Search Results for author:
Found 11 papers, 7 papers with code
Region-Based Optimization in Continual Learning for Audio Deepfake Detection
To address this issue, we propose a continual learning method named Region-Based Optimization (RegO) for audio deepfake detection.
PRECISE: Pre-training Sequential Recommenders with Collaborative and Semantic Information
Traditional pre-trained recommendation models mainly capture user interests by leveraging collaborative signals.
Reject Threshold Adaptation for Open-Set Model Attribution of Deepfake Audio
To address the issues, we propose a novel framework for open set model attribution of deepfake audio with rejection threshold adaptation (ReTA).
Open Vocabulary Monocular 3D Object Detection
In this work, we pioneer the study of open-vocabulary monocular 3D object detection, a novel task that aims to detect and localize objects in 3D space from a single RGB image without limiting detection to a predefined set of categories.
ADD 2023: Towards Audio Deepfake Detection and Analysis in the Wild
The growing prominence of the field of audio deepfake detection is driven by its wide range of applications, notably in protecting the public from potential fraud and other malicious activities, prompting the need for greater attention and research in this area.
MER 2024: Semi-Supervised Learning, Noise Robustness, and Open-Vocabulary Multimodal Emotion Recognition
However, this process may lead to inaccurate annotations, such as ignoring non-majority or non-candidate labels.
GPT-4V with Emotion: A Zero-shot Benchmark for Generalized Emotion Recognition
To bridge this gap, we present the quantitative evaluation results of GPT-4V on 21 benchmark datasets covering 6 tasks: visual sentiment analysis, tweet sentiment analysis, micro-expression recognition, facial emotion recognition, dynamic facial emotion recognition, and multimodal emotion recognition.
REST: Debiased Social Recommendation via Reconstructing Exposure Strategies
By explicitly Reconstructing Exposure STrategies (REST in short), we formalize the recommendation problem as the counterfactual reasoning and propose the debiased social recommendation method.
TEA: A Sequential Recommendation Framework via Temporally Evolving Aggregations
To achieve this, we firstly formalize sequential recommendation as a problem to estimate conditional probability given temporal dynamic heterogeneous graphs and user behavior sequences.
SocialTrans: A Deep Sequential Model with Social Information for Web-Scale Recommendation Systems
Experiments on three data sets verify the effectiveness of our model and show that it outperforms state-of-the-art social recommendation methods.
Semantic Product Search
To address these issues, we train a deep learning model for semantic matching using customer behavior data.
