal portfolio under ratio-type periodic evaluation in incomplete markets with stochastic factors
With the help of the duality results in the auxiliary problems and some fixed point arguments, we further derive and verify the optimal portfolio processes in a periodic manner for the original periodic evaluation problems over an infinite horizon.
Application of 2D Homography for High Resolution Traffic Data Collection using CCTV Cameras
This study implements a three-stage video analytics framework for extracting high-resolution traffic data such vehicle counts, speed, and acceleration from infrastructure-mounted CCTV cameras.
3D Object Detection and High-Resolution Traffic Parameters Extraction Using Low-Resolution LiDAR Data
These are the need for multiple LiDAR systems to obtain complete point cloud information of objects of interest, as well as the labor-intensive process of annotating 3D bounding boxes for object detection tasks.
OpEnCam: Lensless Optical Encryption Camera
OpEnCam encrypts the incoming light before capturing it using the modulating ability of optical masks.
On optimal tracking portfolio in incomplete markets: The classical control and the reinforcement learning approaches
This paper studies an infinite horizon optimal tracking portfolio problem using capital injection in incomplete market models.
Optimal Portfolio with Ratio Type Periodic Evaluation under Short-Selling Prohibition
With the help of the results from the auxiliary problem, the value function and the optimal constrained portfolio for the original problem with periodic evaluation can be derived and verified, allowing us to discuss some financial implications under the new performance paradigm.
Augment the Pairs: Semantics-Preserving Image-Caption Pair Augmentation for Grounding-Based Vision and Language Models
While we demonstrate our data augmentation method with MDETR framework, the proposed approach is applicable to common grounding-based vision and language tasks with other frameworks.
Domain Generalization Guided by Gradient Signal to Noise Ratio of Parameters
Overfitting to the source domain is a common issue in gradient-based training of deep neural networks.
DeFormer: Integrating Transformers with Deformable Models for 3D Shape Abstraction from a Single Image
Accurate 3D shape abstraction from a single 2D image is a long-standing problem in computer vision and graphics.
Trustworthy Representation Learning Across Domains
Inspired by the concepts in trustworthy AI, we proposed the first trustworthy representation learning across domains framework which includes four concepts, i. e, robustness, privacy, fairness, and explainability, to give a comprehensive literature review on this research direction.
Fault Separation Based on An Excitation Operator with Application to a Quadrotor UAV
This paper presents an excitation operator based fault separation architecture for a quadrotor unmanned aerial vehicle (UAV) subject to loss of effectiveness (LoE) faults, actuator aging, and load uncertainty.
Composite Disturbance Filtering: A Novel State Estimation Scheme for Systems With Multi-Source, Heterogeneous, and Isomeric Disturbances
State estimation has long been a fundamental problem in signal processing and control areas.
Continuous-time q-learning for mean-field control problems
This paper studies the q-learning, recently coined as the continuous time counterpart of Q-learning by Jia and Zhou (2023), for continuous time Mckean-Vlasov control problems in the setting of entropy-regularized reinforcement learning.
Q: How to Specialize Large Vision-Language Models to Data-Scarce VQA Tasks? A: Self-Train on Unlabeled Images!
We introduce SelTDA (Self-Taught Data Augmentation), a strategy for finetuning large VLMs on small-scale VQA datasets.
Selective Structured State-Spaces for Long-Form Video Understanding
To address this limitation, we present a novel Selective S4 (i. e., S5) model that employs a lightweight mask generator to adaptively select informative image tokens resulting in more efficient and accurate modeling of long-term spatiotemporal dependencies in videos.
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On time-consistent equilibrium stopping under aggregation of diverse discount rates
When the sufficient condition of the attitude function is violated, we can illustrate by various examples that the characterization of the optimal equilibrium may differ significantly from some existing results for an individual agent.
Learning Phase Mask for Privacy-Preserving Passive Depth Estimation
With over a billion sold each year, cameras are not only becoming ubiquitous and omnipresent, but are driving progress in a wide range of applications such as augmented/virtual reality, robotics, surveillance, security, autonomous navigation and many others.
EMC2A-Net: An Efficient Multibranch Cross-channel Attention Network for SAR Target Classification
In recent years, convolutional neural networks (CNNs) have shown great potential in synthetic aperture radar (SAR) target recognition.
A mean field game approach to equilibrium consumption under external habit formation
This paper studies the equilibrium consumption under external habit formation in a large population of agents.
Controllable Dynamic Multi-Task Architectures
In contrast to the existing dynamic multi-task approaches that adjust only the weights within a fixed architecture, our approach affords the flexibility to dynamically control the total computational cost and match the user-preferred task importance better.
