Search Results for author:
Found 219 papers, 99 papers with code
Label Definitions Improve Semantic Role Labeling
Given a sentence and the predicate, a semantic role label is assigned to each argument of the predicate.
SmartCiteCon: Implicit Citation Context Extraction from Academic Literature Using Supervised Learning
The tool is built on a Support Vector Machine (SVM) model trained on a set of 7, 058 manually annotated citation context sentences, curated from 34, 000 papers from the ACL Anthology.
Multi-Level Gazetteer-Free Geocoding
We present a multi-level geocoding model (MLG) that learns to associate texts to geographic coordinates.
Is “My Favorite New Movie” My Favorite Movie? Probing the Understanding of Recursive Noun Phrases
We introduce the Recursive Noun Phrase Challenge (RNPC), a dataset of three textual inference tasks involving textual entailment and event plausibility comparison, precisely targeting the understanding of recursive NPs.
Generative Semantic Segmentation
To that end, the segmentation mask is expressed with a special type of image (dubbed as maskige).
Single-view Neural Radiance Fields with Depth Teacher
Moreover, it has poor generalizations to new scenes and requires retraining or fine-tuning on each scene.
RTMPose: Real-Time Multi-Person Pose Estimation based on MMPose
Recent studies on 2D pose estimation have achieved excellent performance on public benchmarks, yet its application in the industrial community still suffers from heavy model parameters and high latency.
Ranked #1 on
2D Human Pose Estimation
on COCO-WholeBody
QVRF: A Quantization-error-aware Variable Rate Framework for Learned Image Compression
In this paper, we present a Quantization-error-aware Variable Rate Framework (QVRF) that utilizes a univariate quantization regulator a to achieve wide-range variable rates within a single model.
Self-Asymmetric Invertible Network for Compression-Aware Image Rescaling
In this paper, we propose the Self-Asymmetric Invertible Network (SAIN) for compression-aware image rescaling.
FAME-ViL: Multi-Tasking Vision-Language Model for Heterogeneous Fashion Tasks
In the fashion domain, there exists a variety of vision-and-language (V+L) tasks, including cross-modal retrieval, text-guided image retrieval, multi-modal classification, and image captioning.
S-NeRF: Neural Radiance Fields for Street Views
Specifically, we improve the scene parameterization function and the camera poses for learning better neural representations from street views.
Nonlinear Intensity, Scale and Rotation Invariant Matching for Multimodal Images
We present an effective method for the matching of multimodal images.
Human-in-the-Loop Schema Induction
Schema induction builds a graph representation explaining how events unfold in a scenario.
Multi-Task Differential Privacy Under Distribution Skew
We study the problem of multi-task learning under user-level differential privacy, in which $n$ users contribute data to $m$ tasks, each involving a subset of users.
Preconditioned Score-based Generative Models
Compared with the latest generative models (\eg, CLD-SGM, DDIM, and Analytic-DDIM), PDS can achieve the best sampling quality on CIFAR-10 at a FID score of 1. 99.
Syntax and Domain Aware Model for Unsupervised Program Translation
The experimental results on function translation tasks between Python, Java, and C++ show that SDA-Trans outperforms many large-scale pre-trained models, especially for unseen language translation.
SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modeling
By relating masked modeling to manifold learning, SimMTM proposes to recover masked time points by the weighted aggregation of multiple neighbors outside the manifold, which eases the reconstruction task by assembling ruined but complementary temporal variations from multiple masked series.
Faithful Chain-of-Thought Reasoning
Together with self-consistency decoding, we achieve new state-of-the-art few-shot performance on 7 out of the 10 datasets, showing a strong synergy between faithfulness and accuracy.
SeaFormer: Squeeze-enhanced Axial Transformer for Mobile Semantic Segmentation
Coupled with a light segmentation head, we achieve the best trade-off between segmentation accuracy and latency on the ARM-based mobile devices on the ADE20K and Cityscapes datasets.
Causal Reasoning of Entities and Events in Procedural Texts
By injecting the causal relations between entities and events as intermediate reasoning steps in our representation, we further boost the performance to . 67 F1.
