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Found 127 papers, 32 papers with code
Capture Human Disagreement Distributions by Calibrated Networks for Natural Language Inference
Natural Language Inference (NLI) datasets contain examples with highly ambiguous labels due to its subjectivity.
HwTscSU’s Submissions on WAT 2022 Shared Task
In this paper we describe our submission to the shared tasks of the 9th Workshop on Asian Translation (WAT 2022) on NICT–SAP under the team name ”HwTscSU”.
数字人文视角下的《史记》《汉书》比较研究(A Comparative Study of Shiji and Hanshu from the Perspective of Digital Humanities)
"《史记》和《汉书》具有经久不衰的研究价值。尽管两书异同的研究已经较为丰富, 但研究的全面性、完备性、科学性、客观性均仍显不足。在数字人文的视角下, 本文利用计算语言学方法, 通过对字、词、命名实体、段落等的多粒度、多角度分析, 开展对于《史》《汉》的比较研究。首先, 本文对于《史》《汉》中的字、词、命名实体的分布和特点进行对比, 以遍历穷举的考察方式提炼出两书在主要内容上的相同点与不同点, 揭示了汉武帝之前和汉武帝到西汉灭亡两段历史时期在政治、文化、思想上的重要变革与承袭。其次, 本文使用一种融入命名实体作为外部特征的文本相似度算法对于《史记》《汉书》的异文进行自动发现, 成功识别出过去研究者通过人工手段没有发现的袭用段落, 使得我们对于《史》《汉》的承袭关系形成更加完整和立体的认识。再次, 本文通过计算异文段落之间的最长公共子序列来自动得出两段异文之间存在的差异, 从宏观统计上证明了《汉书》文字风格《史记》的差别, 并从微观上进一步对二者语言特点进行了阐释, 为理解《史》《汉》异文特点提供了新的角度和启发。本研究站在数字人文的视域下, 利用先进的计算方法对于传世千年的中国古代经典进行了再审视、再发现, 其方法对于今人研究古籍有一定的借鉴价值。”
The HW-TSC’s Offline Speech Translation System for IWSLT 2022 Evaluation
For machine translation part, we pretrained three translation models on WMT21 dataset and fine-tuned them on in-domain corpora.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
The HW-TSC’s Simultaneous Speech Translation System for IWSLT 2022 Evaluation
The cascade system is composed of a chunking-based streaming ASR model and the SimulMT model used in the T2T track.
The HW-TSC’s Speech to Speech Translation System for IWSLT 2022 Evaluation
The paper presents the HW-TSC’s pipeline and results of Offline Speech to Speech Translation for IWSLT 2022.
HW-TSC’s Participation in the IWSLT 2022 Isometric Spoken Language Translation
This paper presents our submissions to the IWSLT 2022 Isometric Spoken Language Translation task.
HW-TSC at SemEval-2022 Task 7: Ensemble Model Based on Pretrained Models for Identifying Plausible Clarifications
This paper describes the system for the identifying Plausible Clarifications of Implicit and Underspecified Phrases.
Unified Humor Detection Based on Sentence-pair Augmentation and Transfer Learning
We propose a unified multilingual model for humor detection which can be trained under a transfer learning framework.
Efficient Transfer Learning for Quality Estimation with Bottleneck Adapter Layer
Compared with the commonly used NuQE baseline, BAL-QE achieves 47% (En-Ru) and 75% (En-De) of performance promotions.
HW-TSC’s Participation in the WAT 2020 Indic Languages Multilingual Task
This paper describes our work in the WAT 2020 Indic Multilingual Translation Task.
HW-TSC’s Participation in the WMT 2020 News Translation Shared Task
We also conduct experiment with similar language augmentation, which lead to positive results, although not used in our submission.
HW-TSC’s Participation at WMT 2020 Automatic Post Editing Shared Task
The paper presents the submission by HW-TSC in the WMT 2020 Automatic Post Editing Shared Task.
Huawei’s Submissions to the WMT20 Biomedical Translation Task
This paper describes Huawei’s submissions to the WMT20 biomedical translation shared task.
