Results 101 to 110 of about 59,151 (296)
TARN: Temporal Attentive Relation Network for Few-Shot and Zero-Shot Action Recognition [PDF]
In this paper we propose a novel Temporal Attentive Relation Network (TARN) for the problems of few-shot and zero-shot action recognition. At the heart of our network is a meta-learning approach that learns to compare representations of variable temporal
Bishay, M +3 more
core
We analyze cisplatin–DNA adducts (CDAs) and double‐strand breaks (DSBs) in a cell‐cycle‐dependent manner. We find that CDAs form similarly across all cell cycle phases. DSBs arise only in S‐phase. CDAs might not directly impair DSB repair, but S‐phase DSB lesions evolve in the presence of CDAs and disrupt repair in G2, also causing radiosensitization ...
Ye Qiu +10 more
wiley +1 more source
Few-shot action recognition in video method based on continuous fame information fusion modeling
ObjectivesTo overcome the limitations of existing few-shot video action recognition methods in capturing global spatiotemporal information and modeling complex behaviors, a new network architecture was developed to significantly enhances the accuracy and
Zhang Bingbing +3 more
doaj +1 more source
Self-regularized prototypical network for few-shot semantic segmentation
The deep CNNs in image semantic segmentation typically require a large number of densely-annotated images for training and have difficulties in generalizing to unseen object categories.
Zhang, Hui, Jiang, Xudong, Ding, Henghui
core +1 more source
MITF maintains genome stability in nonmelanocyte lineages
MITF is essential for melanocyte survival and acts as an oncogene in 10%–20% of melanomas. We show that MITF depletion causes genome instability in nonmelanocytic cells, leading to LATS2‐mediated P53 activation, cell cycle arrest, and apoptosis. This study highlights the role of MITF as a genome maintenance factor beyond the melanocyte lineage. Created
Drifa H. Gudmundsdottir +13 more
wiley +1 more source
Visual‑Semantic Enhanced Joint Classifier for Few‑Shot Open‑Set Recognition
This paper investigates the potential of the vision‑language pretrained model contrastive language-image pre-training(CLIP) in few‑shot open‑set recognition (FSOR).
DING Xiangshu +2 more
doaj +1 more source
Driver activity recognition using few-shot learning techniques
In the realm of computer vision, activity recognition from videos has become increasingly crucial due to its applications in surveillance, healthcare, and autonomous systems.
Elmaraghy, Youssef
core +1 more source
Oncogenic DMTF1β promotes cancer cell motility by regulating autophagy through ULK1 stabilization
In the current study, we demonstrate that the oncogene DMTF1β regulates ULK1 stability by reducing its proteasomal degradation in cancer cells. This stabilization enables ULK1 to induce autophagy, which in turn facilitates cancer cell migration. Consequently, reduced DMTF1β levels lead to decreased autophagy and impaired cancer cell migration.
Jun Xu +13 more
wiley +1 more source
Few-shot disease recognition algorithm based on supervised contrastive learning
Diseases cause crop yield reduction and quality decline, which has a great impact on agricultural production. Plant disease recognition based on computer vision can help farmers quickly and accurately recognize diseases.
Jiawei Mu +5 more
doaj +1 more source
Loss of proton‐sensing TDAG8 increases tumor progression in mouse models of colon cancer
Loss of the pH‐sensing receptor TDAG8 accelerates colorectal cancer progression in mice. Animals lacking TDAG8 expression had increased tumor growth, DNA damage, and recruitment of tumor‐associated immune cells, including macrophages, neutrophils, and monocytes.
Ermanno Malagola +11 more
wiley +1 more source

