Search Results for author:
Found 345 papers, 134 papers with code
TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis
TimesBlock can discover the multi-periodicity adaptively and extract the complex temporal variations from transformed 2D tensors by a parameter-efficient inception block.
iTransformer: Inverted Transformers Are Effective for Time Series Forecasting
These forecasters leverage Transformers to model the global dependencies over temporal tokens of time series, with each token formed by multiple variates of the same timestamp.
RIFormer: Keep Your Vision Backbone Effective While Removing Token Mixer
Extensive experiments and ablative analysis also demonstrate that the inductive bias of network architecture, can be incorporated into simple network structure with appropriate optimization strategy.
ActionCLIP: A New Paradigm for Video Action Recognition
Moreover, to handle the deficiency of label texts and make use of tremendous web data, we propose a new paradigm based on this multimodal learning framework for action recognition, which we dub "pre-train, prompt and fine-tune".
Ranked #2 on
Action Recognition In Videos
on Kinetics-400
Self-supervised Monocular Depth Estimation for All Day Images using Domain Separation
Meanwhile, to guarantee that the day and night images contain the same information, the domain-separated network takes the day-time images and corresponding night-time images (generated by GAN) as input, and the private and invariant feature extractors are learned by orthogonality and similarity loss, where the domain gap can be alleviated, thus better depth maps can be expected.
Targetless Calibration of LiDAR-IMU System Based on Continuous-time Batch Estimation
Sensor calibration is the fundamental block for a multi-sensor fusion system.
Robotics
How Can Recommender Systems Benefit from Large Language Models: A Survey
In this paper, we conduct a comprehensive survey on this research direction from the perspective of the whole pipeline in real-world recommender systems.
Paint3D: Paint Anything 3D with Lighting-Less Texture Diffusion Models
This paper presents Paint3D, a novel coarse-to-fine generative framework that is capable of producing high-resolution, lighting-less, and diverse 2K UV texture maps for untextured 3D meshes conditioned on text or image inputs.
Parse Geometry from a Line: Monocular Depth Estimation with Partial Laser Observation
Many standard robotic platforms are equipped with at least a fixed 2D laser range finder and a monocular camera.
Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting
However, their performance can degenerate terribly on non-stationary real-world data in which the joint distribution changes over time.
Towards Knowledge-driven Autonomous Driving
This paper explores the emerging knowledge-driven autonomous driving technologies.
CL-MAPF: Multi-Agent Path Finding for Car-Like Robots with Kinematic and Spatiotemporal Constraints
In this paper, we give a mathematical formalization of Multi-Agent Path Finding for Car-Like robots (CL-MAPF) problem.
Robotics Multiagent Systems
Bootstrap Latent Representations for Multi-modal Recommendation
Besides the user-item interaction graph, existing state-of-the-art methods usually use auxiliary graphs (e. g., user-user or item-item relation graph) to augment the learned representations of users and/or items.
Learning by Analogy: Reliable Supervision from Transformations for Unsupervised Optical Flow Estimation
Unsupervised learning of optical flow, which leverages the supervision from view synthesis, has emerged as a promising alternative to supervised methods.
Ranked #2 on
Optical Flow Estimation
on KITTI 2012 unsupervised
HR-Depth: High Resolution Self-Supervised Monocular Depth Estimation
To obtainmore accurate depth estimation in large gradient regions, itis necessary to obtain high-resolution features with spatialand semantic information.
Ranked #7 on
Unsupervised Monocular Depth Estimation
on KITTI-C
Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects
To fill this gap, we review current state-of-the-art SSL methods for time series data in this article.
Medical Image Segmentation Using Squeeze-and-Expansion Transformers
Medical image segmentation is important for computer-aided diagnosis.
Ranked #1 on
Brain Tumor Segmentation
on BRATS 2019
Few-Shot Domain Adaptation with Polymorphic Transformers
Thus, the chance of overfitting the annotations is greatly reduced, and the model can perform robustly on the target domain after being trained on a few annotated images.
LogME: Practical Assessment of Pre-trained Models for Transfer Learning
In pursuit of a practical assessment method, we propose to estimate the maximum value of label evidence given features extracted by pre-trained models.
Ranked #3 on
Transferability
on classification benchmark
Ranking and Tuning Pre-trained Models: A New Paradigm for Exploiting Model Hubs
(2) The best ranked PTM can either be fine-tuned and deployed if we have no preference for the model's architecture or the target PTM can be tuned by the top $K$ ranked PTMs via a Bayesian procedure that we propose.
SSC: Semantic Scan Context for Large-Scale Place Recognition
We also present a two-step global semantic ICP to obtain the 3D pose (x, y, yaw) used to align the point cloud to improve matching performance.
Ranked #1 on
Visual Place Recognition
on KITTI
Semantic Graph Based Place Recognition for 3D Point Clouds
First, we propose a novel semantic graph representation for the point cloud scenes by reserving the semantic and topological information of the raw point cloud.
SuperLine3D: Self-supervised Line Segmentation and Description for LiDAR Point Cloud
To repetitively extract them as features and perform association between discrete LiDAR frames for registration, we propose the first learning-based feature segmentation and description model for 3D lines in LiDAR point cloud.
AnatomyNet: Deep Learning for Fast and Fully Automated Whole-volume Segmentation of Head and Neck Anatomy
Methods: Our deep learning model, called AnatomyNet, segments OARs from head and neck CT images in an end-to-end fashion, receiving whole-volume HaN CT images as input and generating masks of all OARs of interest in one shot.
Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors
While previous models suffer from complicated series variations induced by changing temporal distribution, we tackle non-stationary time series with modern Koopman theory that fundamentally considers the underlying time-variant dynamics.
Learning Quality-aware Dynamic Memory for Video Object Segmentation
However, they mainly focus on better matching between the current frame and the memory frames without explicitly paying attention to the quality of the memory.
Ranked #11 on
Semi-Supervised Video Object Segmentation
on DAVIS 2016
(using extra training data)
UniInst: Unique Representation for End-to-End Instance Segmentation
Existing instance segmentation methods have achieved impressive performance but still suffer from a common dilemma: redundant representations (e. g., multiple boxes, grids, and anchor points) are inferred for one instance, which leads to multiple duplicated predictions.
FReeNet: Multi-Identity Face Reenactment
This paper presents a novel multi-identity face reenactment framework, named FReeNet, to transfer facial expressions from an arbitrary source face to a target face with a shared model.
Class-Aware Contrastive Semi-Supervised Learning
Pseudo-label-based semi-supervised learning (SSL) has achieved great success on raw data utilization.
APB2Face: Audio-guided face reenactment with auxiliary pose and blink signals
Audio-guided face reenactment aims at generating photorealistic faces using audio information while maintaining the same facial movement as when speaking to a real person.
APB2FaceV2: Real-Time Audio-Guided Multi-Face Reenactment
Audio-guided face reenactment aims to generate a photorealistic face that has matched facial expression with the input audio.
Real3D-AD: A Dataset of Point Cloud Anomaly Detection
High-precision point cloud anomaly detection is the gold standard for identifying the defects of advancing machining and precision manufacturing.
IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing
We realize that the lack of a uniform IM benchmark is hindering the development and usage of IAD methods in real-world applications.
CRAFT: Cross-Attentional Flow Transformer for Robust Optical Flow
This is mainly because the correlation volume, the basis of pixel matching, is computed as the dot product of the convolutional features of the two images.
Ranked #9 on
Optical Flow Estimation
on KITTI 2015 (train)
DTVNet: Dynamic Time-lapse Video Generation via Single Still Image
The proposed DTVNet consists of two submodules: \emph{Optical Flow Encoder} (OFE) and \emph{Dynamic Video Generator} (DVG).
Universal Segmentation at Arbitrary Granularity with Language Instruction
This paper aims to achieve universal segmentation of arbitrary semantic level.
Ranked #1 on
Referring Expression Segmentation
on RefCOCOg-test
(using extra training data)
A Generalist FaceX via Learning Unified Facial Representation
This work presents FaceX framework, a novel facial generalist model capable of handling diverse facial tasks simultaneously.
End-to-End Beam Retrieval for Multi-Hop Question Answering
This approach models the multi-hop retrieval process in an end-to-end manner by jointly optimizing an encoder and two classification heads across all hops.
Ranked #1 on
Question Answering
on HotpotQA
RICO: Regularizing the Unobservable for Indoor Compositional Reconstruction
Recently, neural implicit surfaces have become popular for multi-view reconstruction.
Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detection
Following this spirit, this paper explores plain ViT architecture for MUAD.
Learning Feature Inversion for Multi-class Anomaly Detection under General-purpose COCO-AD Benchmark
Moreover, current metrics such as AU-ROC have nearly reached saturation on simple datasets, which prevents a comprehensive evaluation of different methods.
Searching Parameterized AP Loss for Object Detection
In this paper, we propose Parameterized AP Loss, where parameterized functions are introduced to substitute the non-differentiable components in the AP calculation.
Diverse Data Augmentation with Diffusions for Effective Test-time Prompt Tuning
In this paper, we focus on a particular setting of learning adaptive prompts on the fly for each test sample from an unseen new domain, which is known as test-time prompt tuning (TPT).
Towards Reliable Medical Image Segmentation by utilizing Evidential Calibrated Uncertainty
DEviS not only enhances the calibration and robustness of baseline segmentation accuracy but also provides high-efficiency uncertainty estimation for reliable predictions.
A Survey of Visual Sensory Anomaly Detection
In this survey, we are the first one to provide a comprehensive review of visual sensory AD and category into three levels according to the form of anomalies.
Unsupervised Continual Anomaly Detection with Contrastively-learned Prompt
Unsupervised Anomaly Detection (UAD) with incremental training is crucial in industrial manufacturing, as unpredictable defects make obtaining sufficient labeled data infeasible.
CoMAE: A Multi-factor Hierarchical Framework for Empathetic Response Generation
However, existing methods for empathetic response generation usually either consider only one empathy factor or ignore the hierarchical relationships between different factors, leading to a weak ability of empathy modeling.
Guide Local Feature Matching by Overlap Estimation
Local image feature matching under large appearance, viewpoint, and distance changes is challenging yet important.
Sentence-level Prompts Benefit Composed Image Retrieval
Composed image retrieval (CIR) is the task of retrieving specific images by using a query that involves both a reference image and a relative caption.
Ranked #1 on
Image Retrieval
on CIRR
OverlapNetVLAD: A Coarse-to-Fine Framework for LiDAR-based Place Recognition
Place recognition is a challenging yet crucial task in robotics.