HW-TSC’s Participation at WMT 2020 Quality Estimation Shared Task
This paper presents our work in the WMT 2020 Word and Sentence-Level Post-Editing Quality Estimation (QE) Shared Task.
How Length Prediction Influence the Performance of Non-Autoregressive Translation?
Length prediction is a special task in a series of NAT models where target length has to be determined before generation.
Make the Blind Translator See The World: A Novel Transfer Learning Solution for Multimodal Machine Translation
Based on large-scale pretrained networks and the liability to be easily overfitting with limited labelled training data of multimodal translation (MMT) is a critical issue in MMT.
HI-CMLM: Improve CMLM with Hybrid Decoder Input
Mask-predict CMLM (Ghazvininejad et al., 2019) has achieved stunning performance among non-autoregressive NMT models, but we find that the mechanism of predicting all of the target words only depending on the hidden state of [MASK] is not effective and efficient in initial iterations of refinement, resulting in ungrammatical repetitions and slow convergence.
HW-TSC’s Participation in the WMT 2021 News Translation Shared Task
This paper presents the submission of Huawei Translate Services Center (HW-TSC) to the WMT 2021 News Translation Shared Task.
HW-TSC’s Participation in the WMT 2021 Triangular MT Shared Task
This paper presents the submission of Huawei Translation Service Center (HW-TSC) to WMT 2021 Triangular MT Shared Task.
HW-TSC’s Participation in the WMT 2021 Large-Scale Multilingual Translation Task
This paper presents the submission of Huawei Translation Services Center (HW-TSC) to the WMT 2021 Large-Scale Multilingual Translation Task.
HW-TSC’s Participation in the WMT 2021 Efficiency Shared Task
This paper presents the submission of Huawei Translation Services Center (HW-TSC) to WMT 2021 Efficiency Shared Task.
HW-TSC’s Submissions to the WMT21 Biomedical Translation Task
This paper describes the submission of Huawei Translation Service Center (HW-TSC) to WMT21 biomedical translation task in two language pairs: Chinese↔English and German↔English (Our registered team name is HuaweiTSC).
HW-TSC’s Participation at WMT 2021 Quality Estimation Shared Task
This paper presents our work in WMT 2021 Quality Estimation (QE) Shared Task.
Text Style Transfer Back-Translation
To address this issue, we propose Text Style Transfer Back Translation (TST BT), which uses a style transfer model to modify the source side of BT data.
Investigating Pre-trained Audio Encoders in the Low-Resource Condition
Pre-trained speech encoders have been central to pushing state-of-the-art results across various speech understanding and generation tasks.
Integrating Action Knowledge and LLMs for Task Planning and Situation Handling in Open Worlds
Each situation corresponds to a state instance wherein a robot is potentially unable to complete a task using a solution that normally works.
UNIMO-3: Multi-granularity Interaction for Vision-Language Representation Learning
The experimental results show that our model achieves state-of-the-art performance in various downstream tasks, and through ablation study can prove that effective cross-layer learning improves the model's ability of multimodal representation.
Imbalanced Aircraft Data Anomaly Detection
Anomaly detection in temporal data from sensors under aviation scenarios is a practical but challenging task: 1) long temporal data is difficult to extract contextual information with temporal correlation; 2) the anomalous data are rare in time series, causing normal/abnormal imbalance in anomaly detection, making the detector classification degenerate or even fail.
Surgical tool classification and localization: results and methods from the MICCAI 2022 SurgToolLoc challenge
Unfortunately, obtaining the annotations needed to train machine learning models to identify and localize surgical tools is a difficult task.
Musketeer (All for One, and One for All): A Generalist Vision-Language Model with Task Explanation Prompts
We present a sequence-to-sequence vision-language model whose parameters are jointly trained on all tasks (all for one) and fully shared among multiple tasks (one for all), resulting in a single model which we named Musketeer.
Context-aware Domain Adaptation for Time Series Anomaly Detection
We formulate context sampling into the Markov decision process and exploit deep reinforcement learning to optimize the time series domain adaptation process via context sampling and design a tailored reward function to generate domain-invariant features that better align two domains for anomaly detection.
