Search Results for author:
Found 62 papers, 24 papers with code
When does Further Pre-training MLM Help? An Empirical Study on Task-Oriented Dialog Pre-training
Further pre-training language models on in-domain data (domain-adaptive pre-training, DAPT) or task-relevant data (task-adaptive pre-training, TAPT) before fine-tuning has been shown to improve downstream tasks’ performances.
Knowledge Is Flat: A Seq2Seq Generative Framework for Various Knowledge Graph Completion
To remedy the KG structure information loss from the "flat" text, we further improve the input representations of entities and relations, and the inference algorithm in KG-S2S.
Combating Mode Collapse in GANs via Manifold Entropy Estimation
Different from existing methods, we propose to generalize the discriminator as feature embedding, and maximize the entropy of distributions in the embedding space learned by the discriminator.
Cross-Architecture Knowledge Distillation
However, most existing knowledge distillation methods only consider homologous-architecture distillation, such as distilling knowledge from CNN to CNN.
Nonlinear Sufficient Dimension Reduction for Distribution-on-Distribution Regression
We introduce a novel framework for nonlinear sufficient dimension reduction where both the predictor and the response are distributional data, which are modeled as members of a metric space.
PIC 4th Challenge: Semantic-Assisted Multi-Feature Encoding and Multi-Head Decoding for Dense Video Captioning
The task of Dense Video Captioning (DVC) aims to generate captions with timestamps for multiple events in one video.
Narrowing the Gap: Improved Detector Training with Noisy Location Annotations
Experiments for both synthesized and real-world scenarios consistently demonstrate the effectiveness of our approach, e. g., our method increases the degraded performance of the FCOS detector from 33. 6% AP to 35. 6% AP on COCO.
SubGraph Networks based Entity Alignment for Cross-lingual Knowledge Graph
Cross-lingual knowledge graph entity alignment aims to discover the cross-lingual links in the multi-language KGs, which is of great significance to the NLP applications and multi-language KGs fusion.
Representation Learning for Compressed Video Action Recognition via Attentive Cross-modal Interaction with Motion Enhancement
To address the two issues above, this paper proposes a novel framework, namely Attentive Cross-modal Interaction Network with Motion Enhancement (MEACI-Net).
Improving Visual Grounding with Visual-Linguistic Verification and Iterative Reasoning
They base the visual grounding on the features from pre-generated proposals or anchors, and fuse these features with the text embeddings to locate the target mentioned by the text.
CREATE: A Benchmark for Chinese Short Video Retrieval and Title Generation
In this paper, we propose to CREATE, the first large-scale Chinese shoRt vidEo retrievAl and Title gEneration benchmark, to facilitate research and application in video titling and video retrieval in Chinese.
Disentangling Object Motion and Occlusion for Unsupervised Multi-frame Monocular Depth
Conventional self-supervised monocular depth prediction methods are based on a static environment assumption, which leads to accuracy degradation in dynamic scenes due to the mismatch and occlusion problems introduced by object motions.
Learning Scene Flow in 3D Point Clouds with Noisy Pseudo Labels
To provide an alternative, we propose a novel approach that utilizes monocular RGB images and point clouds to generate pseudo scene flow labels for training scene flow networks.
Continual Prompt Tuning for Dialog State Tracking
A desirable dialog system should be able to continually learn new skills without forgetting old ones, and thereby adapt to new domains or tasks in its life cycle.
Learning Target-aware Representation for Visual Tracking via Informative Interactions
The proposed GIM module and InBN mechanism are general and applicable to different backbone types including CNN and Transformer for improvements, as evidenced by our extensive experiments on multiple benchmarks.
An additive graphical model for discrete data
We introduce a nonparametric graphical model for discrete node variables based on additive conditional independence.
Recursive Least-Squares Estimator-Aided Online Learning for Visual Tracking
In this paper, we propose a simple yet effective recursive least-squares estimator-aided online learning approach for few-shot online adaptation without requiring offline training.
EMScore: Evaluating Video Captioning via Coarse-Grained and Fine-Grained Embedding Matching
The datasets will be released to facilitate the development of video captioning metrics.
Joint Learning of Visual-Audio Saliency Prediction and Sound Source Localization on Multi-face Videos
In this paper, we propose a multitask learning method for visual-audio saliency prediction and sound source localization on multi-face video by leveraging visual, audio and face information.
Multimodal Semi-Supervised Learning for 3D Objects
This paper explores how the coherence of different modelities of 3D data (e. g. point cloud, image, and mesh) can be used to improve data efficiency for both 3D classification and retrieval tasks.
