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Found 8 papers, 0 papers with code
LoopAnimate: Loopable Salient Object Animation
The proposed LoopAnimate, which for the first time extends the single-pass generation length of UNet-based video generation models to 35 frames while maintaining high-quality video generation.
Post-Training Attribute Unlearning in Recommender Systems
The first component is distinguishability loss, where we design a distribution-based measurement to make attribute labels indistinguishable from attackers.
A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methods
Automatic Text Summarization (ATS), utilizing Natural Language Processing (NLP) algorithms, aims to create concise and accurate summaries, thereby significantly reducing the human effort required in processing large volumes of text.
Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems
To address the PoT-AU problem in recommender systems, we design a two-component loss function that consists of i) distinguishability loss: making attribute labels indistinguishable from attackers, and ii) regularization loss: preventing drastic changes in the model that result in a negative impact on recommendation performance.
AttMOT: Improving Multiple-Object Tracking by Introducing Auxiliary Pedestrian Attributes
To the best of our knowledge, AttMOT is the first MOT dataset with semantic attributes.
Knowledge Federation: A Unified and Hierarchical Privacy-Preserving AI Framework
With strict protections and regulations of data privacy and security, conventional machine learning based on centralized datasets is confronted with significant challenges, making artificial intelligence (AI) impractical in many mission-critical and data-sensitive scenarios, such as finance, government, and health.
Ranking and Sampling in Open-Domain Question Answering
Open-domain question answering (OpenQA) aims to answer questions based on a number of unlabeled paragraphs.
Template-Instance Loss for Offline Handwritten Chinese Character Recognition
The long-standing challenges for offline handwritten Chinese character recognition (HCCR) are twofold: Chinese characters can be very diverse and complicated while similarly looking, and cursive handwriting (due to increased writing speed and infrequent pen lifting) makes strokes and even characters connected together in a flowing manner.