Single-Stream Multi-Level Alignment for Vision-Language Pretraining
Self-supervised vision-language pretraining from pure images and text with a contrastive loss is effective, but ignores fine-grained alignment due to a dual-stream architecture that aligns image and text representations only on a global level.
On Generalizing Beyond Domains in Cross-Domain Continual Learning
Humans have the ability to accumulate knowledge of new tasks in varying conditions, but deep neural networks often suffer from catastrophic forgetting of previously learned knowledge after learning a new task.
Learning to Learn across Diverse Data Biases in Deep Face Recognition
Convolutional Neural Networks have achieved remarkable success in face recognition, in part due to the abundant availability of data.
Learning Cross-modal Contrastive Features for Video Domain Adaptation
Learning transferable and domain adaptive feature representations from videos is important for video-relevant tasks such as action recognition.
Mean Field Game of Optimal Relative Investment with Jump Risk
This paper studies the n-player game and the mean field game under the CRRA relative performance on terminal wealth, in which the interaction occurs by peer competition.
Cross-Domain Similarity Learning for Face Recognition in Unseen Domains
Intuitively, it discriminatively correlates explicit metrics derived from one domain, with triplet samples from another domain in a unified loss function to be minimized within a network, which leads to better alignment of the training domains.
Adversarial Attacks and Defenses in Physiological Computing: A Systematic Review
Physiological computing uses human physiological data as system inputs in real time.
Synthetic Generation of Three-Dimensional Cancer Cell Models from Histopathological Images
Synthetic generation of three-dimensional cell models from histopathological images aims to enhance understanding of cell mutation, and progression of cancer, necessary for clinical assessment and optimal treatment.
Real-Valued Logics for Typological Universals: Framework and Application
This paper proposes a framework for the expression of typological statements which uses real-valued logics to capture the empirical truth value (truth degree) of a formula on a given data source, e. g. a collection of multilingual treebanks with comparable annotation.
Uncertainty-Aware Physically-Guided Proxy Tasks for Unseen Domain Face Anti-spoofing
Face anti-spoofing (FAS) seeks to discriminate genuine faces from fake ones arising from any type of spoofing attack.
WeiPS: a symmetric fusion model framework for large-scale online learning
The recommendation system is an important commercial application of machine learning, where billions of feed views in the information flow every day.
Voting-based Approaches For Differentially Private Federated Learning
Differentially Private Federated Learning (DPFL) is an emerging field with many applications.
Improving Face Recognition by Clustering Unlabeled Faces in the Wild
While deep face recognition has benefited significantly from large-scale labeled data, current research is focused on leveraging unlabeled data to further boost performance, reducing the cost of human annotation.
Fast and Accurate Non-Projective Dependency Tree Linearization
We propose a graph-based method to tackle the dependency tree linearization task.
Ensemble Self-Training for Low-Resource Languages: Grapheme-to-Phoneme Conversion and Morphological Inflection
We present an iterative data augmentation framework, which trains and searches for an optimal ensemble and simultaneously annotates new training data in a self-training style.
Optimal Tracking Portfolio with A Ratcheting Capital Benchmark
We first transform the original problem with floor constraints into an unconstrained control problem, however, under a running maximum cost.
Optimal Consumption with Reference to Past Spending Maximum
This paper studies the infinite-horizon optimal consumption with a path-dependent reference under exponential utility.
A refined dynamic finite-strain shell theory for incompressible hyperelastic materials: equations and two-dimensional shell virtual work principle
Based on previous work for the static problem, in this paper we first derive one form of dynamic finite-strain shell equations for incompressible hyperelastic materials that involve three shell constitutive relations.
Computational Engineering, Finance, and Science Biological Physics
Towards Universal Representation Learning for Deep Face Recognition
Recognizing wild faces is extremely hard as they appear with all kinds of variations.
DAVID: Dual-Attentional Video Deblurring
To train and evaluate on more diverse blur severity levels, we propose a Challenging DVD dataset generated from the raw DVD video set by pooling frames with different temporal windows.
IMSurReal: IMS at the Surface Realization Shared Task 2019
We introduce the IMS contribution to the Surface Realization Shared Task 2019.
Head-First Linearization with Tree-Structured Representation
We present a dependency tree linearization model with two novel components: (1) a tree-structured encoder based on bidirectional Tree-LSTM that propagates information first bottom-up then top-down, which allows each token to access information from the entire tree; and (2) a linguistically motivated head-first decoder that emphasizes the central role of the head and linearizes the subtree by incrementally attaching the dependents on both sides of the head.
Optimal Dividend Strategy for an Insurance Group with Contagious Default Risk
This paper studies the optimal dividend for a multi-line insurance group, in which each subsidiary runs a product line and is exposed to some external credit risk.
