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Found 309 papers, 136 papers with code
Multi-Label Transfer Learning for Multi-Relational Semantic Similarity
Multi-relational semantic similarity datasets define the semantic relations between two short texts in multiple ways, e. g., similarity, relatedness, and so on.
Direct Network Transfer: Transfer Learning of Sentence Embeddings for Semantic Similarity
Sentence encoders, which produce sentence embeddings using neural networks, are typically evaluated by how well they transfer to downstream tasks.
Cascaded multi-scale and multi-dimension convolutional neural network for stereo matching
Firstly, we propose a new multi-scale matching cost computation sub-network, in which two different sizes of receptive fields are implemented parallelly.
Algorithmic Collusion in Cournot Duopoly Market: Evidence from Experimental Economics
In application, we hope, the frameworks, the algorithm design as well as the experiment environment illustrated in this work, can be an incubator or a test bed for researchers and policymakers to handle the emerging algorithmic collusion.
Actor-Critic Sequence Training for Image Captioning
Generating natural language descriptions of images is an important capability for a robot or other visual-intelligence driven AI agent that may need to communicate with human users about what it is seeing.
GaDei: On Scale-up Training As A Service For Deep Learning
By evaluating the NLC workloads, we show that only the conservative hyper-parameter setup (e. g., small mini-batch size and small learning rate) can guarantee acceptable model accuracy for a wide range of customers.
On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches
The recent, remarkable growth of machine learning has led to intense interest in the privacy of the data on which machine learning relies, and to new techniques for preserving privacy.
Motion Feature Augmented Recurrent Neural Network for Skeleton-based Dynamic Hand Gesture Recognition
Dynamic hand gesture recognition has attracted increasing interests because of its importance for human computer interaction.
Ranked #6 on
Hand Gesture Recognition
on DHG-28
TAPAS: Two-pass Approximate Adaptive Sampling for Softmax
TAPAS is a novel adaptive sampling method for the softmax model.
Physics Inspired Optimization on Semantic Transfer Features: An Alternative Method for Room Layout Estimation
In this paper, we propose an alternative method to estimate room layouts of cluttered indoor scenes.
Learning to Learn: Meta-Critic Networks for Sample Efficient Learning
We propose a novel and flexible approach to meta-learning for learning-to-learn from only a few examples.
Classification of Neurological Gait Disorders Using Multi-task Feature Learning
An advanced machine learning method, multi-task feature learning (MTFL), is used to jointly train classification models of a subject's gait in three classes, post-stroke, PD and healthy gait.
Real-Time Visual Tracking: Promoting the Robustness of Correlation Filter Learning
By exploiting the anisotropy of the filter response, three sparsity related loss functions are proposed to alleviate the overfitting issue of previous methods and improve the overall tracking performance.
Tracking Completion
A fundamental component of modern trackers is an online learned tracking model, which is typically modeled either globally or locally.
Learning a Discriminative Null Space for Person Re-identification
Most existing person re-identification (re-id) methods focus on learning the optimal distance metrics across camera views.
Ranked #116 on
Person Re-Identification
on Market-1501
Differentially Private False Discovery Rate Control
Differential privacy provides a rigorous framework for privacy-preserving data analysis.
Efficient Training on Very Large Corpora via Gramian Estimation
We study the problem of learning similarity functions over very large corpora using neural network embedding models.
Automated segmentaiton and classification of arterioles and venules using Cascading Dilated Convolutional Neural Networks
The change of retinal vasculature is an early sign of many vascular and systematic diseases, such as diabetes and hypertension.
Nearly Optimal Private LASSO
In addition, we show that this error bound is nearly optimal amongst all differentially private algorithms.
CAN: Constrained Attention Networks for Multi-Aspect Sentiment Analysis
Aspect level sentiment classification is a fine-grained sentiment analysis task.
Depth creates no more spurious local minima
We show that for any convex differentiable loss, a deep linear network has no spurious local minima as long as it is true for the two layer case.