Few-shot Class-incremental Learning for Cross-domain Disease Classification
The ability to incrementally learn new classes from limited samples is crucial to the development of artificial intelligence systems for real clinical application.
InterFormer: Real-time Interactive Image Segmentation
Second, the model has to repeatedly process the image, the annotator's current click, and the model's feedback of the annotator's former clicks at each step of interaction, resulting in redundant computations.
ContraNeRF: Generalizable Neural Radiance Fields for Synthetic-to-real Novel View Synthesis via Contrastive Learning
In this work, we first investigate the effects of synthetic data in synthetic-to-real novel view synthesis and surprisingly observe that models trained with synthetic data tend to produce sharper but less accurate volume densities.
MGA: Medical generalist agent through text-guided knowledge transformation
To this end, we propose a novel medical generalist agent, MGA, that can address three kinds of common clinical tasks via clinical reports knowledge transformation.
Introspective Cross-Attention Probing for Lightweight Transfer of Pre-trained Models
We show that, even when selecting a single top-scoring adapter, InCA achieves performance comparable to full fine-tuning, at a cost comparable to fine-tuning just the last layer.
A Meta-Learning Approach to Predicting Performance and Data Requirements
The PPL improves the performance estimation on average by 37% across 16 classification and 33% across 10 detection datasets, compared to the power law.
KG-BERTScore: Incorporating Knowledge Graph into BERTScore for Reference-Free Machine Translation Evaluation
BERTScore is an effective and robust automatic metric for referencebased machine translation evaluation.
SwiftAvatar: Efficient Auto-Creation of Parameterized Stylized Character on Arbitrary Avatar Engines
To this end, we propose SwiftAvatar, a novel avatar auto-creation framework that is evidently superior to previous works.
Guided Recommendation for Model Fine-Tuning
With thousands of historical training jobs, a recommendation system can be learned to predict the model selection score given the features of the dataset and the model as input.
FreeEnricher: Enriching Face Landmarks without Additional Cost
To evaluate our method, we manually label the dense landmarks on 300W testset.
P-Transformer: Towards Better Document-to-Document Neural Machine Translation
To alleviate this problem, we propose a position-aware Transformer (P-Transformer) to enhance both the absolute and relative position information in both self-attention and cross-attention.
OFASys: A Multi-Modal Multi-Task Learning System for Building Generalist Models
As a starting point, we provide presets of 7 different modalities and 23 highly-diverse example tasks in OFASys, with which we also develop a first-in-kind, single model, OFA+, that can handle text, image, speech, video, and motion data.
TinyKG: Memory-Efficient Training Framework for Knowledge Graph Neural Recommender Systems
We found that our TinyKG with INT2 quantization aggressively reduces the memory footprint of activation maps with $7 \times$, only with $2\%$ loss in accuracy, allowing us to deploy KGNNs on memory-constrained devices.
Denoising Self-attentive Sequential Recommendation
Transformer-based sequential recommenders are very powerful for capturing both short-term and long-term sequential item dependencies.
SMARTQUERY: An Active Learning Framework for Graph Neural Networks through Hybrid Uncertainty Reduction
Graph neural networks have achieved significant success in representation learning.
Self-supervised Rewiring of Pre-trained Speech Encoders: Towards Faster Fine-tuning with Less Labels in Speech Processing
Pre-trained speech Transformers have facilitated great success across various speech processing tasks.
Towards Generating Adversarial Examples on Mixed-type Data
The existence of adversarial attacks (or adversarial examples) brings huge concern about the machine learning (ML) model's safety issues.
RedApt: An Adaptor for wav2vec 2 Encoding \\ Faster and Smaller Speech Translation without Quality Compromise
Pre-trained speech Transformers in speech translation (ST) have facilitated state-of-the-art (SotA) results; yet, using such encoders is computationally expensive.
Robot Task Planning and Situation Handling in Open Worlds
This paper introduces a novel algorithm (COWP) for open-world task planning and situation handling that dynamically augments the robot's action knowledge with task-oriented common sense.
ComplETR: Reducing the cost of annotations for object detection in dense scenes with vision transformers
Combined with a matching loss, it can effectively find objects that are similar to the input patch and complete the missing annotations.
