Specifically, the lack of corpus, sparsity of toxicity in enterprise emails, and well-defined criteria for annotating toxic conversations have prevented researchers from addressing the problem at scale.
In this study, the structural problems of the YOLOv5 model were analyzed emphatically.
Maintaining inference speeds remarkably similar to those of the YOLOv5 model, the large, medium, and small YOLOCS models surpass the YOLOv5 model's AP by 1. 1%, 2. 3%, and 5. 2%, respectively.
To this end, we, in this paper, propose a novel deep lifelong cross-modal hashing to achieve lifelong hashing retrieval instead of re-training hash function repeatedly when new data arrive.
Since their binarization processes are not a component of the network, the learning-based binary descriptor cannot fully utilize the advances of deep learning.
Inspired by the plain contrast idea, MCF introduces two different subnets to explore and utilize the discrepancies between subnets to correct cognitive bias of the model.
no code implementations • 10 Jan 2022 • Lei LI, Fuping Wu, Sihan Wang, Xinzhe Luo, Carlos Martin-Isla, Shuwei Zhai, Jianpeng Zhang, Yanfei Liu7, Zhen Zhang, Markus J. Ankenbrand, Haochuan Jiang, Xiaoran Zhang, Linhong Wang, Tewodros Weldebirhan Arega, Elif Altunok, Zhou Zhao, Feiyan Li, Jun Ma, Xiaoping Yang, Elodie Puybareau, Ilkay Oksuz, Stephanie Bricq, Weisheng Li, Kumaradevan Punithakumar, Sotirios A. Tsaftaris, Laura M. Schreiber, Mingjing Yang, Guocai Liu, Yong Xia, Guotai Wang, Sergio Escalera, Xiahai Zhuang
Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on myocardium is the key to this assessment.
We first evaluate Poolingformer on two long sequence QA tasks: the monolingual NQ and the multilingual TyDi QA.
In this paper, a color-related local binary pattern (cLBP) which learns the dominant patterns from the decoded LBP is proposed for color images recognition.
The unfixed encoder autonomously learns the image fingerprints that differentiate between the tampered and non-tampered regions, whereas the fixed encoder intentionally provides the direction information that assists the learning and detection of the network.
The core idea of the RRU-Net is to strengthen the learning way of CNN, which is inspired by the recall and the consolidation mechanism of the human brain and implemented by the propagation and the feedback process of the residual in CNN.
For example, the representation in sparse representation-based classification (SRC) uses L1 norm penalty as regularization, which is equal to lasso.