LB-SimTSC: An Efficient Similarity-Aware Graph Neural Network for Semi-Supervised Time Series Classification
Recently, Similarity-aware Time Series Classification (SimTSC) is proposed to address this problem by using a graph neural network classification model on the graph generated from pairwise Dynamic Time Warping (DTW) distance of batch data.
PMP: Privacy-Aware Matrix Profile against Sensitive Pattern Inference for Time Series
During the process, data sharing is often involved to allow the third-party modelers to perform specific time series data mining (TSDM) tasks based on the need of data owner.
Language Models are Drummers: Drum Composition with Natural Language Pre-Training
Automatic music generation with artificial intelligence typically requires a large amount of data which is hard to obtain for many less common genres and musical instruments.
MSV Challenge 2022: NPU-HC Speaker Verification System for Low-resource Indian Languages
Specifically, the weight transfer fine-tuning aims to constrain the distance of the weights between the pre-trained model and the fine-tuned model, which takes advantage of the previously acquired discriminative ability from the large-scale out-domain datasets and avoids catastrophic forgetting and overfitting at the same time.
Panoramic Video Salient Object Detection with Ambisonic Audio Guidance
In this paper, we aim to tackle the video salient object detection problem for panoramic videos, with their corresponding ambisonic audios.
Robust Time Series Chain Discovery with Incremental Nearest Neighbors
Inspired by a recent work that tracks how the nearest neighbor of a time series subsequence changes over time, we introduce a new TSC definition which is much more robust to noise in the data, in the sense that they can better locate the evolving patterns while excluding the non-evolving ones.
TSUP Speaker Diarization System for Conversational Short-phrase Speaker Diarization Challenge
In this challenge, we explore three kinds of typical speaker diarization systems, which are spectral clustering(SC) based diarization, target-speaker voice activity detection(TS-VAD) and end-to-end neural diarization(EEND) respectively.
Generative Model Watermarking Based on Human Visual System
Intellectual property protection of deep neural networks is receiving attention from more and more researchers, and the latest research applies model watermarking to generative models for image processing.
NWPU-ASLP System for the VoicePrivacy 2022 Challenge
Our system consists of four modules, including feature extractor, acoustic model, anonymization module, and neural vocoder.
Dynamic Graph Message Passing Networks for Visual Recognition
A fully-connected graph, such as the self-attention operation in Transformers, is beneficial for such modelling, however, its computational overhead is prohibitive.
Model-Guided Multi-Contrast Deep Unfolding Network for MRI Super-resolution Reconstruction
Interpretable neural network models are of significant interest since they enhance the trustworthiness required in clinical practice when dealing with medical images.
Data-Driven Deep Supervision for Skin Lesion Classification
Specifically, we analyze the convolutional network's behavior (field-of-view) to find the location of deep supervision for improved feature extraction.
Scalable Nanophotonic-Electronic Spiking Neural Networks
Photonic devices are ideal for the design of high-bandwidth, parallel architectures matching the SNN computational paradigm.
Hierarchical Reinforcement Learning Based Video Semantic Coding for Segmentation
In this paper, we take a step forward to video semantic compression and propose the Hierarchical Reinforcement Learning based task-driven Video Semantic Coding, named as HRLVSC.
Hierarchical Reinforcement Learning
reinforcement-learning
+3
DeepInteraction: 3D Object Detection via Modality Interaction
Existing top-performance 3D object detectors typically rely on the multi-modal fusion strategy.
CelebV-HQ: A Large-Scale Video Facial Attributes Dataset
Large-scale datasets have played indispensable roles in the recent success of face generation/editing and significantly facilitated the advances of emerging research fields.
Ranked #1 on
Unconditional Video Generation
on CelebV-HQ
RCLane: Relay Chain Prediction for Lane Detection
This is because most of the existing lane detection methods either treat the lane detection as a dense prediction or a detection task, few of them consider the unique topologies (Y-shape, Fork-shape, nearly horizontal lane) of the lane markers, which leads to sub-optimal solution.
Vision Transformers: From Semantic Segmentation to Dense Prediction
In this work, for the first time we explore the global context learning potentials of ViTs for dense visual prediction (e. g., semantic segmentation).
FashionViL: Fashion-Focused Vision-and-Language Representation Learning
We thus propose a Multi-View Contrastive Learning task for pulling closer the visual representation of one image to the compositional multimodal representation of another image+text.
