Search Results for author:
Found 55 papers, 22 papers with code
Non-stationary Projection-free Online Learning with Dynamic and Adaptive Regret Guarantees
Specifically, we first provide a novel dynamic regret analysis for an existing projection-free method named $\text{BOGD}_\text{IP}$, and establish an $\mathcal{O}(T^{3/4}(1+P_T))$ dynamic regret bound, where $P_T$ denotes the path-length of the comparator sequence.
Unsupervised Sentence Representation Learning with Frequency-induced Adversarial Tuning and Incomplete Sentence Filtering
To solve the problems, we fine-tune PLMs by leveraging the frequency information of words and propose a novel USRL framework, namely Sentence Representation Learning with Frequency-induced Adversarial tuning and Incomplete sentence filtering (SLT-FAI).
Unleashing Infinite-Length Input Capacity for Large-scale Language Models with Self-Controlled Memory System
To address this limitation, we propose the Self-Controlled Memory (SCM) system to unleash infinite-length input capacity for large-scale language models.
DAA: A Delta Age AdaIN operation for age estimation via binary code transformer
Inspired by the transfer learning, we designed the Delta Age AdaIN (DAA) operation to obtain the feature difference with each age, which obtains the style map of each age through the learned values representing the mean and standard deviation.
Decoupling Skill Learning from Robotic Control for Generalizable Object Manipulation
Our experiments on manipulating complex articulated objects show that the proposed approach is more generalizable to unseen objects with large intra-class variations, outperforming previous approaches.
GrowSP: Unsupervised Semantic Segmentation of 3D Point Clouds
Our method consists of three major components, 1) the feature extractor to learn per-point features from input point clouds, 2) the superpoint constructor to progressively grow the sizes of superpoints, and 3) the semantic primitive clustering module to group superpoints into semantic elements for the final semantic segmentation.
Towards Robustness of Text-to-SQL Models Against Natural and Realistic Adversarial Table Perturbation
The robustness of Text-to-SQL parsers against adversarial perturbations plays a crucial role in delivering highly reliable applications.
Know What I don't Know: Handling Ambiguous and Unanswerable Questions for Text-to-SQL
Following this study, we propose a simple yet effective counterfactual example generation approach that automatically produces ambiguous and unanswerable text-to-SQL examples.
DM-NeRF: 3D Scene Geometry Decomposition and Manipulation from 2D Images
In this paper, we study the problem of 3D scene geometry decomposition and manipulation from 2D views.
Sparse Attentive Memory Network for Click-through Rate Prediction with Long Sequences
SAM supports efficient training and real-time inference for user behavior sequences with lengths on the scale of thousands.
Learning an Adaptive Forwarding Strategy for Mobile Wireless Networks: Resource Usage vs. Latency
Designing effective routing strategies for mobile wireless networks is challenging due to the need to seamlessly adapt routing behavior to spatially diverse and temporally changing network conditions.
Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Object Detection
There are two critical sensors for 3D perception in autonomous driving, the camera and the LiDAR.
BEVFusion: A Simple and Robust LiDAR-Camera Fusion Framework
Fusing the camera and LiDAR information has become a de-facto standard for 3D object detection tasks.
Knowledge Distillation via the Target-aware Transformer
To this end, we propose a novel one-to-all spatial matching knowledge distillation approach.
RangeUDF: Semantic Surface Reconstruction from 3D Point Clouds
We present RangeUDF, a new implicit representation based framework to recover the geometry and semantics of continuous 3D scene surfaces from point clouds.
A Contrastive Cross-Channel Data Augmentation Framework for Aspect-based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task, which focuses on detecting the sentiment polarity towards the aspect in a sentence.
Aspect-Based Sentiment Analysis (ABSA)
Contrastive Learning
+2
No Pain, Big Gain: Classify Dynamic Point Cloud Sequences with Static Models by Fitting Feature-level Space-time Surfaces
Scene flow is a powerful tool for capturing the motion field of 3D point clouds.
Exploring Inter-Channel Correlation for Diversity-preserved KnowledgeDistillation
Extensive experiments on two vision tasks, includ-ing ImageNet classification and Pascal VOC segmentation, demonstrate the superiority of our ICKD, which consis-tently outperforms many existing methods, advancing thestate-of-the-art in the fields of Knowledge Distillation.