MDERank: A Masked Document Embedding Rank Approach for Unsupervised Keyphrase Extraction
In this work, we propose a novel unsupervised embedding-based KPE approach, Masked Document Embedding Rank (MDERank), to address this problem by leveraging a mask strategy and ranking candidates by the similarity between embeddings of the source document and the masked document.
Dimension Reduction and Data Visualization for Fréchet Regression
In this paper, we introduce a flexible sufficient dimension reduction (SDR) method for Fr\'echet regression to achieve two purposes: to mitigate the curse of dimensionality caused by high-dimensional predictors, and to provide a tool for data visualization for Fr\'echet regression.
Self-Supervised Modality-Invariant and Modality-Specific Feature Learning for 3D Objects
Our proposed novel self-supervised model learns two types of distinct features: modality-invariant features and modality-specific features.
Advancing Self-supervised Monocular Depth Learning with Sparse LiDAR
Unlike the existing methods that use sparse LiDAR mainly in a manner of time-consuming iterative post-processing, our model fuses monocular image features and sparse LiDAR features to predict initial depth maps.
Ranked #1 on
Depth Completion
on KITTI
SDTP: Semantic-aware Decoupled Transformer Pyramid for Dense Image Prediction
CDI builds the global attention and interaction among different levels in decoupled space which also solves the problem of heavy computation.
Sent2Span: Span Detection for PICO Extraction in the Biomedical Text without Span Annotations
The rapid growth in published clinical trials makes it difficult to maintain up-to-date systematic reviews, which requires finding all relevant trials.
Learn to Match: Automatic Matching Network Design for Visual Tracking
Siamese tracking has achieved groundbreaking performance in recent years, where the essence is the efficient matching operator cross-correlation and its variants.
PSE-Match: A Viewpoint-free Place Recognition Method with Parallel Semantic Embedding
Accurate localization on autonomous driving cars is essential for autonomy and driving safety, especially for complex urban streets and search-and-rescue subterranean environments where high-accurate GPS is not available.
Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition
Graph convolutional networks (GCNs) have been widely used and achieved remarkable results in skeleton-based action recognition.
Ranked #3 on
Skeleton Based Action Recognition
on N-UCLA
Learned Image Compression with Discretized Gaussian-Laplacian-Logistic Mixture Model and Concatenated Residual Modules
However, due to the vast diversity of images, it is not optimal to use one model for all images, even different regions of one image.
Automatic Construction of Enterprise Knowledge Base
In this paper, we present an automatic knowledge base construction system from large scale enterprise documents with minimal efforts of human intervention.
SCTN: Sparse Convolution-Transformer Network for Scene Flow Estimation
We propose a novel scene flow estimation approach to capture and infer 3D motions from point clouds.
A Simple and Strong Baseline for Universal Targeted Attacks on Siamese Visual Tracking
In this paper, we show the existence of universal perturbations that can enable the targeted attack, e. g., forcing a tracker to follow the ground-truth trajectory with specified offsets, to be video-agnostic and free from inference in a network.
PDNet: Towards Better One-stage Object Detection with Prediction Decoupling
However, the most suitable positions for inferring different targets, i. e., the object category and boundaries, are generally different.
One More Check: Making "Fake Background" Be Tracked Again
Eventually, it helps to reload the ``fake background'' and repair the broken tracklets.
Learning to Predict Salient Faces: A Novel Visual-Audio Saliency Model
Inspired by the findings of our investigation, we propose a novel multi-modal video saliency model consisting of three branches: visual, audio and face.
Open-book Video Captioning with Retrieve-Copy-Generate Network
Due to the rapid emergence of short videos and the requirement for content understanding and creation, the video captioning task has received increasing attention in recent years.
AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generation
Specifically, a new generator architecture is proposed to simultaneously transfer color/texture styles and transform local facial shapes into anime-like counterparts based on the style of a reference anime-face, while preserving the global structure of the source photo-face.
On Forward Sufficient Dimension Reduction for Categorical and Ordinal Responses
Like other forward regression-based sufficient dimension reduction methods, our approach avoids the relatively stringent distributional requirements necessary for inverse regression alternatives.
Dimensionality Reduction
Methodology
Periodic repeating fast radio bursts: interaction between a magnetized neutron star and its planet in an eccentric orbit
Fast radio bursts (FRBs) are mysterious transient phenomena.
