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Found 24 papers, 9 papers with code
基于HowNet的无监督汉语动词隐喻识别方法(Unsupervised Chinese Verb Metaphor Recognition Method Based on HowNet)
“隐喻是人类思维和语言理解的核心问题。随着互联网发展和海量文本出现, 利用自然语言处理技术对隐喻文本进行自动识别成为一种迫切的需求。但是目前在汉语隐喻识别研究中, 由于语义资源有限, 导致模型容易过拟合。此外, 主流隐喻识别方法存在可解释性差的缺点。针对上述问题, 本文构建了一个规模较大的汉语动词隐喻数据集, 并且提出了一种基于HowNet的无监督汉语动词隐喻识别模型。实验结果表明, 本文提出的模型能够有效地应用于动词隐喻识别任务, 识别效果超过了对比的无监督模型;并且, 与其它用于隐喻识别的深度学习模型相比, 本文模型具有结构简单、参数少、可解释性强的优点。”
Enhancing Textual Personality Detection toward Social Media: Integrating Long-term and Short-term Perspectives
This limitation hinders a comprehensive understanding of individuals' personalities, as both stable traits and dynamic states are vital.
CoLafier: Collaborative Noisy Label Purifier With Local Intrinsic Dimensionality Guidance
CoLafier consists of two subnets: LID-dis and LID-gen. LID-dis is a specialized classifier.
Image Restoration Through Generalized Ornstein-Uhlenbeck Bridge
Diffusion models possess powerful generative capabilities enabling the mapping of noise to data using reverse stochastic differential equations.
UCE-FID: Using Large Unlabeled, Medium Crowdsourced-Labeled, and Small Expert-Labeled Tweets for Foodborne Illness Detection
To overcome these challenges, we propose EGAL, a deep learning framework for foodborne illness detection that uses small expert-labeled tweets augmented by crowdsourced-labeled and massive unlabeled data.
FATA-Trans: Field And Time-Aware Transformer for Sequential Tabular Data
FATA-Trans is field- and time-aware for sequential tabular data.
EVE: Efficient Vision-Language Pre-training with Masked Prediction and Modality-Aware MoE
Owing to the combination of the unified architecture and pre-training task, EVE is easy to scale up, enabling better downstream performance with fewer resources and faster training speed.
TWEET-FID: An Annotated Dataset for Multiple Foodborne Illness Detection Tasks
To accelerate the development of machine learning-based models for foodborne outbreak detection, we thus present TWEET-FID (TWEET-Foodborne Illness Detection), the first publicly available annotated dataset for multiple foodborne illness incident detection tasks.
Surf or sleep? Understanding the influence of bedtime patterns on campus
In this paper, we develop a general data-driven method for identifying students' sleep patterns according to their Internet access pattern stored in the education management system and explore its influence from various aspects.
Enhancing Prototypical Few-Shot Learning by Leveraging the Local-Level Strategy
Aiming at recognizing the samples from novel categories with few reference samples, few-shot learning (FSL) is a challenging problem.
Instant Response Few-shot Object Detection with Meta Strategy and Explicit Localization Inference
Specifically, we first propose two simple but effective meta-strategies for the box classifier and RPN module to enable the object detection of novel categories with instant response.
Ranked #17 on
Few-Shot Object Detection
on MS-COCO (10-shot)
MultiMET: A Multimodal Dataset for Metaphor Understanding
Metaphor involves not only a linguistic phenomenon, but also a cognitive phenomenon structuring human thought, which makes understanding it challenging.
Transferable, Controllable, and Inconspicuous Adversarial Attacks on Person Re-identification With Deep Mis-Ranking
To examine the robustness of ReID systems is rather important because the insecurity of ReID systems may cause severe losses, e. g., the criminals may use the adversarial perturbations to cheat the CCTV systems.
Graduate Employment Prediction with Bias
The failure of landing a job for college students could cause serious social consequences such as drunkenness and suicide.
Telling the Whole Story: A Manually Annotated Chinese Dataset for the Analysis of Humor in Jokes
To the best of our knowledge, we are the first to approach humor annotation for exploring the underlying mechanism of the use of humor, which may contribute to a significantly deeper analysis of humor.
VisDrone-DET2019: The Vision Meets Drone Object Detection in Image Challenge Results
Results of 33 object detection algorithms are presented.
A Knowledge Graph Based Solution for Entity Discovery and Linking in Open-Domain Questions
In Question Entity Discovery and Linking (QEDL) problem, traditional methods are challenged because multiple entities in one short question are difficult to be discovered entirely and the incomplete information in short text makes entity linking hard to implement.
WECA: A WordNet-Encoded Collocation-Attention Network for Homographic Pun Recognition
In this work, we first use WordNet to understand and expand word embedding for settling the polysemy of homographic puns, and then propose a WordNet-Encoded Collocation-Attention network model (WECA) which combined with the context weights for recognizing the puns.
Construction of a Chinese Corpus for the Analysis of the Emotionality of Metaphorical Expressions
However, there is little research on the construction of metaphor corpora annotated with emotion for the analysis of emotionality of metaphorical expressions.
Cost-effective Object Detection: Active Sample Mining with Switchable Selection Criteria
The proposed process can be compatible with mini-batch based training (i. e., using a batch of unlabeled or partially labeled data as a one-time input) for object detection.
Towards Human-Machine Cooperation: Self-supervised Sample Mining for Object Detection
Though quite challenging, leveraging large-scale unlabeled or partially labeled images in a cost-effective way has increasingly attracted interests for its great importance to computer vision.
Content-Adaptive Sketch Portrait Generation by Decompositional Representation Learning
Sketch portrait generation benefits a wide range of applications such as digital entertainment and law enforcement.
Look into Person: Self-supervised Structure-sensitive Learning and A New Benchmark for Human Parsing
Human parsing has recently attracted a lot of research interests due to its huge application potentials.
Ranked #13 on
Semantic Segmentation
on LIP val
Cost-Effective Active Learning for Deep Image Classification
In this paper, we propose a novel active learning framework, which is capable of building a competitive classifier with optimal feature representation via a limited amount of labeled training instances in an incremental learning manner.
