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Found 23 papers, 2 papers with code
Prompting for Multimodal Hateful Meme Classification
Specifically, we construct simple prompts and provide a few in-context examples to exploit the implicit knowledge in the pre-trained RoBERTa language model for hateful meme classification.
A Coarse-to-Fine Approach for Urban Land Use Mapping Based on Multisource Geospatial Data
The results show that the proposed approach can significantly outperform baseline method that mixes built-up and non-built-up regions, with accuracy increase of 25% and 30% for level-1 and level-2 classification, respectively.
Sim-to-Real 6D Object Pose Estimation via Iterative Self-training for Robotic Bin Picking
To continuously improve the quality of pseudo labels, we iterate the above steps by taking the trained student model as a new teacher and re-label real data using the refined teacher model.
Towards Robust Part-aware Instance Segmentation for Industrial Bin Picking
Industrial bin picking is a challenging task that requires accurate and robust segmentation of individual object instances.
Exploring the Impact of Negative Samples of Contrastive Learning: A Case Study of Sentence Embedding
We define a maximum traceable distance metric, through which we learn to what extent the text contrastive learning benefits from the historical information of negative samples.
ESOD:Edge-based Task Scheduling for Object Detection
In detail, we train a DNN model (termed as pre-model) to predict which object detection model to use for the coming task and offloads to which edge servers by physical characteristics of the image task (e. g., brightness, saturation).
Spatio-temporal Parking Behaviour Forecasting and Analysis Before and During COVID-19
Parking demand forecasting and behaviour analysis have received increasing attention in recent years because of their critical role in mitigating traffic congestion and understanding travel behaviours.
Disentangling Hate in Online Memes
Our experiment results show that DisMultiHate is able to outperform state-of-the-art unimodal and multimodal baselines in the hateful meme classification task.
An End-to-End and Accurate PPG-based Respiratory Rate Estimation Approach Using Cycle Generative Adversarial Networks
In this paper, we present an end-to-end and accurate pipeline for RR estimation using Cycle Generative Adversarial Networks (CycleGAN) to reconstruct respiratory signals from raw PPG signals.
Learning to Remove: Towards Isotropic Pre-trained BERT Embedding
We train the weights on word similarity tasks and show that processed embedding is more isotropic.
DeepHate: Hate Speech Detection via Multi-Faceted Text Representations
Online hate speech is an important issue that breaks the cohesiveness of online social communities and even raises public safety concerns in our societies.
AngryBERT: Joint Learning Target and Emotion for Hate Speech Detection
Automated hate speech detection in social media is a challenging task that has recently gained significant traction in the data mining and Natural Language Processing community.
HateGAN: Adversarial Generative-Based Data Augmentation for Hate Speech Detection
We also conduct case studies to empirically examine the HateGAN generated hate speeches and show that the generated tweets are diverse, coherent, and relevant to hate speech detection.
Deep Learning based Monocular Depth Prediction: Datasets, Methods and Applications
Despite the rapid progress in this topic, there are lacking of a comprehensive review, which is needed to summarize the current progress and provide the future directions.
On Analyzing Antisocial Behaviors Amid COVID-19 Pandemic
The COVID-19 pandemic has developed to be more than a bio-crisis as global news has reported a sharp rise in xenophobia and discrimination in both online and offline communities.
MiniNet: An extremely lightweight convolutional neural network for real-time unsupervised monocular depth estimation
To the best of our knowledge, this work is the first extremely lightweight neural network trained on monocular video sequences for real-time unsupervised monocular depth estimation, which opens up the possibility of implementing deep learning-based real-time unsupervised monocular depth prediction on low-cost embedded devices.
On Analyzing Annotation Consistency in Online Abusive Behavior Datasets
In this study, we proposed an analytical framework to study the annotation consistency in online hate and abusive content datasets.
Vispi: Automatic Visual Perception and Interpretation of Chest X-rays
Medical imaging contains the essential information for rendering diagnostic and treatment decisions.
Enhancing Remote Sensing Image Retrieval with Triplet Deep Metric Learning Network
With the rapid growing of remotely sensed imagery data, there is a high demand for effective and efficient image retrieval tools to manage and exploit such data.
The Limits of Morality in Strategic Games
A coalition is blameable for an outcome if the coalition had a strategy to prevent it.
Relative Geometry-Aware Siamese Neural Network for 6DOF Camera Relocalization
6DOF camera relocalization is an important component of autonomous driving and navigation.