Exemplar-Based Face Parsing
Given a test image, our algorithm first selects a subset of exemplar images from the database, Our algorithm then computes a nonrigid warp for each exemplar image to align it with the test image.
Nonparametric Context Modeling of Local Appearance for Pose- and Expression-Robust Facial Landmark Localization
We propose a data-driven approach to facial landmark localization that models the correlations between each landmark and its surrounding appearance features.
Discriminative Low-Rank Tracking
Good tracking performance is in general attributed to accurate representation over previously obtained targets or reliable discrimination between the target and the surrounding background.
Decoder Network Over Lightweight Reconstructed Feature for Fast Semantic Style Transfer
Our method decomposes the semantic style transfer problem into feature reconstruction part and feature decoder part.
Field-aware Neural Factorization Machine for Click-Through Rate Prediction
This model can have strong second order feature interactive learning ability like Field-aware Factorization Machine, on this basis, deep neural network is used for higher-order feature combination learning.
Monte Carlo Neural Fictitious Self-Play: Approach to Approximate Nash equilibrium of Imperfect-Information Games
Experimentally, we demonstrate that the proposed Monte Carlo Neural Fictitious Self Play can converge to approximate Nash equilibrium in games with large-scale search depth while the Neural Fictitious Self Play can't.
Deep Learning based Pedestrian Detection at Distance in Smart Cities
Generative adversarial networks (GANs) have been promising for many computer vision problems due to their powerful capabilities to enhance the data for training and test.
Hierarchical method for cataract grading based on retinal images using improved Haar wavelet
In this paper, a feature extraction-based method for grading cataract severity using retinal images is proposed.
Automated Segmentation of Pulmonary Lobes using Coordination-Guided Deep Neural Networks
The proposed model is trained and evaluated on a few publicly available datasets and has achieved the state-of-the-art accuracy with a mean Dice coefficient index of 0. 947 $\pm$ 0. 044.
Jointly Learning Structured Analysis Discriminative Dictionary and Analysis Multiclass Classifier
Then we compute a linear classifier based on the approximated sparse codes by an analysis mechanism to simultaneously consider the classification and representation powers.
Distant Pedestrian Detection in the Wild using Single Shot Detector with Deep Convolutional Generative Adversarial Networks
In our work, GAN has been trained intensively on low resolution images, in order to neutralize the challenges of the pedestrian detection in the wild, and considered humans, and few other classes for detection in smart cities.
End-to-End Learning of Multi-scale Convolutional Neural Network for Stereo Matching
To tackle this problem, we propose a network for disparity estimation based on abundant contextual details and semantic information, called Multi-scale Features Network (MSFNet).
ASCNet: Adaptive-Scale Convolutional Neural Networks for Multi-Scale Feature Learning
Such pixel-level dilation rates produce optimal receptive fields so that the information of objects with different sizes can be extracted at the corresponding scale.
Multi-level Domain Adaptive learning for Cross-Domain Detection
In recent years, object detection has shown impressive results using supervised deep learning, but it remains challenging in a cross-domain environment.
Annotation-Free Cardiac Vessel Segmentation via Knowledge Transfer from Retinal Images
Segmenting coronary arteries is challenging, as classic unsupervised methods fail to produce satisfactory results and modern supervised learning (deep learning) requires manual annotation which is often time-consuming and can some time be infeasible.
A cascaded dual-domain deep learning reconstruction method for sparsely spaced multidetector helical CT
This step is to suppress helical artifacts and reduce the following computational cost.
Large-scale Gastric Cancer Screening and Localization Using Multi-task Deep Neural Network
Gastric cancer is one of the most common cancers, which ranks third among the leading causes of cancer death.
Discriminative Local Sparse Representation by Robust Adaptive Dictionary Pair Learning
In this paper, we propose a structured Robust Adaptive Dic-tionary Pair Learning (RA-DPL) framework for the discrim-inative sparse representation learning.
