DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention | Paper Citation Record

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention Flamingo: a visual language model for few-shot learning

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention Emergent Visual-Semantic Hierarchies in Image-Text Representations

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention Emerg- ing properties in self-supervised vision transformers

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention SoGAR: Self-supervised Spatiotemporal Attention-based Social Group Activity Recognition

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention Spartan: Self-supervised spatiotemporal transformers ap- proach to group activity recognition

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention Advanced deep learning techniques for tobacco usage assessment in tiktok videos

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention React: Recognize every action everywhere all at once

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention Public health advocacy dataset: A dataset of tobacco usage videos from social media

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention Hatt- flow: Hierarchical attention-flow mechanism for group- activity scene graph generation in videos

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention Feddat: An approach for foundation model finetuning in multi-modal heterogeneous federated learning

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention Improved baselines with momentum contrastive learning

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention Multiscale Vision Transformers

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention Multi-modal Transfer Learning between Biological Foundation Models

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention Deep residual learning for image recognition

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention Momentum contrast for unsupervised visual rep- resentation learning

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention Masked autoencoders are scalable vision learners

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention Scaling up visual and vision-language representa- tion learning with noisy text supervision

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention Mdetr- modulated detection for end-to-end multi-modal understand- ing

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention Grounding Foundation Models through Federated Transfer Learning: A General Framework

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention Ma- chine learning models of tobacco susceptibility and current use among adolescents from 97 countries in the global youth tobacco survey, 2013-2017

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention Understanding e-cigarette con- tent and promotion on youtube through machine learning

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention A multimodal deep learning architecture for smoking detec- tion with a small data approach

Reference 24

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention Visual instruction tuning

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention Decoupled Weight Decay Regularization

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention SGDR: Stochastic Gradient Descent with Warm Restarts

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention Visual relationship detection with language priors

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention MolFM: A Multimodal Molecular Foundation Model

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention A scalable hierarchical distributed language model

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention Influence of user profile attributes on e-cigarette– related searches on youtube: Machine learning clustering and classification

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention Using Computer Vision to Detect E-cigarette Con- tent in TikTok Videos

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention Insect- foundation: A foundation model and large-scale 1m dataset for visual insect understanding

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention Type-to-track: Retrieve any object via prompt-based track- ing

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention Classification of lapses in smokers attempting to stop: A su- pervised machine learning approach using data from a pop- ular smoking cessation smartphone app

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention Learning transferable visual models from natural language supervi- sion

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention Efficientnet: Rethinking model scaling for convolutional neural networks

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention Learning to compose dynamic tree structures for visual contexts

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention Scalable Surveil- lance of E-Cigarette Products on Instagram and TikTok Us- ing Computer Vision

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention ActionCLIP: A New Paradigm for Video Action Recognition

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention Ip102: A large-scale benchmark dataset for insect pest recognition

Reference 43

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention mplug-2: A modularized multi-modal foundation model across text, image and video

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention Seed the views: Hi- erarchical semantic alignment for contrastive representation learning

Reference 45

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention Linguistic structures as weak supervision for visual scene graph generation

Reference 46

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention CoCa: Contrastive Captioners are Image-Text Foundation Models

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention Bridging knowledge graphs to generate scene graphs

Reference 48

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention Learning visual commonsense for robust scene graph 10 generation

Reference 49

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention Learning human action recognition representations without real humans

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This paper cites Learning to generate scene graph from natural language supervision.

DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention Learning to generate scene graph from natural language supervision

Reference 51

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DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention MiniGPT-4: Enhancing vision-language understanding with advanced large language models

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