Search Results for author:
Found 65 papers, 28 papers with code
A Timestep aware Sentence Embedding and Acme Coverage for Brief but Informative Title Generation
The title generation task that summarizes article content in recapitulatory words relies heavily on utilizing the corresponding key context.
Mixed Neural Voxels for Fast Multi-view Video Synthesis
In this paper, we present a novel method named MixVoxels to better represent the dynamic scenes with fast training speed and competitive rendering qualities.
Towards A Unified Conformer Structure: from ASR to ASV Task
Transformer has achieved extraordinary performance in Natural Language Processing and Computer Vision tasks thanks to its powerful self-attention mechanism, and its variant Conformer has become a state-of-the-art architecture in the field of Automatic Speech Recognition (ASR).
DART: Articulated Hand Model with Diverse Accessories and Rich Textures
Unity GUI is also provided to generate synthetic hand data with user-defined settings, e. g., pose, camera, background, lighting, textures, and accessories.
Learning to Decompose Visual Features with Latent Textual Prompts
Recent advances in pre-training vision-language models like CLIP have shown great potential in learning transferable visual representations.
DUET: A Tuning-Free Device-Cloud Collaborative Parameters Generation Framework for Efficient Device Model Generalization
DUET is deployed on a powerful cloud server that only requires the low cost of forwarding propagation and low time delay of data transmission between the device and the cloud.
Communication-Efficient and Privacy-Preserving Feature-based Federated Transfer Learning
In order to improve the communication efficiency, we in this paper propose the feature-based federated transfer learning as an innovative approach to reduce the uplink payload by more than five orders of magnitude compared to that of existing approaches.
Personalizing Intervened Network for Long-tailed Sequential User Behavior Modeling
In this paper, we propose a novel approach that significantly improves the recommendation performance of the tail users while achieving at least comparable performance for the head users over the base model.
Fully Sparse 3D Object Detection
To enable efficient long-range LiDAR-based object detection, we build a fully sparse 3D object detector (FSD).
SHREC'22 Track: Sketch-Based 3D Shape Retrieval in the Wild
We define two SBSR tasks and construct two benchmarks consisting of more than 46, 000 CAD models, 1, 700 realistic models, and 145, 000 sketches in total.
Device-Cloud Collaborative Recommendation via Meta Controller
To overcome this issue, we propose a meta controller to dynamically manage the collaboration between the on-device recommender and the cloud-based recommender, and introduce a novel efficient sample construction from the causal perspective to solve the dataset absence issue of meta controller.
Towards Understanding and Mitigating Audio Adversarial Examples for Speaker Recognition
According to the characteristic of SRSs, we present 22 diverse transformations and thoroughly evaluate them using 7 recent promising adversarial attacks (4 white-box and 3 black-box) on speaker recognition.
Keywords and Instances: A Hierarchical Contrastive Learning Framework Unifying Hybrid Granularities for Text Generation
Contrastive learning has achieved impressive success in generation tasks to militate the "exposure bias" problem and discriminatively exploit the different quality of references.
CP2: Copy-Paste Contrastive Pretraining for Semantic Segmentation
Recent advances in self-supervised contrastive learning yield good image-level representation, which favors classification tasks but usually neglects pixel-level detailed information, leading to unsatisfactory transfer performance to dense prediction tasks such as semantic segmentation.
DePS: An improved deep learning model for de novo peptide sequencing
De novo peptide sequencing from mass spectrometry data is an important method for protein identification.
TECM: Transfer Learning-based Evidential C-Means Clustering
As a representative evidential clustering algorithm, evidential c-means (ECM) provides a deeper insight into the data by allowing an object to belong not only to a single class, but also to any subset of a collection of classes, which generalizes the hard, fuzzy, possibilistic, and rough partitions.
Tracing Text Provenance via Context-Aware Lexical Substitution
Tracing text provenance can help claim the ownership of text content or identify the malicious users who distribute misleading content like machine-generated fake news.
Embracing Single Stride 3D Object Detector with Sparse Transformer
In LiDAR-based 3D object detection for autonomous driving, the ratio of the object size to input scene size is significantly smaller compared to 2D detection cases.
Ranked #2 on
3D Object Detection
on waymo pedestrian
Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI
However, edge computing, especially edge and cloud collaborative computing, are still in its infancy to announce their success due to the resource-constrained IoT scenarios with very limited algorithms deployed.