What Makes for Automatic Reconstruction of Pulmonary Segments
The automatic reconstruction of pulmonary segments by ImPulSe is accurate in metrics and visually appealing.
SiamMask: A Framework for Fast Online Object Tracking and Segmentation
In this paper we introduce SiamMask, a framework to perform both visual object tracking and video object segmentation, in real-time, with the same simple method.
Accelerating Score-based Generative Models with Preconditioned Diffusion Sampling
However, a fundamental limitation is that their inference is very slow due to a need for many (e. g., 2000) iterations of sequential computations.
Softmax-free Linear Transformers
Crucially, with a linear complexity, much longer token sequences are permitted in SOFT, resulting in superior trade-off between accuracy and complexity.
PolarFormer: Multi-camera 3D Object Detection with Polar Transformer
3D object detection in autonomous driving aims to reason "what" and "where" the objects of interest present in a 3D world.
Ranked #8 on
3D Object Detection
on nuScenes Camera Only
Knowledge-aware Neural Collective Matrix Factorization for Cross-domain Recommendation
In this paper, we propose to incorporate the knowledge graph (KG) for CDR, which enables items in different domains to share knowledge.
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code
This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims.
Intra Encoding Complexity Control with a Time-Cost Model for Versatile Video Coding
Inspired by rate control algorithms, we propose a scheme to precisely control the intra encoding complexity of VVC.
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
Learning Ego 3D Representation as Ray Tracing
A self-driving perception model aims to extract 3D semantic representations from multiple cameras collectively into the bird's-eye-view (BEV) coordinate frame of the ego car in order to ground downstream planner.
Accelerating Score-based Generative Models for High-Resolution Image Synthesis
To ensure stability of convergence in sampling and generation quality, however, this sequential sampling process has to take a small step size and many sampling iterations (e. g., 2000).
Region-Aware Metric Learning for Open World Semantic Segmentation via Meta-Channel Aggregation
Therefore, we propose a method called region-aware metric learning (RAML), which first separates the regions of the images and generates region-aware features for further metric learning.
Reasoning about Procedures with Natural Language Processing: A Tutorial
This tutorial provides a comprehensive and in-depth view of the research on procedures, primarily in Natural Language Processing.
ONCE-3DLanes: Building Monocular 3D Lane Detection
We present ONCE-3DLanes, a real-world autonomous driving dataset with lane layout annotation in 3D space.
In Defense of Subspace Tracker: Orthogonal Embedding for Visual Tracking
The paper focuses on a classical tracking model, subspace learning, grounded on the fact that the targets in successive frames are considered to reside in a low-dimensional subspace or manifold due to the similarity in their appearances.
Bidirectional Self-Training with Multiple Anisotropic Prototypes for Domain Adaptive Semantic Segmentation
A thriving trend for domain adaptive segmentation endeavors to generate the high-quality pseudo labels for target domain and retrain the segmentor on them.
Ranked #11 on
Domain Adaptation
on GTA5 to Cityscapes
CholecTriplet2021: A benchmark challenge for surgical action triplet recognition
In this paper, we present the challenge setup and assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge.
Ranked #1 on
Action Triplet Recognition
on CholecT50 (Challenge)
(using extra training data)
UIGR: Unified Interactive Garment Retrieval
In this paper, we propose a Unified Interactive Garment Retrieval (UIGR) framework to unify TGR and VCR.
ImpDet: Exploring Implicit Fields for 3D Object Detection
Conventional 3D object detection approaches concentrate on bounding boxes representation learning with several parameters, i. e., localization, dimension, and orientation.
BigDetection: A Large-scale Benchmark for Improved Object Detector Pre-training
Multiple datasets and open challenges for object detection have been introduced in recent years.
Ranked #1 on
Object Detection
on BigDetection val
Multi-Scale Context-Guided Lumbar Spine Disease Identification with Coarse-to-fine Localization and Classification
Accurate and efficient lumbar spine disease identification is crucial for clinical diagnosis.
Show Me More Details: Discovering Hierarchies of Procedures from Semi-structured Web Data
To this end, we develop a simple and efficient method that links steps (e. g., "purchase a camera") in an article to other articles with similar goals (e. g., "how to choose a camera"), recursively constructing the KB.