Contrastive Instruction-Trajectory Learning for Vision-Language Navigation
The vision-language navigation (VLN) task requires an agent to reach a target with the guidance of natural language instruction.
Weakly Supervised Prototype Topic Model with Discriminative Seed Words: Modifying the Category Prior by Self-exploring Supervised Signals
The recent generative dataless methods construct document-specific category priors by using seed word occurrences only, however, such category priors often contain very limited and even noisy supervised signals.
CubeLearn: End-to-end Learning for Human Motion Recognition from Raw mmWave Radar Signals
To avoid the drawbacks of conventional DFT pre-processing, we propose a learnable pre-processing module, named CubeLearn, to directly extract features from raw radar signal and build an end-to-end deep neural network for mmWave FMCW radar motion recognition applications.
DMML-Net: Deep Metametric Learning for Few-Shot Geographic Object Segmentation in Remote Sensing Imagery
After metatraining, DMML-Net can be applied for the few-shot segmentation tasks of novel geographic objects with only a few gradient steps on the small training set.
A novel class of fixed-time consensus protocols for multi-agent systems with simple dynamics
This paper investigates the fixed-time consensus problem for a class of multi-agent systems with simple dynamics.
Iterative Memory Network for Long Sequential User Behavior Modeling in Recommender Systems
Extensive empirical studies show that our method outperforms various state-of-the-art sequential modeling methods on both public and industrial datasets for long sequential user behavior modeling.
DS-Net++: Dynamic Weight Slicing for Efficient Inference in CNNs and Transformers
Dynamic networks have shown their promising capability in reducing theoretical computation complexity by adapting their architectures to the input during inference.
An AI-assisted Economic Model of Endogenous Mobility and Infectious Diseases: The Case of COVID-19 in the United States
We build a deep-learning-based SEIR-AIM model integrating the classical Susceptible-Exposed-Infectious-Removed epidemiology model with forecast modules of infection, community mobility, and unemployment.
Graph-based Thermal-Inertial SLAM with Probabilistic Neural Networks
Simultaneous Localization and Mapping (SLAM) system typically employ vision-based sensors to observe the surrounding environment.
Dynamic Slimmable Network
Here, we explore a dynamic network slimming regime, named Dynamic Slimmable Network (DS-Net), which aims to achieve good hardware-efficiency via dynamically adjusting filter numbers of networks at test time with respect to different inputs, while keeping filters stored statically and contiguously in hardware to prevent the extra burden.
BossNAS: Exploring Hybrid CNN-transformers with Block-wisely Self-supervised Neural Architecture Search
In this work, we present Block-wisely Self-supervised Neural Architecture Search (BossNAS), an unsupervised NAS method that addresses the problem of inaccurate architecture rating caused by large weight-sharing space and biased supervision in previous methods.
Ranked #1 on
Neural Architecture Search
on ImageNet
(Top 5 Accuracy metric)
P2-Net: Joint Description and Detection of Local Features for Pixel and Point Matching
Accurately describing and detecting 2D and 3D keypoints is crucial to establishing correspondences across images and point clouds.
Exploring Inter-Channel Correlation for Diversity-Preserved Knowledge Distillation
Extensive experiments on two vision tasks, including ImageNet classification and Pascal VOC segmentation, demonstrate the superiority of our ICKD, which consistently outperforms many existing methods, advancing the state-of-the-art in the fields of Knowledge Distillation.
Ranked #7 on
Knowledge Distillation
on ImageNet
Relational Deep Reinforcement Learning for Routing in Wireless Networks
While routing in wireless networks has been studied extensively, existing protocols are typically designed for a specific set of network conditions and so cannot accommodate any drastic changes in those conditions.
Photonic Floquet time crystals
The public and scientists constantly have different perspectives.
Quantum Physics Mesoscale and Nanoscale Physics Other Condensed Matter Optics
PSF-LO: Parameterized Semantic Features Based Lidar Odometry
Lidar odometry (LO) is a key technology in numerous reliable and accurate localization and mapping systems of autonomous driving.
A Survey on Deep Learning for Localization and Mapping: Towards the Age of Spatial Machine Intelligence
Deep learning based localization and mapping has recently attracted significant attention.