MaskCLIP: Masked Self-Distillation Advances Contrastive Language-Image Pretraining
Second, masked self-distillation is also consistent with vision-language contrastive from the perspective of training objective as both utilize the visual encoder for feature aligning, and thus is able to learn local semantics getting indirect supervision from the language.
Prompt Tuning for Generative Multimodal Pretrained Models
Prompt tuning has become a new paradigm for model tuning and it has demonstrated success in natural language pretraining and even vision pretraining.
Ranked #2 on
Visual Entailment
on SNLI-VE test
Rethinking Few-Shot Object Detection on a Multi-Domain Benchmark
Most existing works on few-shot object detection (FSOD) focus on a setting where both pre-training and few-shot learning datasets are from a similar domain.
Boosting 3D Object Detection via Object-Focused Image Fusion
Given a set of point features and image feature maps, DeMF adaptively aggregates image features by taking the projected 2D location of the 3D point as reference.
Ranked #4 on
3D Object Detection
on SUN-RGBD val
M-Adapter: Modality Adaptation for End-to-End Speech-to-Text Translation
End-to-end speech-to-text translation models are often initialized with pre-trained speech encoder and pre-trained text decoder.
Reliable Representations Make A Stronger Defender: Unsupervised Structure Refinement for Robust GNN
However, both strategies are faced with some immediate problems: raw features cannot represent various properties of nodes (e. g., structure information), and representations learned by supervised GNN may suffer from the poor performance of the classifier on the poisoned graph.
MACSA: A Multimodal Aspect-Category Sentiment Analysis Dataset with Multimodal Fine-grained Aligned Annotations
In this paper, we propose a new dataset, the Multimodal Aspect-Category Sentiment Analysis (MACSA) dataset, which contains more than 21K text-image pairs.
Traffic-Twitter Transformer: A Nature Language Processing-joined Framework For Network-wide Traffic Forecasting
To better manage future roadway capacity and accommodate social and human impacts, it is crucial to propose a flexible and comprehensive framework to predict physical-aware long-term traffic conditions for public users and transportation agencies.
Instance-wise Prompt Tuning for Pretrained Language Models
Prompt Learning has recently gained great popularity in bridging the gap between pretraining tasks and various downstream tasks.
Exploring Entity Interactions for Few-Shot Relation Learning (Student Abstract)
Few-shot relation learning refers to infer facts for relations with a limited number of observed triples.
Neighbors Are Not Strangers: Improving Non-Autoregressive Translation under Low-Frequency Lexical Constraints
To this end, we propose a plug-in algorithm for this line of work, i. e., Aligned Constrained Training (ACT), which alleviates this problem by familiarizing the model with the source-side context of the constraints.
Real-Time Neural Character Rendering with Pose-Guided Multiplane Images
We propose pose-guided multiplane image (MPI) synthesis which can render an animatable character in real scenes with photorealistic quality.
Omni-DETR: Omni-Supervised Object Detection with Transformers
This is enabled by a unified architecture, Omni-DETR, based on the recent progress on student-teacher framework and end-to-end transformer based object detection.
Ranked #12 on
Semi-Supervised Object Detection
on COCO 2% labeled data
Large-Scale Pre-training for Person Re-identification with Noisy Labels
Since theses ID labels automatically derived from tracklets inevitably contain noises, we develop a large-scale Pre-training framework utilizing Noisy Labels (PNL), which consists of three learning modules: supervised Re-ID learning, prototype-based contrastive learning, and label-guided contrastive learning.
Ranked #7 on
Person Re-Identification
on CUHK03
Sentiment Word Aware Multimodal Refinement for Multimodal Sentiment Analysis with ASR Errors
Through further analysis of the ASR outputs, we find that in some cases the sentiment words, the key sentiment elements in the textual modality, are recognized as other words, which makes the sentiment of the text change and hurts the performance of multimodal sentiment models directly.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Forecast-based Multi-aspect Framework for Multivariate Time-series Anomaly Detection
Today's cyber-world is vastly multivariate.