Unsupervised Learnable Sinogram Inpainting Network (SIN) for Limited Angle CT reconstruction
In this paper, we propose a sinogram inpainting network (SIN) to solve limited-angle CT reconstruction problem, which is a very challenging ill-posed issue and of great interest for several clinical applications.
Medical Physics Image and Video Processing
Deep Self-representative Concept Factorization Network for Representation Learning
In this paper, we investigate the unsupervised deep representation learning issue and technically propose a novel framework called Deep Self-representative Concept Factorization Network (DSCF-Net), for clustering deep features.
Automatic marker-free registration of tree point-cloud data based on rotating projection
In coarse registration, point clouds produced by each scan are projected onto a spherical surface to generate a series of two-dimensional (2D) images, which are used to estimate the initial positions of multiple scans.
Selective Convolutional Network: An Efficient Object Detector with Ignoring Background
It is well known that attention mechanisms can effectively improve the performance of many CNNs including object detectors.
Superbloom: Bloom filter meets Transformer
We extend the idea of word pieces in natural language models to machine learning tasks on opaque ids.
Feedback Graph Convolutional Network for Skeleton-based Action Recognition
It transmits the high-level semantic features to the low-level layers and flows temporal information stage by stage to progressively model global spatial-temporal features for action recognition; (3) The FGCN model provides early predictions.
Ranked #34 on
Skeleton Based Action Recognition
on NTU RGB+D 120
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.
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.
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.
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.
Long-Term Cloth-Changing Person Re-identification
Specifically, we consider that under cloth-changes, soft-biometrics such as body shape would be more reliable.
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.
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.
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.
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.
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).
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.
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.
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.
Holistic Grid Fusion Based Stop Line Estimation
Intersection scenarios provide the most complex traffic situations in Autonomous Driving and Driving Assistance Systems.
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.
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.
Rankmax: An Adaptive Projection Alternative to the Softmax Function
Several machine learning models involve mapping a score vector to a probability vector.
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.
Searching for Quasi-Periodic Modulations in $γ$-ray Active Galactic Nuclei
We perform a systematic search of quasi-periodic variabilities in $\gamma$-ray active galactic nuclei (AGNs) in the third \emph{Fermi} Large Area Telescope source catalog (3FGL).
High Energy Astrophysical Phenomena
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.
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.
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.
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.
Complementary Evidence Identification in Open-Domain Question Answering
This paper proposes a new problem of complementary evidence identification for open-domain question answering (QA).
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.
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.
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.
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.
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.
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 #664 on
Image Classification
on ImageNet
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.
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.
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.
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.
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.
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-Level Gazetteer-Free Geocoding
We present a multi-level geocoding model (MLG) that learns to associate texts to geographic coordinates.
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.
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.
Depth creates no more spurious local minima in linear networks
We show that for any convex differentiable loss, a deep linear network has no spurious local minima as long as it is true for the two layer case.
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.
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.
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}.
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.
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.
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.
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).
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.
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.
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.
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.
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.
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.
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
Scalable Nanophotonic-Electronic Spiking Neural Networks
Photonic devices are ideal for the design of high-bandwidth, parallel architectures matching the SNN computational paradigm.
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.
NWPU-ASLP System for the VoicePrivacy 2022 Challenge
Our system consists of four modules, including feature extractor, acoustic model, anonymization module, and neural vocoder.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Human-in-the-Loop Schema Induction
Schema induction builds a graph representation explaining how events unfold in a scenario.
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.
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.
Devil is in the Queries: Advancing Mask Transformers for Real-world Medical Image Segmentation and Out-of-Distribution Localization
Real-world medical image segmentation has tremendous long-tailed complexity of objects, among which tail conditions correlate with relatively rare diseases and are clinically significant.
Learning by Grouping: A Multilevel Optimization Framework for Improving Fairness in Classification without Losing Accuracy
Our proposed framework involves three stages of learning, which are formulated as a three-level optimization problem: (i) learning to group problems into different subgroups; (ii) learning group-specific sub-models for problem-solving; and (iii) updating group assignments of training examples by minimizing the validation loss.