VMLoc: Variational Fusion For Learning-Based Multimodal Camera Localization
We conjecture that this is because of the naive approaches to feature space fusion through summation or concatenation which do not take into account the different strengths of each modality.
PointLoc: Deep Pose Regressor for LiDAR Point Cloud Localization
In this paper, we present a novel end-to-end learning-based LiDAR relocalization framework, termed PointLoc, which infers 6-DoF poses directly using only a single point cloud as input, without requiring a pre-built map.
Robotics
Learning Selective Sensor Fusion for States Estimation
By integrating the observations from different sensors, these mobile agents are able to perceive the environment and estimate system states, e. g. locations and orientations.
ATCSpeech: a multilingual pilot-controller speech corpus from real Air Traffic Control environment
To our best knowledge, this is the first work that aims at building a real and multilingual ASR corpus for the air traffic related research.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Chinese Spelling Error Detection Using a Fusion Lattice LSTM
Spelling error detection serves as a crucial preprocessing in many natural language processing applications.
See Through Smoke: Robust Indoor Mapping with Low-cost mmWave Radar
This paper presents the design, implementation and evaluation of milliMap, a single-chip millimetre wave (mmWave) radar based indoor mapping system targetted towards low-visibility environments to assist in emergency response.
AtLoc: Attention Guided Camera Localization
Deep learning has achieved impressive results in camera localization, but current single-image techniques typically suffer from a lack of robustness, leading to large outliers.
Ranked #2 on
Visual Localization
on Oxford RobotCar Full
DynaNet: Neural Kalman Dynamical Model for Motion Estimation and Prediction
In addition we show how DynaNet can indicate failures through investigation of properties such as the rate of innovation (Kalman Gain).
X-LineNet: Detecting Aircraft in Remote Sensing Images by a pair of Intersecting Line Segments
Motivated by the development of deep convolution neural networks (DCNNs), tremendous progress has been gained in the field of aircraft detection.
Accelerating Primal Solution Findings for Mixed Integer Programs Based on Solution Prediction
In many applications, a similar MIP model is solved on a regular basis, maintaining remarkable similarities in model structures and solution appearances but differing in formulation coefficients.
Learning Hierarchical Features for Visual Object Tracking with Recursive Neural Networks
First, we learn RNN parameters to discriminate between the target object and background in the first frame of a test sequence.
Joint Calibration of Panoramic Camera and Lidar Based on Supervised Learning
In view of contemporary panoramic camera-laser scanner system, the traditional calibration method is not suitable for panoramic cameras whose imaging model is extremely nonlinear.
Joint Learning of Siamese CNNs and Temporally Constrained Metrics for Tracklet Association
Then the Siamese CNN and temporally constrained metrics are jointly learned online to construct the appearance-based tracklet affinity models.
Scene Parsing with Integration of Parametric and Non-parametric Models
The local beliefs of pixels are output by CNN-Ensemble.
Tracklet Association by Online Target-Specific Metric Learning and Coherent Dynamics Estimation
In this paper, we present a novel method based on online target-specific metric learning and coherent dynamics estimation for tracklet (track fragment) association by network flow optimization in long-term multi-person tracking.
Learning Contextual Dependencies with Convolutional Hierarchical Recurrent Neural Networks
In this manuscript, we integrate CNNs with HRNNs, and develop end-to-end convolutional hierarchical recurrent neural networks (C-HRNNs).
DAG-Recurrent Neural Networks For Scene Labeling
In image labeling, local representations for image units are usually generated from their surrounding image patches, thus long-range contextual information is not effectively encoded.
Ranked #21 on
Semantic Segmentation
on COCO-Stuff test
Integrating Parametric and Non-Parametric Models For Scene Labeling
We adopt Convolutional Neural Networks (CNN) as our parametric model to learn discriminative features and classifiers for local patch classification.
Tracklet Association with Online Target-Specific Metric Learning
In our method, target-specific similarity metrics are learned, which give rise to the appearance-based models used in the tracklet affinity estimation.
Radial basis function process neural network training based on generalized frechet distance and GA-SA hybrid strategy
For learning problem of Radial Basis Function Process Neural Network (RBF-PNN), an optimization training method based on GA combined with SA is proposed in this paper.