Time Series Anomaly Detection
Unsupervised Anomaly Detection
Rethinking Feature Uncertainty in Stochastic Neural Networks for Adversarial Robustness
Extensive experiments on well-known white- and black-box attacks show that MFDV-SNN achieves a significant improvement over existing methods, which indicates that it is a simple but effective method to improve model robustness.
Diformer: Directional Transformer for Neural Machine Translation
In this paper, we aim to close the gap by preserving the original objective of AR and NAR under a unified framework.
Joint-training on Symbiosis Networks for Deep Nueral Machine Translation models
Deep encoders have been proven to be effective in improving neural machine translation (NMT) systems, but it reaches the upper bound of translation quality when the number of encoder layers exceeds 18.
Self-Distillation Mixup Training for Non-autoregressive Neural Machine Translation
An effective training strategy to improve the performance of AT models is Self-Distillation Mixup (SDM) Training, which pre-trains a model on raw data, generates distilled data by the pre-trained model itself and finally re-trains a model on the combination of raw data and distilled data.
General Facial Representation Learning in a Visual-Linguistic Manner
In this paper, we study the transfer performance of pre-trained models on face analysis tasks and introduce a framework, called FaRL, for general Facial Representation Learning in a visual-linguistic manner.
Ranked #1 on
Face Parsing
on CelebAMask-HQ
(using extra training data)
Delayed Propagation Transformer: A Universal Computation Engine towards Practical Control in Cyber-Physical Systems
This paper presents the Delayed Propagation Transformer (DePT), a new transformer-based model that specializes in the global modeling of CPS while taking into account the immutable constraints from the physical world.
Few-shot graph link prediction with domain adaptation
In this work, we propose an adversarial training based modification to the current state-of-the-arts link prediction method to solve this problem.
ADNet: Leveraging Error-Bias Towards Normal Direction in Face Alignment
On the other hand, AAM is an attention module which can get anisotropic attention mask focusing on the region of point and its local edge connected by adjacent points, it has a stronger response in tangent than in normal, which means relaxed constraints in the tangent.
Ranked #3 on
Face Alignment
on 300W
How Does Adversarial Fine-Tuning Benefit BERT?
Adversarial training (AT) is one of the most reliable methods for defending against adversarial attacks in machine learning.
Event2Graph: Event-driven Bipartite Graph for Multivariate Time-series Anomaly Detection
Modeling inter-dependencies between time-series is the key to achieve high performance in anomaly detection for multivariate time-series data.
The HW-TSC's Offline Speech Translation Systems for IWSLT 2021 Evaluation
This paper describes our work in participation of the IWSLT-2021 offline speech translation task.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Task and Situation Structures for Service Agent Planning
Everyday tasks are characterized by their varieties and variations, and frequently are not clearly specified to service agents.
Patch-Wise Spatial-Temporal Quality Enhancement for HEVC Compressed Video
To overcome these limitations, we propose a patch-wise spatial-temporal quality enhancement network which firstly extracts spatial and temporal features, then recalibrates and fuses the obtained spatial and temporal features.
Normalization of Language Embeddings for Cross-Lingual Alignment
Learning a good transfer function to map the word vectors from two languages into a shared cross-lingual word vector space plays a crucial role in cross-lingual NLP.
Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection
Graph-based fraud detection approaches have escalated lots of attention recently due to the abundant relational information of graph-structured data, which may be beneficial for the detection of fraudsters.
Ranked #3 on
Node Classification
on Amazon-Fraud
Integrating Subgraph-aware Relation and DirectionReasoning for Question Answering
Question Answering (QA) models over Knowledge Bases (KBs) are capable of providing more precise answers by utilizing relation information among entities.
Learning from Noisy Labels via Dynamic Loss Thresholding
During the training process, DLT records the loss value of each sample and calculates dynamic loss thresholds.
Style-based Point Generator with Adversarial Rendering for Point Cloud Completion
In this paper, we proposed a novel Style-based Point Generator with Adversarial Rendering (SpareNet) for point cloud completion.
Ranked #1 on
Point Cloud Completion
on ShapeNet
(Earth Mover's Distance metric)
Large-Scale Training System for 100-Million Classification at Alibaba
In the last decades, extreme classification has become an essential topic for deep learning.