Learning Federated Visual Prompt in Null Space for MRI Reconstruction
Federated Magnetic Resonance Imaging (MRI) reconstruction enables multiple hospitals to collaborate distributedly without aggregating local data, thereby protecting patient privacy.
Towards Persona-Based Empathetic Conversational Models
To this end, we propose a new task towards persona-based empathetic conversations and present the first empirical study on the impact of persona on empathetic responding.
Adaptive Assignment for Geometry Aware Local Feature Matching
The detector-free feature matching approaches are currently attracting great attention thanks to their excellent performance.
Bridging the Gap: A Unified Video Comprehension Framework for Moment Retrieval and Highlight Detection
Video Moment Retrieval (MR) and Highlight Detection (HD) have attracted significant attention due to the growing demand for video analysis.
Ranked #1 on
Highlight Detection
on YouTube Highlights
NeRF-Loc: Visual Localization with Conditional Neural Radiance Field
We propose a novel visual re-localization method based on direct matching between the implicit 3D descriptors and the 2D image with transformer.
Unsupervised Representation Learning for Time Series: A Review
However, there is a lack of systematic analysis of unsupervised representation learning approaches for time series.
Can Large Language Models Empower Molecular Property Prediction?
Molecular property prediction has gained significant attention due to its transformative potential in multiple scientific disciplines.
SelfCF: A Simple Framework for Self-supervised Collaborative Filtering
Collaborative filtering (CF) is widely used to learn informative latent representations of users and items from observed interactions.
E-NeRV: Expedite Neural Video Representation with Disentangled Spatial-Temporal Context
However, the redundant parameters within the network structure can cause a large model size when scaling up for desirable performance.
Ranked #4 on
Video Reconstruction
on UVG
Layer-refined Graph Convolutional Networks for Recommendation
Specifically, these models usually aggregate all layer embeddings for node updating and achieve their best recommendation performance within a few layers because of over-smoothing.
Extended Feature Pyramid Network for Small Object Detection
Small object detection remains an unsolved challenge because it is hard to extract information of small objects with only a few pixels.
Omni-frequency Channel-selection Representations for Unsupervised Anomaly Detection
Density-based and classification-based methods have ruled unsupervised anomaly detection in recent years, while reconstruction-based methods are rarely mentioned for the poor reconstruction ability and low performance.
Ranked #39 on
Anomaly Detection
on MVTec AD
MaxQ: Multi-Axis Query for N:M Sparsity Network
Meanwhile, a sparsity strategy that gradually increases the percentage of N:M weight blocks is applied, which allows the network to heal from the pruning-induced damage progressively.
Action Semantics Network: Considering the Effects of Actions in Multiagent Systems
ASN characterizes different actions' influence on other agents using neural networks based on the action semantics between them.
TransVOS: Video Object Segmentation with Transformers
Recently, Space-Time Memory Network (STM) based methods have achieved state-of-the-art performance in semi-supervised video object segmentation (VOS).
EATFormer: Improving Vision Transformer Inspired by Evolutionary Algorithm
Motivated by biological evolution, this paper explains the rationality of Vision Transformer by analogy with the proven practical Evolutionary Algorithm (EA) and derives that both have consistent mathematical formulation.
SoftPatch: Unsupervised Anomaly Detection with Noisy Data
Noise discriminators are utilized to generate outlier scores for patch-level noise elimination before coreset construction.
Go Wider Instead of Deeper
To achieve better performance with fewer trainable parameters, recent methods are proposed to go shallower by parameter sharing or model compressing along with the depth.
Ranked #663 on
Image Classification
on ImageNet
Towards Efficient and Scalable Sharpness-Aware Minimization
Recently, Sharpness-Aware Minimization (SAM), which connects the geometry of the loss landscape and generalization, has demonstrated significant performance boosts on training large-scale models such as vision transformers.
Open-Vocabulary Segmentation with Semantic-Assisted Calibration
We attribute this to the in-vocabulary embedding and domain-biased CLIP prediction.
SOC: Semantic-Assisted Object Cluster for Referring Video Object Segmentation
To address this issue, we propose Semantic-assisted Object Cluster (SOC), which aggregates video content and textual guidance for unified temporal modeling and cross-modal alignment.
Ranked #2 on
Referring Expression Segmentation
on A2D Sentences
(using extra training data)
MHCCL: Masked Hierarchical Cluster-Wise Contrastive Learning for Multivariate Time Series
Learning semantic-rich representations from raw unlabeled time series data is critical for downstream tasks such as classification and forecasting.
SSC-RS: Elevate LiDAR Semantic Scene Completion with Representation Separation and BEV Fusion
However, how to effectively exploit the relationships between the semantic context in semantic segmentation and geometric structure in scene completion remains under exploration.
Semantic Segmentation-assisted Scene Completion for LiDAR Point Clouds
Specifically, the network takes a raw point cloud as input, and merges the features from the segmentation branch into the completion branch hierarchically to provide semantic information.
Ranked #4 on
3D Semantic Scene Completion
on SemanticKITTI
Adaptive Multi-Modalities Fusion in Sequential Recommendation Systems
We propose a graph-based approach (named MMSR) to fuse modality features in an adaptive order, enabling each modality to prioritize either its inherent sequential nature or its interplay with other modalities.
PANet: LiDAR Panoptic Segmentation with Sparse Instance Proposal and Aggregation
Firstly, we propose a non-learning Sparse Instance Proposal (SIP) module with the ``sampling-shifting-grouping" scheme to directly group thing points into instances from the raw point cloud efficiently.