A general framework for adaptive two-index fusion attribute weighted naive Bayes
In addition, the ATFNB framework can improve the existing two-index NB model by introducing the adaptive switching factor \{beta}.
CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation
The aim was to automatically perform unilateral VS and bilateral cochlea segmentation on hrT2 as provided in the testing set (N=137).
The Devil is in the Task: Exploiting Reciprocal Appearance-Localization Features for Monocular 3D Object Detection
Low-cost monocular 3D object detection plays a fundamental role in autonomous driving, whereas its accuracy is still far from satisfactory.
Is "My Favorite New Movie" My Favorite Movie? Probing the Understanding of Recursive Noun Phrases
We introduce the Recursive Noun Phrase Challenge (RNPC), a dataset of three textual inference tasks involving textual entailment and event plausibility comparison, precisely targeting the understanding of recursive NPs.
Persistent Object Identification Leveraging Non-Visual Markers
Our objective is to locate and provide a unique identifier for each mouse in a cluttered home-cage environment through time, as a precursor to automated behaviour recognition for biological research.
SGM3D: Stereo Guided Monocular 3D Object Detection
Monocular 3D object detection aims to predict the object location, dimension and orientation in 3D space alongside the object category given only a monocular image.
ALX: Large Scale Matrix Factorization on TPUs
We present ALX, an open-source library for distributed matrix factorization using Alternating Least Squares, written in JAX.
Learning from Mistakes -- A Framework for Neural Architecture Search
We propose a novel machine learning method called Learning From Mistakes (LFM), wherein the learner improves its ability to learn by focusing more on the mistakes during revision.
Revisiting the Performance of iALS on Item Recommendation Benchmarks
Matrix factorization learned by implicit alternating least squares (iALS) is a popular baseline in recommender system research publications.
iALS++: Speeding up Matrix Factorization with Subspace Optimization
However, iALS does not scale well with large embedding dimensions, d, due to its cubic runtime dependency on d. Coordinate descent variations, iCD, have been proposed to lower the complexity to quadratic in d. In this work, we show that iCD approaches are not well suited for modern processors and can be an order of magnitude slower than a careful iALS implementation for small to mid scale embedding sizes (d ~ 100) and only perform better than iALS on large embeddings d ~ 1000.
SOFT: Softmax-free Transformer with Linear Complexity
Crucially, with a linear complexity, much longer token sequences are permitted in SOFT, resulting in superior trade-off between accuracy and complexity.
Text-Based Person Search with Limited Data
Firstly, to fully utilize the existing small-scale benchmarking datasets for more discriminative feature learning, we introduce a cross-modal momentum contrastive learning framework to enrich the training data for a given mini-batch.
Ranked #3 on
Text based Person Retrieval
on CUHK-PEDES
(using extra training data)
Unsupervised Domain Adaptation in Semantic Segmentation Based on Pixel Alignment and Self-Training
This paper proposes an unsupervised cross-modality domain adaptation approach based on pixel alignment and self-training.
Multi-Frequency Wireless Channel Measurements and Characteristics Analysis in Indoor Corridor Scenarios
In this paper, we conduct wireless channel measurements in indoor corridor scenarios at 2. 4, 5 and 6 GHz bands with bandwidth of 320 MHz.
Progressive Coordinate Transforms for Monocular 3D Object Detection
Recognizing and localizing objects in the 3D space is a crucial ability for an AI agent to perceive its surrounding environment.
Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformer
A few-shot semantic segmentation model is typically composed of a CNN encoder, a CNN decoder and a simple classifier (separating foreground and background pixels).
A Unified Efficient Pyramid Transformer for Semantic Segmentation
Semantic segmentation is a challenging problem due to difficulties in modeling context in complex scenes and class confusions along boundaries.
Goal-Oriented Script Construction
The knowledge of scripts, common chains of events in stereotypical scenarios, is a valuable asset for task-oriented natural language understanding systems.
Global Aggregation then Local Distribution for Scene Parsing
Modelling long-range contextual relationships is critical for pixel-wise prediction tasks such as semantic segmentation.
Private Alternating Least Squares: Practical Private Matrix Completion with Tighter Rates
We study the problem of differentially private (DP) matrix completion under user-level privacy.
