Search Results for author:
Found 34 papers, 14 papers with code
A General and Flexible Multi-concept Parsing Framework for Multilingual Semantic Matching
Although DC-Match is a simple yet effective method for semantic matching, it highly depends on the external NER techniques to identify the keywords of sentences, which limits the performance of semantic matching for minor languages since satisfactory NER tools are usually hard to obtain.
Learning the Unlearned: Mitigating Feature Suppression in Contrastive Learning
Self-Supervised Contrastive Learning has proven effective in deriving high-quality representations from unlabeled data.
Joint Multi-Facts Reasoning Network For Complex Temporal Question Answering Over Knowledge Graph
Temporal Knowledge Graph (TKG) is an extension of regular knowledge graph by attaching the time scope.
Improving Low-resource Prompt-based Relation Representation with Multi-view Decoupling Learning
Recently, prompt-tuning with pre-trained language models (PLMs) has demonstrated the significantly enhancing ability of relation extraction (RE) tasks.
Mirror: A Universal Framework for Various Information Extraction Tasks
Sharing knowledge between information extraction tasks has always been a challenge due to the diverse data formats and task variations.
Unified Multi-modal Unsupervised Representation Learning for Skeleton-based Action Understanding
In this manner, our framework is able to learn the unified representations of uni-modal or multi-modal skeleton input, which is flexible to different kinds of modality input for robust action understanding in practical cases.
MIRACLE: Towards Personalized Dialogue Generation with Latent-Space Multiple Personal Attribute Control
Subsequently, we employ a conditional variational auto-encoder to align with the dense personalized responses within a latent joint attribute space.
TREA: Tree-Structure Reasoning Schema for Conversational Recommendation
TREA constructs a multi-hierarchical scalable tree as the reasoning structure to clarify the causal relationships between mentioned entities, and fully utilizes historical conversations to generate more reasonable and suitable responses for recommended results.
From Region to Patch: Attribute-Aware Foreground-Background Contrastive Learning for Fine-Grained Fashion Retrieval
As the attribute-specific similarity typically corresponds to the specific subtle regions of images, we propose a Region-to-Patch Framework (RPF) that consists of a region-aware branch and a patch-aware branch to extract fine-grained attribute-related visual features for precise retrieval in a coarse-to-fine manner.
Transform-Equivariant Consistency Learning for Temporal Sentence Grounding
This paper addresses the temporal sentence grounding (TSG).
A Survey on Arabic Named Entity Recognition: Past, Recent Advances, and Future Trends
In this paper, we provide a comprehensive review of the development of Arabic NER, especially the recent advances in deep learning and pre-trained language model.
Dual Learning with Dynamic Knowledge Distillation for Partially Relevant Video Retrieval
During the knowledge distillation, an inheritance student branch is devised to absorb the knowledge from the teacher model.
Distantly-Supervised Named Entity Recognition with Adaptive Teacher Learning and Fine-grained Student Ensemble
Distantly-Supervised Named Entity Recognition (DS-NER) effectively alleviates the data scarcity problem in NER by automatically generating training samples.
Reducing the Vision and Language Bias for Temporal Sentence Grounding
In this paper, we study the above issue of selection biases and accordingly propose a Debiasing-TSG (D-TSG) model to filter and remove the negative biases in both vision and language modalities for enhancing the model generalization ability.
Delving Deep into Regularity: A Simple but Effective Method for Chinese Named Entity Recognition
Recent years have witnessed the improving performance of Chinese Named Entity Recognition (NER) from proposing new frameworks or incorporating word lexicons.
Chinese Named Entity Recognition
named-entity-recognition
+3
Reading-strategy Inspired Visual Representation Learning for Text-to-Video Retrieval
In this work, we concentrate on video representation learning, an essential component for text-to-video retrieval.
Unsupervised Temporal Video Grounding with Deep Semantic Clustering
Temporal video grounding (TVG) aims to localize a target segment in a video according to a given sentence query.
Memory-Guided Semantic Learning Network for Temporal Sentence Grounding
To tackle this issue, we propose a memory-augmented network, called Memory-Guided Semantic Learning Network (MGSL-Net), that learns and memorizes the rarely appeared content in TSG tasks.
Exploring Motion and Appearance Information for Temporal Sentence Grounding
Then, we develop separate motion and appearance branches to learn motion-guided and appearance-guided object relations, respectively.
Efficient Document-level Event Extraction via Pseudo-Trigger-aware Pruned Complete Graph
Most previous studies of document-level event extraction mainly focus on building argument chains in an autoregressive way, which achieves a certain success but is inefficient in both training and inference.
Ranked #3 on
Document-level Event Extraction
on ChFinAnn
Progressively Guide to Attend: An Iterative Alignment Framework for Temporal Sentence Grounding
A key solution to temporal sentence grounding (TSG) exists in how to learn effective alignment between vision and language features extracted from an untrimmed video and a sentence description.
Adaptive Proposal Generation Network for Temporal Sentence Localization in Videos
However, the performance of bottom-up model is inferior to the top-down counterpart as it fails to exploit the segment-level interaction.
Coarse to Fine: Domain Adaptive Crowd Counting via Adversarial Scoring Network
In specific, at the coarse-grained stage, we design a dual-discriminator strategy to adapt source domain to be close to the targets from the perspectives of both global and local feature space via adversarial learning.
Context-aware Biaffine Localizing Network for Temporal Sentence Grounding
This paper addresses the problem of temporal sentence grounding (TSG), which aims to identify the temporal boundary of a specific segment from an untrimmed video by a sentence query.
Hierarchical Similarity Learning for Language-based Product Image Retrieval
In this paper, we focus on the cross-modal similarity measurement, and propose a novel Hierarchical Similarity Learning (HSL) network.
Progressive Localization Networks for Language-based Moment Localization
The language-based setting of this task allows for an open set of target activities, resulting in a large variation of the temporal lengths of video moments.
Read, Retrospect, Select: An MRC Framework to Short Text Entity Linking
Entity linking (EL) for the rapidly growing short text (e. g. search queries and news titles) is critical to industrial applications.
Reasoning Step-by-Step: Temporal Sentence Localization in Videos via Deep Rectification-Modulation Network
In this paper, we propose a novel deep rectification-modulation network (RMN), transforming this task into a multi-step reasoning process by repeating rectification and modulation.
Fine-grained Iterative Attention Network for TemporalLanguage Localization in Videos
In this paper, we propose a Fine-grained Iterative Attention Network (FIAN) that consists of an iterative attention module for bilateral query-video in-formation extraction.
Jointly Cross- and Self-Modal Graph Attention Network for Query-Based Moment Localization
To this end, we propose a novel Cross- and Self-Modal Graph Attention Network (CSMGAN) that recasts this task as a process of iterative messages passing over a joint graph.
Enhanced 3D convolutional networks for crowd counting
Recently, convolutional neural networks (CNNs) are the leading defacto method for crowd counting.
Attend To Count: Crowd Counting with Adaptive Capacity Multi-scale CNNs
ACM-CNN consists of three types of modules: a coarse network, a fine network, and a smooth network.
Adversarial Category Alignment Network for Cross-domain Sentiment Classification
Cross-domain sentiment classification aims to predict sentiment polarity on a target domain utilizing a classifier learned from a source domain.
