281 research outputs found
Attention Consistency Refined Masked Frequency Forgery Representation for Generalizing Face Forgery Detection
Due to the successful development of deep image generation technology, visual
data forgery detection would play a more important role in social and economic
security. Existing forgery detection methods suffer from unsatisfactory
generalization ability to determine the authenticity in the unseen domain. In
this paper, we propose a novel Attention Consistency Refined masked frequency
forgery representation model toward generalizing face forgery detection
algorithm (ACMF). Most forgery technologies always bring in high-frequency
aware cues, which make it easy to distinguish source authenticity but difficult
to generalize to unseen artifact types. The masked frequency forgery
representation module is designed to explore robust forgery cues by randomly
discarding high-frequency information. In addition, we find that the forgery
attention map inconsistency through the detection network could affect the
generalizability. Thus, the forgery attention consistency is introduced to
force detectors to focus on similar attention regions for better generalization
ability. Experiment results on several public face forgery datasets
(FaceForensic++, DFD, Celeb-DF, and WDF datasets) demonstrate the superior
performance of the proposed method compared with the state-of-the-art methods.Comment: The source code and models are publicly available at
https://github.com/chenboluo/ACM
Enhancing traditional Chinese medicine diagnostics: Integrating ontological knowledge for multi-label symptom entity classification
In traditional Chinese medicine (TCM), artificial intelligence (AI)-assisted syndrome differentiation and disease diagnoses primarily confront the challenges of accurate symptom identification and classification. This study introduces a multi-label entity extraction model grounded in TCM symptom ontology, specifically designed to address the limitations of existing entity recognition models characterized by limited label spaces and an insufficient integration of domain knowledge. This model synergizes a knowledge graph with the TCM symptom ontology framework to facilitate a standardized symptom classification system and enrich it with domain-specific knowledge. It innovatively merges the conventional bidirectional encoder representations from transformers (BERT) + bidirectional long short-term memory (Bi-LSTM) + conditional random fields (CRF) entity recognition methodology with a multi-label classification strategy, thereby adeptly navigating the intricate label interdependencies in the textual data. Introducing a multi-associative feature fusion module is a significant advancement, thereby enabling the extraction of pivotal entity features while discerning the interrelations among diverse categorical labels. The experimental outcomes affirm the model's superior performance in multi-label symptom extraction and substantially elevates the efficiency and accuracy. This advancement robustly underpins research in TCM syndrome differentiation and disease diagnoses
Black Phosphorus Q-Switched Large-Mode-Area Tm-Doped Fiber Laser
We report on a passively Q-switched fiber laser with black phosphorus as saturable absorber. By employing the sol-gel fabricated large-mode-area Tm-doped fiber as gain medium, a high-energy Q-switched fiber laser has been demonstrated which delivers the maximum pulse energy of 11.72 μJ with the pulse width of 660 ns at the wavelength of 1954 nm. Our experimental results indicate that BP Q-switched large-mode-area Tm-doped fiber laser is an effective and reliable approach to generate high-energy pulses at 2 μm
- …