Oneshot Differentially Private Top-k Selection
Being able to efficiently and accurately select the top-$k$ elements with differential privacy is an integral component of various private data analysis tasks.
Composite Localization for Human Pose Estimation
This work proposes a novel deep learning framework for human pose estimation called composite localization to divide the complex learning objective into two simpler ones: a sparse heatmap to find the keypoint's approximate location and two short-distance offsetmaps to obtain its final precise coordinates.
BasisNet: Two-stage Model Synthesis for Efficient Inference
With early termination, the average cost can be further reduced to 198M MAdds while maintaining accuracy of 80. 0% on ImageNet.
Ranked #578 on
Image Classification
on ImageNet
Prediction of clinical tremor severity using Rank Consistent Ordinal Regression
The videos are coupled with clinician assessed TETRAS scores, which are used as ground truth labels to train the DNN.
Delving into Data: Effectively Substitute Training for Black-box Attack
Previous substitute training approaches focus on stealing the knowledge of the target model based on real training data or synthetic data, without exploring what kind of data can further improve the transferability between the substitute and target models.
Optimize Neural Fictitious Self-Play in Regret Minimization Thinking
Optimization of deep learning algorithms to approach Nash Equilibrium remains a significant problem in imperfect information games, e. g. StarCraft and poker.
Improving Weakly-supervised Object Localization via Causal Intervention
The recent emerged weakly supervised object localization (WSOL) methods can learn to localize an object in the image only using image-level labels.
Visual Goal-Step Inference using wikiHow
Understanding what sequence of steps are needed to complete a goal can help artificial intelligence systems reason about human activities.
Ranked #1 on
VGSI
on wikiHow-image
BEFD: Boundary Enhancement and Feature Denoising for Vessel Segmentation
Blood vessel segmentation is crucial for many diagnostic and research applications.
Hierarchical Road Topology Learning for Urban Map-less Driving
The majority of current approaches in autonomous driving rely on High-Definition (HD) maps which detail the road geometry and surrounding area.
Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection
The objective of this paper is to learn context- and depth-aware feature representation to solve the problem of monocular 3D object detection.
Ranked #9 on
Monocular 3D Object Detection
on KITTI Cars Moderate
Learning Dynamic Alignment via Meta-filter for Few-shot Learning
Our insight is that these methods would lead to poor adaptation with redundant matching, and leveraging channel-wise adjustment is the key to well adapting the learned knowledge to new classes.
Robust and Accurate Object Detection via Adversarial Learning
Data augmentation has become a de facto component for training high-performance deep image classifiers, but its potential is under-explored for object detection.
Complementary Evidence Identification in Open-Domain Question Answering
This paper proposes a new problem of complementary evidence identification for open-domain question answering (QA).
MoViNets: Mobile Video Networks for Efficient Video Recognition
We present Mobile Video Networks (MoViNets), a family of computation and memory efficient video networks that can operate on streaming video for online inference.
Ranked #3 on
Action Classification
on Charades
Automatically detecting the conflicts between software requirements based on finer semantic analysis
Besides, our approach is capable of transforming the natural language functional requirements into eight semantic tuples, which is useful not only the detection of the conflicts between requirements but also some other tasks such as constructing the association between requirements and so on.
The NPU System for the 2020 Personalized Voice Trigger Challenge
This paper describes the system developed by the NPU team for the 2020 personalized voice trigger challenge.
EEGFuseNet: Hybrid Unsupervised Deep Feature Characterization and Fusion for High-Dimensional EEG with An Application to Emotion Recognition
The performance of the extracted deep and low-dimensional features by EEGFuseNet is carefully evaluated in an unsupervised emotion recognition application based on three public emotion databases.
Failure Prediction in Production Line Based on Federated Learning: An Empirical Study
Data protection across organizations is limiting the application of centralized learning (CL) techniques.
Few-shot Action Recognition with Prototype-centered Attentive Learning
Extensive experiments on four standard few-shot action benchmarks show that our method clearly outperforms previous state-of-the-art methods, with the improvement particularly significant (10+\%) on the most challenging fine-grained action recognition benchmark.
TEAC: Intergrating Trust Region and Max Entropy Actor Critic for Continuous Control
Trust region methods and maximum entropy methods are two state-of-the-art branches used in reinforcement learning (RL) for the benefits of stability and exploration in continuous environments, respectively.
Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers
In this paper, we aim to provide an alternative perspective by treating semantic segmentation as a sequence-to-sequence prediction task.
Ranked #1 on
Semantic Segmentation
on FoodSeg103
(using extra training data)
Hop-Hop Relation-aware Graph Neural Networks
Graph Neural Networks (GNNs) are widely used in graph representation learning.
Unifying Homophily and Heterophily Network Transformation via Motifs
Because H2NT can sparsify networks with motif structures, it can also improve the computational efficiency of existing network embedding methods when integrated.
A Systematic Literature Review on Federated Learning: From A Model Quality Perspective
In this process, the server uses an incentive mechanism to encourage clients to contribute high-quality and large-volume data to improve the global model.
Rankmax: An Adaptive Projection Alternative to the Softmax Function
Several machine learning models involve mapping a score vector to a probability vector.
Boundary-sensitive Pre-training for Temporal Localization in Videos
However, most existing models developed for these tasks are pre-trained on general video action classification tasks.
Ranked #16 on
Temporal Action Localization
on ActivityNet-1.3
Direct Classification of Emotional Intensity
In this paper, we present a model that can directly predict emotion intensity score from video inputs, instead of deriving from action units.
Skin disease diagnosis with deep learning: a review
As an important part of this article, we then review the literature involving deep learning methods for skin disease diagnosis from several aspects according to the specific tasks.
Towards Efficient Scene Understanding via Squeeze Reasoning
Instead of propagating information on the spatial map, we first learn to squeeze the input feature into a channel-wise global vector and perform reasoning within the single vector where the computation cost can be significantly reduced.
Depth Guided Adaptive Meta-Fusion Network for Few-shot Video Recognition
Humans can easily recognize actions with only a few examples given, while the existing video recognition models still heavily rely on the large-scale labeled data inputs.
Ranked #1 on
Few Shot Action Recognition
on Kinetics-100
LID 2020: The Learning from Imperfect Data Challenge Results
The purpose of the Learning from Imperfect Data (LID) workshop is to inspire and facilitate the research in developing novel approaches that would harness the imperfect data and improve the data-efficiency during training.
Towards Optimal Filter Pruning with Balanced Performance and Pruning Speed
In this paper, we propose a balanced filter pruning method for both performance and pruning speed.
Holistic Grid Fusion Based Stop Line Estimation
Intersection scenarios provide the most complex traffic situations in Autonomous Driving and Driving Assistance Systems.
Small but Mighty: New Benchmarks for Split and Rephrase
Split and Rephrase is a text simplification task of rewriting a complex sentence into simpler ones.
Reasoning about Goals, Steps, and Temporal Ordering with WikiHow
We propose a suite of reasoning tasks on two types of relations between procedural events: goal-step relations ("learn poses" is a step in the larger goal of "doing yoga") and step-step temporal relations ("buy a yoga mat" typically precedes "learn poses").
Intent Detection with WikiHow
Modern task-oriented dialog systems need to reliably understand users' intents.
Dual-constrained Deep Semi-Supervised Coupled Factorization Network with Enriched Prior
Nonnegative matrix factorization is usually powerful for learning the "shallow" parts-based representation, but it clearly fails to discover deep hierarchical information within both the basis and representation spaces.
Spatial Language Representation with Multi-Level Geocoding
We present a multi-level geocoding model (MLG) that learns to associate texts to geographic locations.
Reversing the cycle: self-supervised deep stereo through enhanced monocular distillation
In many fields, self-supervised learning solutions are rapidly evolving and filling the gap with supervised approaches.
Zero-Shot Heterogeneous Transfer Learning from Recommender Systems to Cold-Start Search Retrieval
In light of these problems, we observed that most online content platforms have both a search and a recommender system that, while having heterogeneous input spaces, can be connected through their common output item space and a shared semantic representation.
Hybrid Template Canonical Correlation Analysis Method for Enhancing SSVEP Recognition under data-limited Condition
In this study, an advanced CCA-based algorithn called hybrid template canonical correlation analysis (HTCCA) was proposed to improve the performance of brain-computer interface (BCI) based on steady state visual evoked potential (SSVEP) uuder data-linited condition.