Unfolding Once is Enough: A Deployment-Friendly Transformer Unit for Super-Resolution
Since the high resolution of intermediate features in SISR models increases memory and computational requirements, efficient SISR transformers are more favored.
SUBP: Soft Uniform Block Pruning for 1xN Sparse CNNs Multithreading Acceleration
To overcome them, this paper proposes a novel \emph{\textbf{S}oft \textbf{U}niform \textbf{B}lock \textbf{P}runing} (SUBP) approach to train a uniform 1$\times$N sparse structured network from scratch.
GPT-4V-AD: Exploring Grounding Potential of VQA-oriented GPT-4V for Zero-shot Anomaly Detection
Large Multimodal Model (LMM) GPT-4V(ision) endows GPT-4 with visual grounding capabilities, making it possible to handle certain tasks through the Visual Question Answering (VQA) paradigm.
Towards Tracing Trustworthiness Dynamics: Revisiting Pre-training Period of Large Language Models
This research provides an initial exploration of trustworthiness modeling during LLM pre-training, seeking to unveil new insights and spur further developments in the field.
Camera-based 3D Semantic Scene Completion with Sparse Guidance Network
Semantic scene completion (SSC) aims to predict the semantic occupancy of each voxel in the entire 3D scene from limited observations, which is an emerging and critical task for autonomous driving.
SDSTrack: Self-Distillation Symmetric Adapter Learning for Multi-Modal Visual Object Tracking
Multimodal Visual Object Tracking (VOT) has recently gained significant attention due to its robustness.
TryOn-Adapter: Efficient Fine-Grained Clothing Identity Adaptation for High-Fidelity Virtual Try-On
However, the clothing identity uncontrollability and training inefficiency of existing diffusion-based methods, which struggle to maintain the identity even with full parameter training, are significant limitations that hinder the widespread applications.
Analogous to Evolutionary Algorithm: Designing a Unified Sequence Model
Inspired by biological evolution, we explain the rationality of Vision Transformer by analogy with the proven practical Evolutionary Algorithm (EA) and derive that both of them have consistent mathematical representation.
Learning Prompt with Distribution-Based Feature Replay for Few-Shot Class-Incremental Learning
When progressing to a new session, pseudo-features are sampled from old-class distributions combined with training images of the current session to optimize the prompt, thus enabling the model to learn new knowledge while retaining old knowledge.
Very Long Term Field of View Prediction for 360-degree Video Streaming
In this work, we treat the FoV prediction as a sequence learning problem, and propose to predict the target user's future FoV not only based on the user's own past FoV center trajectory but also other users' future FoV locations.
Reference Twice: A Simple and Unified Baseline for Few-Shot Instance Segmentation
In this work, we explore a simple yet unified solution for FSIS as well as its incremental variants, and introduce a new framework named Reference Twice (RefT) to fully explore the relationship between support/query features based on a Transformer-like framework.
RIDERS: Radar-Infrared Depth Estimation for Robust Sensing
Dense depth recovery is crucial in autonomous driving, serving as a foundational element for obstacle avoidance, 3D object detection, and local path planning.
AutoTimes: Autoregressive Time Series Forecasters via Large Language Models
Foundation models of time series have not been fully developed due to the limited availability of large-scale time series and the underexploration of scalable pre-training.
Joint Learning Content and Degradation Aware Feature for Blind Super-Resolution
Although some Unsupervised Degradation Prediction (UDP) methods are proposed to bypass this problem, the \textit{inconsistency} between degradation embedding and SR feature is still challenging.
Beyond Prototypes: Semantic Anchor Regularization for Better Representation Learning
By pulling the learned features to these semantic anchors, several advantages can be attained: 1) the intra-class compactness and naturally inter-class separability, 2) induced bias or errors from feature learning can be avoided, and 3) robustness to the long-tailed problem.
Keyword-Guided Neural Conversational Model
We study the problem of imposing conversational goals/keywords on open-domain conversational agents, where the agent is required to lead the conversation to a target keyword smoothly and fast.
A Unified BEV Model for Joint Learning of 3D Local Features and Overlap Estimation
For overlap detection, a cross-attention module is applied for interacting contextual information of input point clouds, followed by a classification head to estimate the overlapping region.
Global Knowledge Calibration for Fast Open-Vocabulary Segmentation
Recent advancements in pre-trained vision-language models, such as CLIP, have enabled the segmentation of arbitrary concepts solely from textual inputs, a process commonly referred to as open-vocabulary semantic segmentation (OVS).
Knowledge Distillation
Open Vocabulary Semantic Segmentation
+4
1st Place Solution for 5th LSVOS Challenge: Referring Video Object Segmentation
The recent transformer-based models have dominated the Referring Video Object Segmentation (RVOS) task due to the superior performance.
Helen: Optimizing CTR Prediction Models with Frequency-wise Hessian Eigenvalue Regularization
We explore the typical data characteristics and optimization statistics of CTR prediction, revealing a strong positive correlation between the top hessian eigenvalue and feature frequency.
DLPAlign: A Deep Learning based Progressive Alignment Method for Multiple Protein Sequences
This paper proposed a novel and straightforward approach to improve the accuracy of progressive multiple protein sequence alignment method.
Ranked #1 on
Multiple Sequence Alignment
on OXBench
Timer: Transformers for Time Series Analysis at Scale
Continuous progresses have been achieved as the emergence of large language models, exhibiting unprecedented ability in few-shot generalization, scalability, and task generality, which is however absent in time series models.