Federated Learning of Shareable Bases for Personalization-Friendly Image Classification
FedBasis learns a set of few shareable ``basis'' models, which can be linearly combined to form personalized models for clients.
OPDN: Omnidirectional Position-aware Deformable Network for Omnidirectional Image Super-Resolution
Model A aims to enhance the feature extraction ability of 360{\deg} image positional information, while Model B further focuses on the high-frequency information of 360{\deg} images.
Hybrid Transformer and CNN Attention Network for Stereo Image Super-resolution
Multi-stage strategies are frequently employed in image restoration tasks.
Train-Once-for-All Personalization
We study the problem of how to train a "personalization-friendly" model such that given only the task descriptions, the model can be adapted to different end-users' needs, e. g., for accurately classifying different subsets of objects.
propnet: Propagating 2D Annotation to 3D Segmentation for Gastric Tumors on CT Scans
This model consists of a proposing stage for coarse segmentation and a refining stage for improved segmentation, using two-way branches for enhanced performance and an up-down strategy for efficiency.
Answering Compositional Queries with Set-Theoretic Embeddings
The need to compactly and robustly represent item-attribute relations arises in many important tasks, such as faceted browsing and recommendation systems.
Video Compression with Arbitrary Rescaling Network
Most video platforms provide video streaming services with different qualities, and the quality of the services is usually adjusted by the resolution of the videos.
CD-CTFM: A Lightweight CNN-Transformer Network for Remote Sensing Cloud Detection Fusing Multiscale Features
In the decoder part, we utilize a lightweight network combing CNN and Transformer as backbone, which is conducive to extract local and global features simultaneously.
MCPI: Integrating Multimodal Data for Enhanced Prediction of Compound Protein Interactions
The identification of compound-protein interactions (CPI) plays a critical role in drug screening, drug repurposing, and combination therapy studies.
Joint Coordinate Regression and Association For Multi-Person Pose Estimation, A Pure Neural Network Approach
We introduce a novel one-stage end-to-end multi-person 2D pose estimation algorithm, known as Joint Coordinate Regression and Association (JCRA), that produces human pose joints and associations without requiring any post-processing.
SUIT: Learning Significance-guided Information for 3D Temporal Detection
To this end, we propose to learn Significance-gUided Information for 3D Temporal detection (SUIT), which simplifies temporal information as sparse features for information fusion across frames.
Towards Efficient In-memory Computing Hardware for Quantized Neural Networks: State-of-the-art, Open Challenges and Perspectives
Limited energy and computational resources on edge push the transition from traditional von Neumann architectures to In-memory Computing (IMC), especially for machine learning and neural network applications.
Cluster-Induced Mask Transformers for Effective Opportunistic Gastric Cancer Screening on Non-contrast CT Scans
In our experiments, the proposed method achieves a sensitivity of 85. 0% and specificity of 92. 6% for detecting gastric tumors on a hold-out test set consisting of 100 patients with cancer and 148 normal.
Deep neural network improves the estimation of polygenic risk scores for breast cancer
In the test cohort with 50% prevalence, the Area Under the receiver operating characteristic Curve (AUC) were 67. 4% for DNN, 64. 2% for BLUP, 64. 5% for BayesA, and 62. 4% for LDpred.
Improved Prognostic Prediction of Pancreatic Cancer Using Multi-Phase CT by Integrating Neural Distance and Texture-Aware Transformer
Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer in which the tumor-vascular involvement greatly affects the resectability and, thus, overall survival of patients.
Semi-Supervised Dual-Stream Self-Attentive Adversarial Graph Contrastive Learning for Cross-Subject EEG-based Emotion Recognition
In this paper, a semi-supervised Dual-stream Self-Attentive Adversarial Graph Contrastive learning framework (termed as DS-AGC) is proposed to tackle the challenge of limited labeled data in cross-subject EEG-based emotion recognition.
Designs and Implementations in Neural Network-based Video Coding
The past decade has witnessed the huge success of deep learning in well-known artificial intelligence applications such as face recognition, autonomous driving, and large language model like ChatGPT.