Adversarial Example Detection Using Latent Neighborhood Graph
We present the first graph-based adversarial detection method that constructs a Latent Neighborhood Graph (LNG) around an input example to determine if the input example is adversarial.
On Position Embeddings in BERT
Various Position Embeddings (PEs) have been proposed in Transformer based architectures~(e. g. BERT) to model word order.
Beating Attackers At Their Own Games: Adversarial Example Detection Using Adversarial Gradient Directions
Adversarial examples are input examples that are specifically crafted to deceive machine learning classifiers.
Unsupervised Pre-training for Person Re-identification
In this paper, we present a large scale unlabeled person re-identification (Re-ID) dataset "LUPerson" and make the first attempt of performing unsupervised pre-training for improving the generalization ability of the learned person Re-ID feature representation.
Ranked #2 on
Person Re-Identification
on Market-1501
(using extra training data)
Modelling Long-distance Node Relations for KBQA with Global Dynamic Graph
The structural information of Knowledge Bases (KBs) has proven effective to Question Answering (QA).
Gaussian State-Based Quantum Illumination with Simple Photodetection
We use the continuous-variable Gaussian quantum information formalism to show that quantum illumination is better for object detection compared with coherent states of the same mean photon number, even for simple direct photodetection.
Object Detection
Quantum Physics
A coarse-to-fine framework for unsupervised multi-contrast MR image deformable registration with dual consistency constraint
Multi-contrast magnetic resonance (MR) image registration is useful in the clinic to achieve fast and accurate imaging-based disease diagnosis and treatment planning.
Edge Computing for Real-Time Near-Crash Detection for Smart Transportation Applications
It is among the first efforts in applying edge computing for real-time traffic video analytics and is expected to benefit multiple sub-fields in smart transportation research and applications.
The HW-TSC Video Speech Translation System at IWSLT 2020
The paper presents details of our system in the IWSLT Video Speech Translation evaluation.
Category-Specific CNN for Visual-aware CTR Prediction at JD.com
Existing algorithms usually extract visual features using off-the-shelf Convolutional Neural Networks (CNNs) and late fuse the visual and non-visual features for the finally predicted CTR.
GroupIM: A Mutual Information Maximization Framework for Neural Group Recommendation
We study the problem of making item recommendations to ephemeral groups, which comprise users with limited or no historical activities together.
Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain Recommendation
The rapid proliferation of new users and items on the social web has aggravated the gray-sheep user/long-tail item challenge in recommender systems.
Fashion Recommendation and Compatibility Prediction Using Relational Network
There are a few approaches that consider an entire outfit, but these approaches have limitations such as requiring rich semantic information, category labels, and fixed order of items.
Adversarial Light Projection Attacks on Face Recognition Systems: A Feasibility Study
While feasible, digital attacks have limited applicability in attacking deployed systems, including face recognition systems, where an adversary typically has access to the input and not the transmission channel.
Feedback Graph Convolutional Network for Skeleton-based Action Recognition
It transmits the high-level semantic features to the low-level layers and flows temporal information stage by stage to progressively model global spatial-temporal features for action recognition; (3) The FGCN model provides early predictions.
Ranked #28 on
Skeleton Based Action Recognition
on NTU RGB+D 120
Rethinking the Hyperparameters for Fine-tuning
Our findings challenge common practices of fine-tuning and encourages deep learning practitioners to rethink the hyperparameters for fine-tuning.
Multi-Task Incremental Learning for Object Detection
For the difficult cases, where the domain gaps and especially category differences are large, we explore three different exemplar sampling methods and show the proposed adaptive sampling method is effective to select diverse and informative samples from entire datasets, to further prevent forgetting.
FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping
We propose a novel attributes encoder for extracting multi-level target face attributes, and a new generator with carefully designed Adaptive Attentional Denormalization (AAD) layers to adaptively integrate the identity and the attributes for face synthesis.
Face X-ray for More General Face Forgery Detection
For this reason, face X-ray provides an effective way for detecting forgery generated by most existing face manipulation algorithms.
motif2vec: Motif Aware Node Representation Learning for Heterogeneous Networks
Supervised machine learning tasks in networks such as node classification and link prediction require us to perform feature engineering that is known and agreed to be the key to success in applied machine learning.