Learning-based Computer-aided Prescription Model for Parkinson's Disease: A Data-driven Perspective
Then, we build a novel computer-aided prescription model by learning the relation between observed symptoms and prescription drug.
A Survey on Concept Factorization: From Shallow to Deep Representation Learning
In this paper, we therefore survey the recent advances on CF methodologies and the potential benchmarks by categorizing and summarizing the current methods.
Improving Semantic Segmentation via Decoupled Body and Edge Supervision
Our insight is that appealing performance of semantic segmentation requires \textit{explicitly} modeling the object \textit{body} and \textit{edge}, which correspond to the high and low frequency of the image.
A novel deep learning-based method for monochromatic image synthesis from spectral CT using photon-counting detectors
To solve this problem, in this paper, we proposed a novel deep learning-based monochromatic image synthesis method working in sinogram domain.
XingGAN for Person Image Generation
We propose a novel Generative Adversarial Network (XingGAN or CrossingGAN) for person image generation tasks, i. e., translating the pose of a given person to a desired one.
Ranked #1 on
Pose Transfer
on Market-1501
(IS metric)
How to trust unlabeled data? Instance Credibility Inference for Few-Shot Learning
We rank the credibility of pseudo-labeled instances along the regularization path of their corresponding incidental parameters, and the most trustworthy pseudo-labeled examples are preserved as the augmented labeled instances.
Egocentric Action Recognition by Video Attention and Temporal Context
In this challenge, action recognition is posed as the problem of simultaneously predicting a single `verb' and `noun' class label given an input trimmed video clip.
PriceAggregator: An Intelligent System for Hotel Price Fetching
The major challenge is that each supplier only allows Agoda to fetch for the hotel price with a limited amount of Queries Per Second (QPS).
Self-supervised Video Object Segmentation
The objective of this paper is self-supervised representation learning, with the goal of solving semi-supervised video object segmentation (a. k. a.
One-shot visual object segmentation
Representation Learning
+2
Long-Term Cloth-Changing Person Re-identification
Specifically, we consider that under cloth-changes, soft-biometrics such as body shape would be more reliable.
Style Normalization and Restitution for Generalizable Person Re-identification
Existing fully-supervised person re-identification (ReID) methods usually suffer from poor generalization capability caused by domain gaps.
Ranked #7 on
Unsupervised Domain Adaptation
on Market to Duke
SentPWNet: A Unified Sentence Pair Weighting Network for Task-specific Sentence Embedding
Our model, SentPWNet, exploits the neighboring spatial distribution of each sentence as locality weight to indicate the informative level of sentence pair.
Neural Collaborative Filtering vs. Matrix Factorization Revisited
This approach is often referred to as neural collaborative filtering (NCF).
Ranked #6 on
Link Prediction
on Yelp
A Survey on Deep Learning for Neuroimaging-based Brain Disorder Analysis
Deep learning has been recently used for the analysis of neuroimages, such as structural magnetic resonance imaging (MRI), functional MRI, and positron emission tomography (PET), and has achieved significant performance improvements over traditional machine learning in computer-aided diagnosis of brain disorders.
3D Printed Brain-Controlled Robot-Arm Prosthetic via Embedded Deep Learning from sEMG Sensors
In this paper, we present our work on developing robot arm prosthetic via deep learning.
In-Vehicle Object Detection in the Wild for Driverless Vehicles
In-vehicle human object identification plays an important role in vision-based automated vehicle driving systems while objects such as pedestrians and vehicles on roads or streets are the primary targets to protect from driverless vehicles.
Universal Adversarial Perturbations Generative Network for Speaker Recognition
Attacking deep learning based biometric systems has drawn more and more attention with the wide deployment of fingerprint/face/speaker recognition systems, given the fact that the neural networks are vulnerable to the adversarial examples, which have been intentionally perturbed to remain almost imperceptible for human.
Direct Speech-to-image Translation
In this paper, we attempt to translate the speech signals into the image signals without the transcription stage.
Multimedia Sound Audio and Speech Processing
Learning to fool the speaker recognition
Due to the widespread deployment of fingerprint/face/speaker recognition systems, attacking deep learning based biometric systems has drawn more and more attention.
Audio and Speech Processing Cryptography and Security Sound