Choice-75: A Dataset on Decision Branching in Script Learning
Script learning studies how stereotypical events unfold, enabling machines to reason about narratives with implicit information.
Private Matrix Factorization with Public Item Features
It can be applied to different types of public item data, including: (1) categorical item features; (2) item-item similarities learned from public sources; and (3) publicly available user feedback.
Translating Images to Road Network: A Non-Autoregressive Sequence-to-Sequence Approach
The extraction of road network is essential for the generation of high-definition maps since it enables the precise localization of road landmarks and their interconnections.
CLIN: A Continually Learning Language Agent for Rapid Task Adaptation and Generalization
Language agents have shown some ability to interact with an external environment, e. g., a virtual world such as ScienceWorld, to perform complex tasks, e. g., growing a plant, without the startup costs of reinforcement learning.
CBARF: Cascaded Bundle-Adjusting Neural Radiance Fields from Imperfect Camera Poses
Existing volumetric neural rendering techniques, such as Neural Radiance Fields (NeRF), face limitations in synthesizing high-quality novel views when the camera poses of input images are imperfect.
Private Learning with Public Features
We study a class of private learning problems in which the data is a join of private and public features.
Consistent4D: Consistent 360° Dynamic Object Generation from Monocular Video
This is achieved by leveraging the object-level 3D-aware image diffusion model as the primary supervision signal for training Dynamic Neural Radiance Fields (DyNeRF).
One Size Does Not Fit All: Customizing Open-Domain Procedures
Our approach is to test several simple multi-LLM-agent architectures for customization, as well as an end-to-end LLM, using a new evaluation set, called CustomPlans, of over 200 WikiHow procedures each with a customization need.
Relightable 3D Gaussian: Real-time Point Cloud Relighting with BRDF Decomposition and Ray Tracing
We present a novel differentiable point-based rendering framework for material and lighting decomposition from multi-view images, enabling editing, ray-tracing, and real-time relighting of the 3D point cloud.
Demonstration of Programmable Brain-Inspired Optoelectronic Neuron in Photonic Spiking Neural Network with Neural Heterogeneity
Photonic Spiking Neural Networks (PSNN) composed of the co-integrated CMOS and photonic elements can offer low loss, low power, highly-parallel, and high-throughput computing for brain-inspired neuromorphic systems.
Periodic Vibration Gaussian: Dynamic Urban Scene Reconstruction and Real-time Rendering
To address this challenge, we present a unified representation model, called Periodic Vibration Gaussian (PVG).
Joint Training or Not: An Exploration of Pre-trained Speech Models in Audio-Visual Speaker Diarization
The scarcity of labeled audio-visual datasets is a constraint for training superior audio-visual speaker diarization systems.
FGENet: Fine-Grained Extraction Network for Congested Crowd Counting
Crowd counting has gained significant popularity due to its practical applications.
Ranked #1 on
Crowd Counting
on ShanghaiTech A
Optimal Transcoding Resolution Prediction for Efficient Per-Title Bitrate Ladder Estimation
In this paper, we propose to directly predict the optimal transcoding resolution at each preset bitrate for efficient bitrate ladder construction.
UPDP: A Unified Progressive Depth Pruner for CNN and Vision Transformer
Our method also achieves state-of-the-art pruning performance on the vision transformer model.
Fast Dynamic 3D Object Generation from a Single-view Video
Generating dynamic 3D object from a single-view video is challenging due to the lack of 4D labeled data.
LVC-LGMC: Joint Local and Global Motion Compensation for Learned Video Compression
To validate the effectiveness of our proposed LGMC, we integrate it with DCVC-TCM and obtain learned video compression with joint local and global motion compensation (LVC-LGMC).
S-NeRF++: Autonomous Driving Simulation via Neural Reconstruction and Generation
Autonomous driving simulation system plays a crucial role in enhancing self-driving data and simulating complex and rare traffic scenarios, ensuring navigation safety.