Robust photon-efficient imaging using a pixel-wise residual shrinkage network
Single-photon light detection and ranging (LiDAR) has been widely applied to 3D imaging in challenging scenarios.
Inductive Graph Transformer for Delivery Time Estimation
Providing accurate estimated time of package delivery on users' purchasing pages for e-commerce platforms is of great importance to their purchasing decisions and post-purchase experiences.
LLMs may Dominate Information Access: Neural Retrievers are Biased Towards LLM-Generated Texts
We refer to this category of biases in neural retrieval models towards the LLM-generated text as the \textbf{source bias}.
APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendation
To this end, we propose a graph-driven framework, named Adaptive and Personalized Graph Learning for Sequential Recommendation (APGL4SR), that incorporates adaptive and personalized global collaborative information into sequential recommendation systems.
VQA4CIR: Boosting Composed Image Retrieval with Visual Question Answering
By feeding the retrieved image and question to the VQA model, one can find the images inconsistent with relative caption when the answer by VQA is inconsistent with the answer in the QA pair.
Variational Quantum Circuits for Quantum State Tomography
We demonstrate our method by performing numerical simulations for the tomography of the ground state of a one-dimensional quantum spin chain, using a variational quantum circuit simulator.
PD-GAN: Adversarial Learning for Personalized Diversity-Promoting Recommendation
This paper proposes Personalized Diversity-promoting GAN (PD-GAN), a novel recommendation model to generate diverse, yet relevant recommendations.
EARL: Informative Knowledge-Grounded Conversation Generation with Entity-Agnostic Representation Learning
Generating informative and appropriate responses is challenging but important for building human-like dialogue systems.
Better "CMOS" Produces Clearer Images: Learning Space-Variant Blur Estimation for Blind Image Super-Resolution
Most of the existing blind image Super-Resolution (SR) methods assume that the blur kernels are space-invariant.
Fair Scratch Tickets: Finding Fair Sparse Networks Without Weight Training
We randomly initialize a dense neural network and find appropriate binary masks for the weights to obtain fair sparse subnetworks without any weight training.
RadarCam-Depth: Radar-Camera Fusion for Depth Estimation with Learned Metric Scale
To circumvent this issue, we learn to augment versatile and robust monocular depth prediction with the dense metric scale induced from sparse and noisy Radar data.
Automated Spectral Kernel Learning
The generalization performance of kernel methods is largely determined by the kernel, but common kernels are stationary thus input-independent and output-independent, that limits their applications on complicated tasks.
Automatic Data Augmentation by Learning the Deterministic Policy
By introducing an unified optimization goal, DeepAugNet intends to combine the data augmentation and the deep model training in an end-to-end training manner which is realized by simultaneously training a hybrid architecture of dueling deep Q-learning algorithm and a surrogate deep model.
Boosting Few-shot Action Recognition with Graph-guided Hybrid Matching
Class prototype construction and matching are core aspects of few-shot action recognition.
Semi-Supervised Learning for Visual Bird's Eye View Semantic Segmentation
In this paper, we present a novel semi-supervised framework for visual BEV semantic segmentation to boost performance by exploiting unlabeled images during the training.
Autonomous Vehicles
Bird's-Eye View Semantic Segmentation
+2
A Learning Framework for n-bit Quantized Neural Networks toward FPGAs
Furthermore, we also design a shift vector processing element (SVPE) array to replace all 16-bit multiplications with SHIFT operations in convolution operation on FPGAs.
Inductive Representation Learning in Temporal Networks via Mining Neighborhood and Community Influences
Therefore, we propose a new inductive network representation learning method called MNCI by mining neighborhood and community influences in temporal networks.
Ridgeless Regression with Random Features
Recent theoretical studies illustrated that kernel ridgeless regression can guarantee good generalization ability without an explicit regularization.
Adaptive Value Decomposition with Greedy Marginal Contribution Computation for Cooperative Multi-Agent Reinforcement Learning
We first illustrate that the proposed value decomposition can consider the complicated interactions among agents and is feasible to learn in large-scale scenarios.
Parameter-Efficient Conversational Recommender System as a Language Processing Task
Conversational recommender systems (CRS) aim to recommend relevant items to users by eliciting user preference through natural language conversation.
Towards Sharp Analysis for Distributed Learning with Random Features
In this paper, using refined proof techniques, we first extend the optimal rates for distributed learning with random features to the non-attainable case.
Deep Domain Adversarial Adaptation for Photon-efficient Imaging
Photon-efficient imaging with the single-photon light detection and ranging (LiDAR) captures the three-dimensional (3D) structure of a scene by only a few detected signal photons per pixel.
Localizing Anatomical Landmarks in Ocular Images using Zoom-In Attentive Networks
Therefore, localization has its unique challenges different from segmentation or detection.
ViG-UNet: Vision Graph Neural Networks for Medical Image Segmentation
Deep neural networks have been widely used in medical image analysis and medical image segmentation is one of the most important tasks.
Feature Lenses: Plug-and-play Neural Modules for Transformation-Invariant Visual Representations
To make CNNs more invariant to transformations, we propose "Feature Lenses", a set of ad-hoc modules that can be easily plugged into a trained model (referred to as the "host model").
