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Found 36 papers, 11 papers with code
Adapting Segment Anything Model for Unseen Object Instance Segmentation
Unseen Object Instance Segmentation (UOIS) is crucial for autonomous robots operating in unstructured environments.
MCDGLN: Masked Connection-based Dynamic Graph Learning Network for Autism Spectrum Disorder
We then employ a specialized weighted edge aggregation (WEA) module, which uses the cross convolution with channel-wise element-wise convolutional kernel, to integrate dynamic functional connectivity and to isolating task-relevant connections.
Progressive Residual Extraction based Pre-training for Speech Representation Learning
In this way, we can progressively extract pitch variation, speaker, and content representations from the input speech.
An Evaluation of Standard Statistical Models and LLMs on Time Series Forecasting
This research examines the use of Large Language Models (LLMs) in predicting time series, with a specific focus on the LLMTIME model.
SR-Mamba: Effective Surgical Phase Recognition with State Space Model
Inspired by the recent success of Mamba, a state space model with linear scalability in sequence length, this paper presents SR-Mamba, a novel attention-free model specifically tailored to meet the challenges of surgical phase recognition.
FANFOLD: Graph Normalizing Flows-driven Asymmetric Network for Unsupervised Graph-Level Anomaly Detection
We introduce normalizing flows to unsupervised graph-level anomaly detection due to their successful application and superior quality in learning the underlying distribution of samples.
Modularized Networks for Few-shot Hateful Meme Detection
We then use the few available annotated samples to train a module composer, which assigns weights to the LoRA modules based on their relevance.
Knowledge Generation for Zero-shot Knowledge-based VQA
Previous solutions to knowledge-based visual question answering~(K-VQA) retrieve knowledge from external knowledge bases and use supervised learning to train the K-VQA model.
Recent Advances in Hate Speech Moderation: Multimodality and the Role of Large Models
In the evolving landscape of online communication, moderating hate speech (HS) presents an intricate challenge, compounded by the multimodal nature of digital content.
A Refining Underlying Information Framework for Monaural Speech Enhancement
By bridging the speech enhancement and the Information Bottleneck principle in this letter, we rethink a universal plug-and-play strategy and propose a Refining Underlying Information framework called RUI to rise to the challenges both in theory and practice.
MATK: The Meme Analytical Tool Kit
The rise of social media platforms has brought about a new digital culture called memes.
Pro-Cap: Leveraging a Frozen Vision-Language Model for Hateful Meme Detection
Specifically, we prompt a frozen PVLM by asking hateful content-related questions and use the answers as image captions (which we call Pro-Cap), so that the captions contain information critical for hateful content detection.
Ranked #10 on
Meme Classification
on Hateful Memes
Modularized Zero-shot VQA with Pre-trained Models
We propose a modularized zero-shot network that explicitly decomposes questions into sub reasoning steps and is highly interpretable.
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.
Ranked #3 on
Hateful Meme Classification
on HarMeme
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.
Ranked #4 on
Hateful Meme Classification
on HarMeme
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.
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.
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.
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.
Ranked #5 on
Semantic Segmentation
on SpectralWaste
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.