High Energy Astrophysical Phenomena
FGNET-RH: Fine-Grained Named Entity Typing via Refinement in Hyperbolic Space
Fine-Grained Named Entity Typing (FG-NET) aims at classifying the entity mentions into a wide range of entity types (usually hundreds) depending upon the context.
Cross-Lingual Named Entity Recognition Using Parallel Corpus: A New Approach Using XLM-RoBERTa Alignment
We built an entity alignment model on top of XLM-RoBERTa to project the entities detected on the English part of the parallel data to the target language sentences, whose accuracy surpasses all previous unsupervised models.
Named Entity Recognition in the Style of Object Detection
In this work, we propose a two-stage method for named entity recognition (NER), especially for nested NER.
High Quality Disparity Remapping With Two-Stage Warping
In the second stage, we derive another warping model to refine warping results in less important regions by eliminating serious distortions in shape, disparity and 3D structure.
DSIC: Dynamic Sample-Individualized Connector for Multi-Scale Object Detection
Furthermore, these two components are both plug-and-play and can be embedded in any backbone.
Rethinking the competition between detection and ReID in Multi-Object Tracking
However, the inherent differences and relations between detection and re-identification (ReID) are unconsciously overlooked because of treating them as two isolated tasks in the one-shot tracking paradigm.
Towards Accurate Pixel-wise Object Tracking by Attention Retrieval
We first build a look-up-table (LUT) with the ground-truth mask in the starting frame, and then retrieves the LUT to obtain an attention map for spatial constraints.
Ocean: Object-aware Anchor-free Tracking
In this paper, we propose a novel object-aware anchor-free network to address this issue.
Ranked #2 on
Visual Object Tracking
on VOT2019
Fine-Grained Named Entity Typing over Distantly Supervised Data Based on Refined Representations
Fine-Grained Named Entity Typing (FG-NET) is a key component in Natural Language Processing (NLP).
Object Relational Graph with Teacher-Recommended Learning for Video Captioning
In this paper, we propose a complete video captioning system including both a novel model and an effective training strategy.
Ranked #1 on
Video Captioning
on MSR-VTT
(using extra training data)
HAMNER: Headword Amplified Multi-span Distantly Supervised Method for Domain Specific Named Entity Recognition
To tackle Named Entity Recognition (NER) tasks, supervised methods need to obtain sufficient cleanly annotated data, which is labor and time consuming.
VATEX Captioning Challenge 2019: Multi-modal Information Fusion and Multi-stage Training Strategy for Video Captioning
Multi-modal information is essential to describe what has happened in a video.
An Overview of In-memory Processing with Emerging Non-volatile Memory for Data-intensive Applications
The conventional von Neumann architecture has been revealed as a major performance and energy bottleneck for rising data-intensive applications.
Multimodal Semantic Attention Network for Video Captioning
Inspired by the fact that different modalities in videos carry complementary information, we propose a Multimodal Semantic Attention Network(MSAN), which is a new encoder-decoder framework incorporating multimodal semantic attributes for video captioning.
Interaction-aware Spatio-temporal Pyramid Attention Networks for Action Classification
Furthermore, since different layers in a deep network capture feature maps of different scales, we use these feature maps to construct a spatial pyramid and then utilize multi-scale information to obtain more accurate attention scores, which are used to weight the local features in all spatial positions of feature maps to calculate attention maps.
Depth-Aware Stereo Video Retargeting
As compared with traditional video retargeting, stereo video retargeting poses new challenges because stereo video contains the depth information of salient objects and its time dynamics.
Automatic Difficulty Assessment for Chinese Texts
We present a web-based interface that automatically assesses reading difficulty of Chinese texts.
Spatio-Temporal Self-Organizing Map Deep Network for Dynamic Object Detection From Videos
In dynamic object detection, it is challenging to construct an effective model to sufficiently characterize the spatial-temporal properties of the background.
Linear Contour Learning: A Method for Supervised Dimension Reduction
We propose a novel approach to sufficient dimension reduction in regression, based on estimating contour directions of negligible variation for the response surface.
Removing Mixture of Gaussian and Impulse Noise by Patch-Based Weighted Means
We first establish a law of large numbers and a convergence theorem in distribution to show the rate of convergence of the non-local means filter for removing Gaussian noise.
Illumination Estimation Based on Bilayer Sparse Coding
In this paper, we propose a novel bilayer sparse coding model for illumination estimation that considers image similarity in terms of both low level color distribution and high level image scene content simultaneously.