Generative Gradient Inversion via Over-Parameterized Networks in Federated Learning
Federated learning has gained recognitions as a secure approach for safeguarding local private data in collaborative learning.
Understanding Fairness Surrogate Functions in Algorithmic Fairness
To mitigate such bias while achieving comparable accuracy, a promising approach is to introduce surrogate functions of the concerned fairness definition and solve a constrained optimization problem.
Nighttime Thermal Infrared Image Colorization with Feedback-based Object Appearance Learning
Extensive experiments illustrate that the proposed FoalGAN is not only effective for appearance learning of small objects, but also outperforms other image translation methods in terms of semantic preservation and edge consistency for the NTIR2DC task.
Learnable Chamfer Distance for Point Cloud Reconstruction
As point clouds are 3D signals with permutation invariance, most existing works train their reconstruction networks by measuring shape differences with the average point-to-point distance between point clouds matched with predefined rules.
LORS: Low-rank Residual Structure for Parameter-Efficient Network Stacking
Deep learning models, particularly those based on transformers, often employ numerous stacked structures, which possess identical architectures and perform similar functions.
Robust Visual SLAM with Point and Line Features
In this paper, we develop a robust efficient visual SLAM system that utilizes heterogeneous point and line features.
Robust Cost-Sensitive Learning for Recommendation with Implicit Feedback
Morever, the proposed algorithm can be scaled up to large-sized datasets after a relaxation.
Large Margin Object Tracking with Circulant Feature Maps
Structured output support vector machine (SVM) based tracking algorithms have shown favorable performance recently.
Real-time 3D Human Tracking for Mobile Robots with Multisensors
An ultrasonic sensor array is employed to provide the range information from the target person to the robot and Gaussian Process Regression is used for partial location estimation (2-D).
RISAS: A Novel Rotation, Illumination, Scale Invariant Appearance and Shape Feature
This paper presents a novel appearance and shape feature, RISAS, which is robust to viewpoint, illumination, scale and rotation variations.
Improved Recurrent Neural Networks for Session-based Recommendations
Recurrent neural networks (RNNs) were recently proposed for the session-based recommendation task.
Robust Object Tracking with a Hierarchical Ensemble Framework
A discriminative model which accounts for the matching degree of local patches is adopted via a bottom ensemble layer, and a generative model which exploits holistic templates is used to search for the object through the middle ensemble layer as well as an adaptive Kalman filter.
Simple and Efficient Learning using Privileged Information
The Support Vector Machine using Privileged Information (SVM+) has been proposed to train a classifier to utilize the additional privileged information that is only available in the training phase but not available in the test phase.
Understand Scene Categories by Objects: A Semantic Regularized Scene Classifier Using Convolutional Neural Networks
As scene images have larger diversity than the iconic object images, it is more challenging for deep learning methods to automatically learn features from scene images with less samples.
Noise Robust IOA/CAS Speech Separation and Recognition System For The Third 'CHIME' Challenge
This paper presents the contribution to the third 'CHiME' speech separation and recognition challenge including both front-end signal processing and back-end speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Place classification with a graph regularized deep neural network model
Furthermore, results show that the features automatically learned from the raw input range data can achieve competitive results to the features constructed based on statistical and geometrical information.
Image Representation Learning Using Graph Regularized Auto-Encoders
We consider the problem of image representation for the tasks of unsupervised learning and semi-supervised learning.
Structure Inference Net: Object Detection Using Scene-Level Context and Instance-Level Relationships
Context is important for accurate visual recognition.
SL$^2$MF: Predicting Synthetic Lethality in Human Cancers via Logistic Matrix Factorization
Moreover, we also incorporate biological knowledge about genes from protein-protein interaction (PPI) data and Gene Ontology (GO).
Adaptive Re-ranking of Deep Feature for Person Re-identification
First, we propose a Deep Feature Fusion (DFF) method to exploit the diverse information embedded in a deep feature.
Dynamic Spatio-temporal Graph-based CNNs for Traffic Prediction
To capture the graph dynamics, we use the graph prediction stream to predict the dynamic graph structures, and the predicted structures are fed into the flow prediction stream.
Constructing Ontology-Based Cancer Treatment Decision Support System with Case-Based Reasoning
Disease Ontology (DO) that pertains to cancer's clinical stages and their corresponding information components is utilized to improve the reasoning ability of a decision support system (DSS).
A Knowledge Graph Based Solution for Entity Discovery and Linking in Open-Domain Questions
In Question Entity Discovery and Linking (QEDL) problem, traditional methods are challenged because multiple entities in one short question are difficult to be discovered entirely and the incomplete information in short text makes entity linking hard to implement.
Approach for Semi-automatic Construction of Anti-infective Drug Ontology Based on Entity Linking
Experiments show that our selected features have achieved a precision rate of 86. 77%, a recall rate of 89. 03% and an F1 score of 87. 89%.
Max-Diversity Distributed Learning: Theory and Algorithms
We study the risk performance of distributed learning for the regularization empirical risk minimization with fast convergence rate, substantially improving the error analysis of the existing divide-and-conquer based distributed learning.
Multi-Class Learning: From Theory to Algorithm
In this paper, we study the generalization performance of multi-class classification and obtain a shaper data-dependent generalization error bound with fast convergence rate, substantially improving the state-of-art bounds in the existing data-dependent generalization analysis.
Efficient Cross-Validation for Semi-Supervised Learning
In this paper, we provide a method to approximate the CV for manifold regularization based on a notion of robust statistics, called Bouligand influence function (BIF).