Detecting 11K Classes: Large Scale Object Detection without Fine-Grained Bounding Boxes
However, these methods require fully annotated object bounding boxes for training, which are incredibly hard to scale up due to the high annotation cost.
Position Focused Attention Network for Image-Text Matching
Then, an attention mechanism is proposed to model the relations between the image region and blocks and generate the valuable position feature, which will be further utilized to enhance the region expression and model a more reliable relationship between the visual image and the textual sentence.
X-Net: Brain Stroke Lesion Segmentation Based on Depthwise Separable Convolution and Long-range Dependencies
Recently, approaches based on deep learning and methods for contextual information extraction have served in many image segmentation tasks.
CLCI-Net: Cross-Level fusion and Context Inference Networks for Lesion Segmentation of Chronic Stroke
To address these challenges, this paper proposes a Cross-Level fusion and Context Inference Network (CLCI-Net) for the chronic stroke lesion segmentation from T1-weighted MR images.
Predicting Next-Season Designs on High Fashion Runway
Fashion is a large and fast-changing industry.
Face Parsing with RoI Tanh-Warping
It uses hierarchical local based method for inner facial components and global methods for outer facial components.
The Pupil Has Become the Master: Teacher-Student Model-Based Word Embedding Distillation with Ensemble Learning
Recent advances in deep learning have facilitated the demand of neural models for real applications.
Ranked #2 on
Sentiment Analysis
on MPQA
Mask-Guided Portrait Editing with Conditional GANs
Portrait editing is a popular subject in photo manipulation.
Real-Time Steganalysis for Stream Media Based on Multi-channel Convolutional Sliding Windows
To solve these two challenges, in this paper, combined with the sliding window detection algorithm and Convolution Neural Network we propose a real-time VoIP steganalysis method which based on multi-channel convolution sliding windows.
Dynamic Graph Representation Learning via Self-Attention Networks
Learning latent representations of nodes in graphs is an important and ubiquitous task with widespread applications such as link prediction, node classification, and graph visualization.
An End-to-End Multi-task Learning Model for Fact Checking
With huge amount of information generated every day on the web, fact checking is an important and challenging task which can help people identify the authenticity of most claims as well as providing evidences selected from knowledge source like Wikipedia.
Zero-Annotation Object Detection with Web Knowledge Transfer
Object detection is one of the major problems in computer vision, and has been extensively studied.
Exploiting Web Images for Weakly Supervised Object Detection
Object detection without bounding box annotations, i. e, weakly supervised detection methods, are still lagging far behind.
Ranked #17 on
Weakly Supervised Object Detection
on PASCAL VOC 2012 test
(using extra training data)
MIML-FCN+: Multi-instance Multi-label Learning via Fully Convolutional Networks with Privileged Information
As the proposed PI loss is convex and SGD compatible and the framework itself is a fully convolutional network, MIML-FCN+ can be easily integrated with state of-the-art deep learning networks.
Improving Multi-label Learning with Missing Labels by Structured Semantic Correlations
Experimental results demonstrate the effectiveness of the proposed semantic descriptor and the usefulness of incorporating the structured semantic correlations.
Efficient 3D Room Shape Recovery From a Single Panorama
We propose a method to recover the shape of a 3D room from a full-view indoor panorama.
A Comparative Study of Object Trackers for Infrared Flying Bird Tracking
Bird strikes present a huge risk for aircraft, especially since traditional airport bird surveillance is mainly dependent on inefficient human observation.
Exploit Bounding Box Annotations for Multi-label Object Recognition
With strong labels, our framework is able to achieve state-of-the-art results in both datasets.
Ranked #16 on
Multi-Label Classification
on PASCAL VOC 2007
A Parallel Way to Select the Parameters of SVM Based on the Ant Optimization Algorithm
A large number of experimental data shows that Support Vector Machine (SVM) algorithm has obvious advantages in text classification, handwriting recognition, image classification, bioinformatics, and some other fields.
