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Found 8 papers, 3 papers with code
Simplifying CLIP: Unleashing the Power of Large-Scale Models on Consumer-level Computers
Contrastive Language-Image Pre-training (CLIP) has attracted a surge of attention for its superior zero-shot performance and excellent transferability to downstream tasks.
KUNPENG: An Embodied Large Model for Intelligent Maritime
Intelligent maritime, as an essential component of smart ocean construction, deeply integrates advanced artificial intelligence technology and data analysis methods, which covers multiple aspects such as smart vessels, route optimization, safe navigation, aiming to enhance the efficiency of ocean resource utilization and the intelligence of transportation networks.
PTM-VQA: Efficient Video Quality Assessment Leveraging Diverse PreTrained Models from the Wild
In this paper, we propose a VQA method named PTM-VQA, which leverages PreTrained Models to transfer knowledge from models pretrained on various pre-tasks, enabling benefits for VQA from different aspects.
Ada-DQA: Adaptive Diverse Quality-aware Feature Acquisition for Video Quality Assessment
Video quality assessment (VQA) has attracted growing attention in recent years.
A Self-adaptive Neuroevolution Approach to Constructing Deep Neural Network Architectures Across Different Types
One of the key settings in SANE is the search space defined by cells and organs self-adapted to different DNN types.
Weighted Concordance Index Loss-based Multimodal Survival Modeling for Radiation Encephalopathy Assessment in Nasopharyngeal Carcinoma Radiotherapy
We design a deep multimodal survival network (MSN) with two feature extractors to learn discriminative features from multimodal data.
Siamese Encoder-based Spatial-Temporal Mixer for Growth Trend Prediction of Lung Nodules on CT Scans
In the management of lung nodules, we are desirable to predict nodule evolution in terms of its diameter variation on Computed Tomography (CT) scans and then provide a follow-up recommendation according to the predicted result of the growing trend of the nodule.
Hybrid Genetic Algorithm for Cloud Computing Applications
In this paper with the aid of genetic algorithm and fuzzy theory, we present a hybrid job scheduling approach, which considers the load balancing of the system and reduces total execution time and execution cost.