Enhanced Fast Boolean Matching based on Sensitivity Signatures Pruning
In this paper, we introduce Boolean sensitivity into Boolean matching and design several sensitivity-related signatures to enhance fast Boolean matching.
Self-supervised denoising for massive noisy images
We propose an effective deep learning model for signal reconstruction, which requires no signal prior, no noise model calibration, and no clean samples.
Self-Supervised Learning by Estimating Twin Class Distributions
To solve this problem, we propose to maximize the mutual information between the input and the class predictions.
Ranked #1 on
Image Classification
on Oxford-IIIT Pet Dataset
Fine-Grained Image Classification
Representation Learning
+5
Boost Neural Networks by Checkpoints
In the experiments, it yields up to 5. 02% higher accuracy over single EfficientNet-B0 on the imbalanced datasets.
MC$^2$-SF: Slow-Fast Learning for Mobile-Cloud Collaborative Recommendation
With the hardware development of mobile devices, it is possible to build the recommendation models on the mobile side to utilize the fine-grained features and the real-time feedbacks.
YOLOX: Exceeding YOLO Series in 2021
In this report, we present some experienced improvements to YOLO series, forming a new high-performance detector -- YOLOX.
Ranked #1 on
Real-Time Object Detection
on Argoverse-HD (Detection-Only, Val)
(using extra training data)
Adversarial Reinforcement Learning in Dynamic Channel Access and Power Control
Deep reinforcement learning (DRL) has recently been used to perform efficient resource allocation in wireless communications.
Device-Cloud Collaborative Learning for Recommendation
With the rapid development of storage and computing power on mobile devices, it becomes critical and popular to deploy models on devices to save onerous communication latencies and to capture real-time features.
LiDAR R-CNN: An Efficient and Universal 3D Object Detector
LiDAR-based 3D detection in point cloud is essential in the perception system of autonomous driving.
RangeDet:In Defense of Range View for LiDAR-based 3D Object Detection
The most notable difference with previous works is that our method is purely based on the range view representation.
RangeDet: In Defense of Range View for LiDAR-Based 3D Object Detection
We first analyze the existing range-view-based methods and find two issues overlooked by previous works: 1) the scale variation between nearby and far away objects; 2) the inconsistency between the 2D range image coordinates used in feature extraction and the 3D Cartesian coordinates used in output.
Understanding the Behaviour of Contrastive Loss
We will show that the contrastive loss is a hardness-aware loss function, and the temperature {\tau} controls the strength of penalties on hard negative samples.
Exploiting Behavioral Consistence for Universal User Representation
In this work, we focus on developing universal user representation model.
Unsupervised Representation Learning by Invariance Propagation
In this paper, we propose Invariance Propagation to focus on learning representations invariant to category-level variations, which are provided by different instances from the same category.
Unsupervised Representation Learning by InvariancePropagation
In this paper, we propose Invariance Propagation to focus on learning representations invariant to category-level variations, which are provided by different instances from the same category.
Convolutional neural network based deep-learning architecture for intraprostatic tumour contouring on PSMA PET images in patients with primary prostate cancer
Accurate delineation of the intraprostatic gross tumour volume (GTV) is a prerequisite for treatment approaches in patients with primary prostate cancer (PCa).
Adversarial jamming attacks and defense strategies via adaptive deep reinforcement learning
As the applications of deep reinforcement learning (DRL) in wireless communications grow, sensitivity of DRL based wireless communication strategies against adversarial attacks has started to draw increasing attention.
Zero-shot Text Classification via Reinforced Self-training
In this situation, transferring from seen classes to unseen classes is extremely hard.
Cooling-Aware Resource Allocation and Load Management for Mobile Edge Computing Systems
Driven by explosive computation demands of Internet of Things (IoT), mobile edge computing (MEC) provides a promising technique to enhance the computation capability for mobile users.
SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check
This paper proposes to incorporate phonological and visual similarity knowledge into language models for CSC via a specialized graph convolutional network (SpellGCN).
An end-to-end CNN framework for polarimetric vision tasks based on polarization-parameter-constructing network
Pixel-wise operations between polarimetric images are important for processing polarization information.
COVID-19 Docking Server: A meta server for docking small molecules, peptides and antibodies against potential targets of COVID-19
Motivation: The coronavirus disease 2019 (COVID-19) caused by a new type of coronavirus has been emerging from China and led to thousands of death globally since December 2019.
A Discriminative Learned CNN Embedding for Remote Sensing Image Scene Classification
Concretely, for remote sensing image scene classification, we would like to map images from the same scene to feature vectors that are close, and map images from different scenes to feature vectors that are widely separated.
