Search Results for author:
Found 35 papers, 15 papers with code
WebUOT-1M: Advancing Deep Underwater Object Tracking with A Million-Scale Benchmark
Most existing trackers are tailored for open-air environments, leading to performance degradation when applied to UOT due to domain gaps.
Awesome Multi-modal Object Tracking
To leverage more modalities, some recent efforts have been made to learn a unified visual object tracking model for any modality.
On the Federated Learning Framework for Cooperative Perception
Cooperative perception is essential to enhance the efficiency and safety of future transportation systems, requiring extensive data sharing among vehicles on the road, which raises significant privacy concerns.
V2X Cooperative Perception for Autonomous Driving: Recent Advances and Challenges
To address this gap, this paper provides a comprehensive overview of the evolution of CP technologies, spanning from early explorations to recent developments, including advancements in V2X communication technologies.
Domain-adaptive Message Passing Graph Neural Network
Cross-network node classification (CNNC), which aims to classify nodes in a label-deficient target network by transferring the knowledge from a source network with abundant labels, draws increasing attention recently.
Masked Spatio-Temporal Structure Prediction for Self-supervised Learning on Point Cloud Videos
In this paper, we propose a Masked Spatio-Temporal Structure Prediction (MaST-Pre) method to capture the structure of point cloud videos without human annotations.
All in One: Exploring Unified Vision-Language Tracking with Multi-Modal Alignment
This approach achieves feature integration in a unified backbone, removing the need for carefully-designed fusion modules and resulting in a more effective and efficient VL tracking framework.
PointCMP: Contrastive Mask Prediction for Self-supervised Learning on Point Cloud Videos
Self-supervised learning can extract representations of good quality from solely unlabeled data, which is appealing for point cloud videos due to their high labelling cost.
HiCo: Hierarchical Contrastive Learning for Ultrasound Video Model Pretraining
This work proposes a hierarchical contrastive learning (HiCo) method to improve the transferability for the US video model pretraining.
A Data Driven Method for Multi-step Prediction of Ship Roll Motion in High Sea States
This paper presents a novel data-driven methodology to provide a multi-step prediction of ship roll motions in high sea states.
You Need to Read Again: Multi-granularity Perception Network for Moment Retrieval in Videos
Moment retrieval in videos is a challenging task that aims to retrieve the most relevant video moment in an untrimmed video given a sentence description.
AI-based Medical e-Diagnosis for Fast and Automatic Ventricular Volume Measurement in the Patients with Normal Pressure Hydrocephalus
Finally, we verify the reliability of the model and achieved automatic measurement of VV and ICV.
Relation-aware Video Reading Comprehension for Temporal Language Grounding
Temporal language grounding in videos aims to localize the temporal span relevant to the given query sentence.
Composing Answer from Multi-spans for Reading Comprehension
This paper presents a novel method to generate answers for non-extraction machine reading comprehension (MRC) tasks whose answers cannot be simply extracted as one span from the given passages.
Filling the Gap of Utterance-aware and Speaker-aware Representation for Multi-turn Dialogue
A multi-turn dialogue is composed of multiple utterances from two or more different speaker roles.
Receptive Multi-granularity Representation for Person Re-Identification
Current stripe-based feature learning approaches have delivered impressive accuracy, but do not make a proper trade-off between diversity, locality, and robustness, which easily suffers from part semantic inconsistency for the conflict between rigid partition and misalignment.
Accurate Temporal Action Proposal Generation with Relation-Aware Pyramid Network
Accurate temporal action proposals play an important role in detecting actions from untrimmed videos.
Focusing and Diffusion: Bidirectional Attentive Graph Convolutional Networks for Skeleton-based Action Recognition
A collection of approaches based on graph convolutional networks have proven success in skeleton-based action recognition by exploring neighborhood information and dense dependencies between intra-frame joints.
Ranked #38 on
Skeleton Based Action Recognition
on NTU RGB+D
Layer Pruning for Accelerating Very Deep Neural Networks
In this paper, we propose an adaptive pruning method.
Semantics-aware BERT for Language Understanding
The latest work on language representations carefully integrates contextualized features into language model training, which enables a series of success especially in various machine reading comprehension and natural language inference tasks.
Ranked #6 on
Natural Language Inference
on SNLI
DCMN+: Dual Co-Matching Network for Multi-choice Reading Comprehension
Multi-choice reading comprehension is a challenging task to select an answer from a set of candidate options when given passage and question.
Relation-Aware Pyramid Network (RapNet) for temporal action proposal
In this technical report, we describe our solution to temporal action proposal (task 1) in ActivityNet Challenge 2019.
Dual Co-Matching Network for Multi-choice Reading Comprehension
Multi-choice reading comprehension is a challenging task that requires complex reasoning procedure.
Ranked #3 on
Question Answering
on RACE
Dependency or Span, End-to-End Uniform Semantic Role Labeling
Semantic role labeling (SRL) aims to discover the predicateargument structure of a sentence.
Ranked #9 on
Semantic Role Labeling
on CoNLL 2005
Pixel-Anchor: A Fast Oriented Scene Text Detector with Combined Networks
Recently, semantic segmentation and general object detection frameworks have been widely adopted by scene text detecting tasks.
Cascaded CNN-resBiLSTM-CTC: An End-to-End Acoustic Model For Speech Recognition
Automatic speech recognition (ASR) tasks are resolved by end-to-end deep learning models, which benefits us by less preparation of raw data, and easier transformation between languages.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
A novel pyramidal-FSMN architecture with lattice-free MMI for speech recognition
Deep Feedforward Sequential Memory Network (DFSMN) has shown superior performance on speech recognition tasks.
Sound Audio and Speech Processing
Toward Better Loanword Identification in Uyghur Using Cross-lingual Word Embeddings
Neural machine translation models integrating results of loanword identification experiments achieve the best results on OOV translation(with 0. 5-0. 9 BLEU improvements)
Learning Discriminative Features with Multiple Granularities for Person Re-Identification
Instead of learning on semantic regions, we uniformly partition the images into several stripes, and vary the number of parts in different local branches to obtain local feature representations with multiple granularities.
Ranked #3 on
Person Re-Identification
on SYSU-30k
(using extra training data)
Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network
We propose a straightforward method that simultaneously reconstructs the 3D facial structure and provides dense alignment.
Ranked #1 on
3D Face Reconstruction
on Florence
Log-linear Models for Uyghur Segmentation in Spoken Language Translation
To alleviate data sparsity in spoken Uyghur machine translation, we proposed a log-linear based morphological segmentation approach.
A Deep Regression Architecture With Two-Stage Re-Initialization for High Performance Facial Landmark Detection
At the global stage, given an image with a rough face detection result, the full face region is firstly re-initialized by a supervised spatial transformer network to a canonical shape state and then trained to regress a coarse landmark estimation.
A Bilingual Discourse Corpus and Its Applications
Existing discourse research only focuses on the monolingual languages and the inconsistency between languages limits the power of the discourse theory in multilingual applications such as machine translation.