Multi-Scale Quasi-RNN for Next Item Recommendation
To this end, architectural inductive biases such as Markov-Chains, Recurrent models, Convolutional networks and many others have demonstrated reasonable success on this task.
Diversity-Promoting Deep Reinforcement Learning for Interactive Recommendation
Interactive recommendation that models the explicit interactions between users and the recommender system has attracted a lot of research attentions in recent years.
An Evaluation of Transfer Learning for Classifying Sales Engagement Emails at Large Scale
Our results show that fine-tuning of the BERT model outperforms with as few as 300 labeled samples, but underperforms with fewer than 300 labeled samples, relative to all the feature-based approaches using different embeddings.
Recent Advances in Diversified Recommendation
With the rapid development of recommender systems, accuracy is no longer the only golden criterion for evaluating whether the recommendation results are satisfying or not.
Audio2Face: Generating Speech/Face Animation from Single Audio with Attention-Based Bidirectional LSTM Networks
We propose an end to end deep learning approach for generating real-time facial animation from just audio.
Converse Attention Knowledge Transfer for Low-Resource Named Entity Recognition
In the process of translation, CAN obtain the attention matrices that align the two languages.
Low Resource Named Entity Recognition
named-entity-recognition
+4
PoseConvGRU: A Monocular Approach for Visual Ego-motion Estimation by Learning
The feature-encoding module encodes the short-term motion feature in an image pair, while the memory-propagating module captures the long-term motion feature in the consecutive image pairs.
Bandit Learning for Diversified Interactive Recommendation
Interactive recommender systems that enable the interactions between users and the recommender system have attracted increasing research attentions.
Multi-Instance Multi-Scale CNN for Medical Image Classification
Deep learning for medical image classification faces three major challenges: 1) the number of annotated medical images for training are usually small; 2) regions of interest (ROIs) are relatively small with unclear boundaries in the whole medical images, and may appear in arbitrary positions across the x, y (and also z in 3D images) dimensions.
Face-to-Parameter Translation for Game Character Auto-Creation
Character customization system is an important component in Role-Playing Games (RPGs), where players are allowed to edit the facial appearance of their in-game characters with their own preferences rather than using default templates.
PASS3D: Precise and Accelerated Semantic Segmentation for 3D Point Cloud
In this paper, we propose PASS3D to achieve point-wise semantic segmentation for 3D point cloud.
From Few to More: Large-scale Dynamic Multiagent Curriculum Learning
In this paper, we design a novel Dynamic Multiagent Curriculum Learning (DyMA-CL) to solve large-scale problems by starting from learning on a multiagent scenario with a small size and progressively increasing the number of agents.
Pose Estimation for Ground Robots: On Manifold Representation, Integration, Re-Parameterization, and Optimization
In this paper, we focus on motion estimation dedicated for non-holonomic ground robots, by probabilistically fusing measurements from the wheel odometer and exteroceptive sensors.
Semi-supervised Vector-valued Learning: Improved Bounds and Algorithms
Vector-valued learning, where the output space admits a vector-valued structure, is an important problem that covers a broad family of important domains, e. g. multi-task learning and transfer learning.
Weighted Distributed Differential Privacy ERM: Convex and Non-convex
By detailed theoretical analysis, we show that in distributed setting, the noise bound and the excess empirical risk bound can be improved by considering different weights held by multiple parties.
Two Generator Game: Learning to Sample via Linear Goodness-of-Fit Test
DEAN can be interpreted as a GOF game between two generative networks, where one explicit generative network learns an energy-based distribution that fits the real data, and the other implicit generative network is trained by minimizing a GOF test statistic between the energy-based distribution and the generated data, such that the underlying distribution of the generated data is close to the energy-based distribution.
Visual-Inertial Localization for Skid-Steering Robots with Kinematic Constraints
While visual localization or SLAM has witnessed great progress in past decades, when deploying it on a mobile robot in practice, few works have explicitly considered the kinematic (or dynamic) constraints of the real robotic system when designing state estimators.
Multi-Agent Game Abstraction via Graph Attention Neural Network
Traditional methods attempt to use pre-defined rules to capture the interaction relationship between agents.
Efficient Robotic Task Generalization Using Deep Model Fusion Reinforcement Learning
The proposed method is able to efficiently generalize the previously learned task by model fusion to solve the environment adaptation problem.
RoboCoDraw: Robotic Avatar Drawing with GAN-based Style Transfer and Time-efficient Path Optimization
The proposed RoboCoDraw system takes a real human face image as input, converts it to a stylized avatar, then draws it with a robotic arm.
Robotics Graphics
AttentionAnatomy: A unified framework for whole-body organs at risk segmentation using multiple partially annotated datasets
Organs-at-risk (OAR) delineation in computed tomography (CT) is an important step in Radiation Therapy (RT) planning.
Data Heterogeneity Differential Privacy: From Theory to Algorithm
Traditionally, the random noise is equally injected when training with different data instances in the field of differential privacy (DP).
Input Perturbation: A New Paradigm between Central and Local Differential Privacy
By adding noise to the original training data and training with the `perturbed data', we achieve ($\epsilon$,$\delta$)-differential privacy on the final model, along with some kind of privacy on the original data.
Convolutional Spectral Kernel Learning
Recently, non-stationary spectral kernels have drawn much attention, owing to its powerful feature representation ability in revealing long-range correlations and input-dependent characteristics.