Kingsoft's Neural Machine Translation System for WMT19
This paper describes the Kingsoft AI Lab{'}s submission to the WMT2019 news translation shared task.
AirFace: Lightweight and Efficient Model for Face Recognition
With the development of convolutional neural network, significant progress has been made in computer vision tasks.
Self-Attention Aligner: A Latency-Control End-to-End Model for ASR Using Self-Attention Network and Chunk-Hopping
Experiments on two Mandarin ASR datasets show the replacement of RNNs by the self-attention networks yields a 8. 4%-10. 2% relative character error rate (CER) reduction.
Multi-scale Convolutional Neural Networks for Inverse Problems
We propose a novel neural network architecture highlighting fast convergence as a generic solution addressing image(s)-to-image(s) inverse problems of different domains.
Computational Physics Materials Science
Dual Ask-Answer Network for Machine Reading Comprehension
The task of question answering or question generation aims to infer an answer or a question when given the counterpart based on context.
Cascaded Mutual Modulation for Visual Reasoning
Our code is available at https://github. com/FlamingHorizon/CMM-VR.
Semi-Supervised Disfluency Detection
While the disfluency detection has achieved notable success in the past years, it still severely suffers from the data scarcity.
Unsupervised Neural Machine Translation with Weight Sharing
Unsupervised neural machine translation (NMT) is a recently proposed approach for machine translation which aims to train the model without using any labeled data.
Ranked #5 on
Machine Translation
on WMT2016 German-English
Precise Temporal Action Localization by Evolving Temporal Proposals
The performances of existing action localization approaches remain unsatisfactory in precisely determining the beginning and the end of an action.
A Subpixel Registration Algorithm for Low PSNR Images
This paper presents a fast algorithm for obtaining high-accuracy subpixel translation of low PSNR images.
Additive Margin Softmax for Face Verification
In this work, we introduce a novel additive angular margin for the Softmax loss, which is intuitively appealing and more interpretable than the existing works.
Ranked #2 on
Face Identification
on Trillion Pairs Dataset
Towards Compact and Fast Neural Machine Translation Using a Combined Method
Neural Machine Translation (NMT) lays intensive burden on computation and memory cost.
Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme
Joint extraction of entities and relations is an important task in information extraction.
Ranked #3 on
Relation Extraction
on NYT-single
NormFace: L2 Hypersphere Embedding for Face Verification
We show that both strategies, and small variants, consistently improve performance by between 0. 2% to 0. 4% on the LFW dataset based on two models.
On Consistency of Graph-based Semi-supervised Learning
We prove the consistency of graph-based learning in the case that the estimated scores are enforced to be equal to the observed responses for the labeled data.
Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets
During training, both the dynamic discriminator and the static BLEU objective are employed to evaluate the generated sentences and feedback the evaluations to guide the learning of the generator.
DALiuGE: A Graph Execution Framework for Harnessing the Astronomical Data Deluge
The Data Activated Liu Graph Engine - DALiuGE - is an execution framework for processing large astronomical datasets at a scale required by the Square Kilometre Array Phase 1 (SKA1).
Distributed, Parallel, and Cluster Computing Instrumentation and Detectors
Regularizing Face Verification Nets For Pain Intensity Regression
In this way, the expression intensity regression task can benefit from the rich feature representations trained on a huge amount of data for face verification.
MAT: A Multimodal Attentive Translator for Image Captioning
In this way, the sequential representation of an image can be naturally translated to a sequence of words, as the target sequence of the RNN model.
A Character-Aware Encoder for Neural Machine Translation
This article proposes a novel character-aware neural machine translation (NMT) model that views the input sequences as sequences of characters rather than words.
DeepPicker: a Deep Learning Approach for Fully Automated Particle Picking in Cryo-EM
Particle picking is a time-consuming step in single-particle analysis and often requires significant interventions from users, which has become a bottleneck for future automated electron cryo-microscopy (cryo-EM).
Survey on the attention based RNN model and its applications in computer vision
In this survey, we introduce some attention based RNN models which can focus on different parts of the input for each output item, in order to explore and take advantage of the implicit relations between the input and the output items.
Qualitative detection of oil adulteration with machine learning approaches
The study focused on the machine learning analysis approaches to identify the adulteration of 9 kinds of edible oil qualitatively and answered the following three questions: Is the oil sample adulterant?