TimeSiam: A Pre-Training Framework for Siamese Time-Series Modeling
To emphasize temporal correlation modeling, this paper proposes TimeSiam as a simple but effective self-supervised pre-training framework for Time series based on Siamese networks.
RA-Rec: An Efficient ID Representation Alignment Framework for LLM-based Recommendation
To address this limitation, we propose a new paradigm, ID representation, which incorporates pre-trained ID embeddings into LLMs in a complementary manner.
A Neural-network Enhanced Video Coding Framework beyond ECM
In this paper, a hybrid video compression framework is proposed that serves as a demonstrative showcase of deep learning-based approaches extending beyond the confines of traditional coding methodologies.
Calibrating Large Language Models with Sample Consistency
Accurately gauging the confidence level of Large Language Models' (LLMs) predictions is pivotal for their reliable application.
FrameNeRF: A Simple and Efficient Framework for Few-shot Novel View Synthesis
We present a novel framework, called FrameNeRF, designed to apply off-the-shelf fast high-fidelity NeRF models with fast training speed and high rendering quality for few-shot novel view synthesis tasks.
A First Look at GPT Apps: Landscape and Vulnerability
With the advancement of Large Language Models (LLMs), increasingly sophisticated and powerful GPTs are entering the market.
BLO-SAM: Bi-level Optimization Based Overfitting-Preventing Finetuning of SAM
Secondly, SAM faces challenges in excelling at specific downstream tasks, like medical imaging, due to a disparity between the distribution of its pretraining data, which predominantly consists of general-domain images, and the data used in downstream tasks.
From Summary to Action: Enhancing Large Language Models for Complex Tasks with Open World APIs
In this work, we introduce a novel tool invocation pipeline designed to control massive real-world APIs.
RSAM-Seg: A SAM-based Approach with Prior Knowledge Integration for Remote Sensing Image Semantic Segmentation
While the direct application of SAM to remote sensing image segmentation tasks does not yield satisfactory results, we propose RSAM-Seg, which stands for Remote Sensing SAM with Semantic Segmentation, as a tailored modification of SAM for the remote sensing field and eliminates the need for manual intervention to provide prompts.
PROC2PDDL: Open-Domain Planning Representations from Texts
Planning in a text-based environment continues to be a major challenge for AI systems.
Implicit Image-to-Image Schrodinger Bridge for CT Super-Resolution and Denoising
As a promising alternative, the Image-to-Image Schr\"odinger Bridge (I2SB) initializes the generative process from corrupted images and integrates training techniques from conditional diffusion models.
FrameQuant: Flexible Low-Bit Quantization for Transformers
If quantization is interpreted as the addition of noise, our casting of the problem allows invoking an extensive body of known consistent recovery and noise robustness guarantees.
Urban Scene Diffusion through Semantic Occupancy Map
Generating unbounded 3D scenes is crucial for large-scale scene understanding and simulation.
OpenOcc: Open Vocabulary 3D Scene Reconstruction via Occupancy Representation
We model the geometric structure of the scene with occupancy representation and distill the pre-trained open vocabulary model into a 3D language field via volume rendering for zero-shot inference.
STEntConv: Predicting Disagreement with Stance Detection and a Signed Graph Convolutional Network
The rise of social media platforms has led to an increase in polarised online discussions, especially on political and socio-cultural topics such as elections and climate change.
Peer-aided Repairer: Empowering Large Language Models to Repair Advanced Student Assignments
Automated generation of feedback on programming assignments holds significant benefits for programming education, especially when it comes to advanced assignments.
Exploring and Evaluating Hallucinations in LLM-Powered Code Generation
The rise of Large Language Models (LLMs) has significantly advanced many applications on software engineering tasks, particularly in code generation.
Effective Lymph Nodes Detection in CT Scans Using Location Debiased Query Selection and Contrastive Query Representation in Transformer
Lymph node (LN) assessment is a critical, indispensable yet very challenging task in the routine clinical workflow of radiology and oncology.