Lifetime Ruin under High-watermark Fees and Drift Uncertainty
This paper aims to make a new contribution to the study of lifetime ruin problem by considering investment in two hedge funds with high-watermark fees and drift uncertainty.
Learning the Dyck Language with Attention-based Seq2Seq Models
The generalized Dyck language has been used to analyze the ability of Recurrent Neural Networks (RNNs) to learn context-free grammars (CFGs).
Pose-variant 3D Facial Attribute Generation
We address the challenging problem of generating facial attributes using a single image in an unconstrained pose.
Optimal Stopping under Model Ambiguity: a Time-Consistent Equilibrium Approach
This allows us to capture much more diverse behavior, depending on an agent's ambiguity attitude, beyond the standard worst-case (or best-case) analysis.
Risk-Sensitive Credit Portfolio Optimization under Partial Information and Contagion Risk
The verification theorem can be concluded with the aid of our BSDE results, which in turn yields the uniqueness of the solution to the BSDE.
Unsupervised Domain Adaptation for Distance Metric Learning
Unsupervised domain adaptation is a promising avenue to enhance the performance of deep neural networks on a target domain, using labels only from a source domain.
Marshall-Olkin Power-Law Distributions in Length-Frequency of Entities
Entities have different forms in different linguistic tasks and researchers treat those different forms as different concepts.
IMS at the PolEval 2018: A Bulky Ensemble Depedency Parser meets 12 Simple Rules for Predicting Enhanced Dependencies in Polish
In this paper we show which components of the system were the most responsible for its final performance.
Approximate Dynamic Oracle for Dependency Parsing with Reinforcement Learning
We present a general approach with reinforcement learning (RL) to approximate dynamic oracles for transition systems where exact dynamic oracles are difficult to derive.
Comparing Attention-based Convolutional and Recurrent Neural Networks: Success and Limitations in Machine Reading Comprehension
We propose a machine reading comprehension model based on the compare-aggregate framework with two-staged attention that achieves state-of-the-art results on the MovieQA question answering dataset.
Feature Transfer Learning for Deep Face Recognition with Under-Represented Data
In this paper, we propose a center-based feature transfer framework to augment the feature space of under-represented subjects from the regular subjects that have sufficiently diverse samples.
Gotta Adapt 'Em All: Joint Pixel and Feature-Level Domain Adaptation for Recognition in the Wild
Recent developments in deep domain adaptation have allowed knowledge transfer from a labeled source domain to an unlabeled target domain at the level of intermediate features or input pixels.
Deep Supervision with Intermediate Concepts
In this work, we explore an approach for injecting prior domain structure into neural network training by supervising hidden layers of a CNN with intermediate concepts that normally are not observed in practice.
Learning Efficient Object Detection Models with Knowledge Distillation
In this work, we propose a new framework to learn compact and fast ob- ject detection networks with improved accuracy using knowledge distillation [20] and hint learning [34].
Unsupervised Domain Adaptation for Face Recognition in Unlabeled Videos
Despite rapid advances in face recognition, there remains a clear gap between the performance of still image-based face recognition and video-based face recognition, due to the vast difference in visual quality between the domains and the difficulty of curating diverse large-scale video datasets.
IMS at the CoNLL 2017 UD Shared Task: CRFs and Perceptrons Meet Neural Networks
This paper presents the IMS contribution to the CoNLL 2017 Shared Task.
A General-Purpose Tagger with Convolutional Neural Networks
We present a general-purpose tagger based on convolutional neural networks (CNN), used for both composing word vectors and encoding context information.
Character Composition Model with Convolutional Neural Networks for Dependency Parsing on Morphologically Rich Languages
We present a transition-based dependency parser that uses a convolutional neural network to compose word representations from characters.
Towards Large-Pose Face Frontalization in the Wild
Despite recent advances in face recognition using deep learning, severe accuracy drops are observed for large pose variations in unconstrained environments.
Reconstruction-Based Disentanglement for Pose-invariant Face Recognition
Finally, we propose a new feature reconstruction metric learning to explicitly disentangle identity and pose, by demanding alignment between the feature reconstructions through various combinations of identity and pose features, which is obtained from two images of the same subject.
Deep Supervision with Shape Concepts for Occlusion-Aware 3D Object Parsing
Monocular 3D object parsing is highly desirable in various scenarios including occlusion reasoning and holistic scene interpretation.
Deep Deformation Network for Object Landmark Localization
We propose a novel cascaded framework, namely deep deformation network (DDN), for localizing landmarks in non-rigid objects.
3D Face Tracking and Multi-Scale, Spatio-temporal Analysis of Linguistically Significant Facial Expressions and Head Positions in ASL
Essential grammatical information is conveyed in signed languages by clusters of events involving facial expressions and movements of the head and upper body.
