Zobrazeno 1 - 10
of 104
pro vyhledávání: '"Chae, Dong Kyu"'
Reliable facial expression learning (FEL) involves the effective learning of distinctive facial expression characteristics for more reliable, unbiased and accurate predictions in real-life settings. However, current systems struggle with FEL tasks be
Externí odkaz:
http://arxiv.org/abs/2410.15927
While Large Language Models (LLM) have created a massive technological impact in the past decade, allowing for human-enabled applications, they can produce output that contains stereotypes and biases, especially when using low-resource languages. Thi
Externí odkaz:
http://arxiv.org/abs/2407.18376
3D single object tracking (SOT) methods based on appearance matching has long suffered from insufficient appearance information incurred by incomplete, textureless and semantically deficient LiDAR point clouds. While motion paradigm exploits motion c
Externí odkaz:
http://arxiv.org/abs/2407.05238
Publikováno v:
The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Knowledge Graphs (KGs) have proven essential in information processing and reasoning applications because they link related entities and give context-rich information, supporting efficient information retrieval and knowledge discovery; presenting inf
Externí odkaz:
http://arxiv.org/abs/2404.03528
Publikováno v:
LREC-COLING2024
Recently, sentiment-aware pre-trained language models (PLMs) demonstrate impressive results in downstream sentiment analysis tasks. However, they neglect to evaluate the quality of their constructed sentiment representations; they just focus on impro
Externí odkaz:
http://arxiv.org/abs/2404.01104
Examining Drug-Drug Interactions (DDIs) is a pivotal element in the process of drug development. DDIs occur when one drug's properties are affected by the inclusion of other drugs. Detecting favorable DDIs has the potential to pave the way for creati
Externí odkaz:
http://arxiv.org/abs/2403.17210
Publikováno v:
The Second Tiny Papers Track at {ICLR} 2024, Tiny Papers @ {ICLR} 2024, Vienna Austria, May 11, 2024
Complex chemical structures, like drugs, are usually defined by SMILES strings as a sequence of molecules and bonds. These SMILES strings are used in different complex machine learning-based drug-related research and representation works. Escaping fr
Externí odkaz:
http://arxiv.org/abs/2403.12984
Category-specific models are provenly valuable methods in 3D single object tracking (SOT) regardless of Siamese or motion-centric paradigms. However, such over-specialized model designs incur redundant parameters, thus limiting the broader applicabil
Externí odkaz:
http://arxiv.org/abs/2401.11204
Federated learning (FL) as distributed machine learning has gained popularity as privacy-aware Machine Learning (ML) systems have emerged as a technique that prevents privacy leakage by building a global model and by conducting individualized trainin
Externí odkaz:
http://arxiv.org/abs/2306.08402
ARBEx: Attentive Feature Extraction with Reliability Balancing for Robust Facial Expression Learning
Publikováno v:
ACCV 2024
In this paper, we introduce a framework ARBEx, a novel attentive feature extraction framework driven by Vision Transformer with reliability balancing to cope against poor class distributions, bias, and uncertainty in the facial expression learning (F
Externí odkaz:
http://arxiv.org/abs/2305.01486