Theoretical Analysis of Divide-and-Conquer ERM: Beyond Square Loss and RKHS
However, the studies on learning theory for general loss functions and hypothesis spaces remain limited.
Nearly Optimal Clustering Risk Bounds for Kernel K-Means
In this paper, we study the statistical properties of kernel $k$-means and obtain a nearly optimal excess clustering risk bound, substantially improving the state-of-art bounds in the existing clustering risk analyses.
Propagating Asymptotic-Estimated Gradients for Low Bitwidth Quantized Neural Networks
Meanwhile, we propose a M-bit Inputs and N-bit Weights Network (MINW-Net) trained by AQE, a quantized neural network with 1-3 bits weights and activations.
Realistic Face Reenactment via Self-Supervised Disentangling of Identity and Pose
On the one hand, we adopt the deforming autoencoder to disentangle identity and pose representations.
Learning Hierarchical Review Graph Representations for Recommendation
Therefore, they may not be effective in capturing the global dependency between words, and tend to be easily biased by noise review information.
Contextualized Graph Attention Network for Recommendation with Item Knowledge Graph
Graph neural networks (GNN) have recently been applied to exploit knowledge graph (KG) for recommendation.
Hierarchical and Efficient Learning for Person Re-Identification
Recent works in the person re-identification task mainly focus on the model accuracy while ignore factors related to the efficiency, e. g. model size and latency, which are critical for practical application.
Neural Architecture Optimization with Graph VAE
The encoder and the decoder belong to a graph VAE, mapping architectures between continuous representations and network architectures.
Dive Deeper Into Box for Object Detection
Anchor free methods have defined the new frontier in state-of-the-art object detection researches where accurate bounding box estimation is the key to the success of these methods.
LIC-Fusion 2.0: LiDAR-Inertial-Camera Odometry with Sliding-Window Plane-Feature Tracking
Only the tracked planar points belonging to the same plane will be used for plane initialization, which makes the plane extraction efficient and robust.
Robotics
LIC-Fusion: LiDAR-Inertial-Camera Odometry
This paper presents a tightly-coupled multi-sensor fusion algorithm termed LiDAR-inertial-camera fusion (LIC-Fusion), which efficiently fuses IMU measurements, sparse visual features, and extracted LiDAR points.
Robotics
Optimizing Quantized Neural Networks with Natural Gradient
Quantized Neural Networks (QNNs) have achieved an enormous step in improving computational efficiency, making it possible to deploy large models to mobile and miniaturized devices.
Effective Distributed Learning with Random Features: Improved Bounds and Algorithms
In this paper, we study the statistical properties of distributed kernel ridge regression together with random features (DKRR-RF), and obtain optimal generalization bounds under the basic setting, which can substantially relax the restriction on the number of local machines in the existing state-of-art bounds.
Fast Estimation for Privacy and Utility in Differentially Private Machine Learning
In this paper, we deduce the influence of $\epsilon$ on utility private learning models through strict mathematical derivation, and propose a novel approximate approach for estimating the utility of any $\epsilon$ value.
Collision-free Trajectory Planning for Autonomous Surface Vehicle
We apply a numerical optimization method in the back-end to generate the trajectory.
Robotics
F-Siamese Tracker: A Frustum-based Double Siamese Network for 3D Single Object Tracking
A main challenge in 3D single object tracking is how to reduce search space for generating appropriate 3D candidates.
Pre-training Graph Transformer with Multimodal Side Information for Recommendation
Side information of items, e. g., images and text description, has shown to be effective in contributing to accurate recommendations.
Moving Forward in Formation: A Decentralized Hierarchical Learning Approach to Multi-Agent Moving Together
We also deploy and validate our method in a real world scenario.
Robotics Multiagent Systems
HILONet: Hierarchical Imitation Learning from Non-Aligned Observations
In this work, we propose a new imitation learning approach called Hierarchical Imitation Learning from Observation(HILONet), which adopts a hierarchical structure to choose feasible sub-goals from demonstrated observations dynamically.
Fine Perceptive GANs for Brain MR Image Super-Resolution in Wavelet Domain
Specifically, FP-GANs firstly divides an MR image into low-frequency global approximation and high-frequency anatomical texture in wavelet domain.
Frequency Response Study on the ERCOT under High Photovoltaic (PV) Penetration Conditions
Solar photovoltaic (PV) generation is growing rapidly around the world.
FlowMOT: 3D Multi-Object Tracking by Scene Flow Association
Most end-to-end Multi-Object Tracking (MOT) methods face the problems of low accuracy and poor generalization ability.
FCFR-Net: Feature Fusion based Coarse-to-Fine Residual Learning for Depth Completion
Then, a refined depth map is further obtained using a residual learning strategy in the coarse-to-fine stage with a coarse depth map and color image as input.
Spatial Context-Aware Self-Attention Model For Multi-Organ Segmentation
State-of-the-art CNN segmentation models apply either 2D or 3D convolutions on input images, with pros and cons associated with each method: 2D convolution is fast, less memory-intensive but inadequate for extracting 3D contextual information from volumetric images, while the opposite is true for 3D convolution.
CodeVIO: Visual-Inertial Odometry with Learned Optimizable Dense Depth
In this work, we present a lightweight, tightly-coupled deep depth network and visual-inertial odometry (VIO) system, which can provide accurate state estimates and dense depth maps of the immediate surroundings.