LaneCorrect: Self-supervised Lane Detection
Lane detection has evolved highly functional autonomous driving system to understand driving scenes even under complex environments.
MDDD: Manifold-based Domain Adaptation with Dynamic Distribution for Non-Deep Transfer Learning in Cross-subject and Cross-session EEG-based Emotion Recognition
The proposed MDDD includes four main modules: manifold feature transformation, dynamic distribution alignment, classifier learning, and ensemble learning.
ResVR: Joint Rescaling and Viewport Rendering of Omnidirectional Images
With the advent of virtual reality technology, omnidirectional image (ODI) rescaling techniques are increasingly embraced for reducing transmitted and stored file sizes while preserving high image quality.
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.
Persistent Animal 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.
Exploring the Curious Case of Code Prompts
Furthermore, we show that the style of code prompt has a large effect on performance for some but not all tasks and that fine-tuning on text instructions leads to better relative performance of code prompts.
In-Orbit Instrument Performance Study and Calibration for POLAR Polarization Measurements
POLAR is a compact space-borne detector designed to perform reliable measurements of the polarization for transient sources like Gamma-Ray Bursts in the energy range 50-500keV.
Instrumentation and Methods for Astrophysics High Energy Physics - Experiment Instrumentation and Detectors
Semantic Discord: Finding Unusual Local Patterns for Time Series
Finding anomalous subsequence in a long time series is a very important but difficult problem.
Analyze Gauss: Optimal Bounds for Privacy-Preserving Principal Component Analysis
We show that the well-known, but misnamed, randomized response algorithm, with properly tuned parameters, provides a nearly optimal additive quality gap compared to the best possible singular subspace of A.
Modular Blind Video Quality Assessment
Blind video quality assessment (BVQA) plays a pivotal role in evaluating and improving the viewing experience of end-users across a wide range of video-based platforms and services.
Generalizable and Stable Finetuning of Pretrained Language Models on Low-Resource Texts
Pretrained Language Models (PLMs) have advanced Natural Language Processing (NLP) tasks significantly, but finetuning PLMs on low-resource datasets poses significant challenges such as instability and overfitting.
Spatial Language Representation with Multi-Level Geocoding
We present a multi-level geocoding model (MLG) that learns to associate texts to geographic locations.
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.
Label Definitions Improve Semantic Role Labeling
Given a sentence and the predicate, a semantic role label is assigned to each argument of the predicate.
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.
Probabilistic computation and uncertainty quantification with emerging covariance
Building robust, interpretable, and secure AI system requires quantifying and representing uncertainty under a probabilistic perspective to mimic human cognitive abilities.
Scale-aware Test-time Click Adaptation for Pulmonary Nodule and Mass Segmentation
Extensive experiments on both open-source and in-house datasets consistently demonstrate the effectiveness of the proposed method over some CNN and Transformer-based segmentation methods.
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.
Improved Dense Nested Attention Network Based on Transformer for Infrared Small Target Detection
To address this issue, we propose a novel method for detecting infrared small targets called improved dense nested attention network (IDNANet), which is based on the transformer architecture.
Few-shot Action Recognition with Permutation-invariant Attention
Such encoded blocks are aggregated by permutation-invariant pooling to make our approach robust to varying action lengths and long-range temporal dependencies whose patterns are unlikely to repeat even in clips of the same class.
Ranked #6 on
Few Shot Action Recognition
on Kinetics-100
PARTNER: Level up the Polar Representation for LiDAR 3D Object Detection
Recently, polar-based representation has shown promising properties in perceptual tasks.
Automated Segmentation of Cervical Nuclei in Pap Smear Images using Deformable Multi-path Ensemble Model
Pap smear testing has been widely used for detecting cervical cancers based on the morphology properties of cell nuclei in microscopic image.
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.
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.
Nonlinear Intensity, Scale and Rotation Invariant Matching for Multimodal Images
We present an effective method for the matching of multimodal images.
Graph Node-Feature Convolution for Representation Learning
Graph convolutional network (GCN) is an emerging neural network approach.
