“‘Video Analysis’ Tag”,2019-12-29 (anime AI, CLIP, AI scaling, RL)⁠:

Bibliography for tag ai/video/analysis, most recent first: 2 related tags, 93 annotations, & 9 links (parent).

• See Also
• Links
  • “CT Foundation: Taking Medical Imaging Embeddings 3D”, Kiraly & Traverse2024
  • “Long-Term Tracking of Social Structure in Groups of Rats”, Nagy et al 2024
  • “Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-Modal LLMs in Video Analysis”, Fu et al 2024
  • “InternVid: A Large-Scale Video-Text Dataset for Multimodal Understanding and Generation”, Wang et al 2023
  • “Test-Time Training on Video Streams”, Wang et al 2023
  • “Magenta Green Screen: Spectrally Multiplexed Alpha Matting With Deep Colorization”, Smirnov et al 2023
  • “PaLI-X: On Scaling up a Multilingual Vision and Language Model”, Chen et al 2023
  • “ImageBind: One Embedding Space To Bind Them All”, Girdhar et al 2023
  • “Scaling Vision Transformers to 22 Billion Parameters”, Dehghani et al 2023
  • “VideoCoCa: Video-Text Modeling With Zero-Shot Transfer from Contrastive Captioners”, Yan et al 2022
  • “VindLU: A Recipe for Effective Video-And-Language Pretraining”, Cheng et al 2022
  • “Videogenic: Video Highlights via Photogenic Moments”, Lin et al 2022
  • “AnimeRun: 2D Animation Visual Correspondence from Open Source 3D Movies”, Siyao et al 2022
  • “Vision-Language Pre-Training: Basics, Recent Advances, and Future Trends”, Gan et al 2022
  • “TVLT: Textless Vision-Language Transformer”, Tang et al 2022
  • “EVL: Frozen CLIP Models Are Efficient Video Learners”, Lin et al 2022
  • “X-CLIP: Expanding Language-Image Pretrained Models for General Video Recognition”, Ni et al 2022
  • “X-CLIP: End-To-End Multi-Grained Contrastive Learning for Video-Text Retrieval”, Ma et al 2022
  • “Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos”, Baker et al 2022
  • “OmniMAE: Single Model Masked Pretraining on Images and Videos”, Girdhar et al 2022
  • “LAVENDER: Unifying Video-Language Understanding As Masked Language Modeling”, Li et al 2022
  • “MLP-3D: A MLP-Like 3D Architecture With Grouped Time Mixing”, Qiu et al 2022
  • “Uni-Perceiver-MoE: Learning Sparse Generalist Models With Conditional MoEs”, Zhu et al 2022
  • “Revisiting the “Video” in Video-Language Understanding”, Buch et al 2022
  • “VidIL: Language Models With Image Descriptors Are Strong Few-Shot Video-Language Learners”, Wang et al 2022
  • “Masked Autoencoders As Spatiotemporal Learners”, Feichtenhofer et al 2022
  • “Imitating, Fast and Slow: Robust Learning from Demonstrations via Decision-Time Planning”, Qi et al 2022
  • “ViS4mer: Long Movie Clip Classification With State-Space Video Models”, Islam & Bertasius2022
  • “Socratic Models: Composing Zero-Shot Multimodal Reasoning With Language”, Zeng et al 2022
  • “Reinforcement Learning With Action-Free Pre-Training from Videos”, Seo et al 2022
  • “CLIP Meets GamePhysics: Towards Bug Identification in Gameplay Videos Using Zero-Shot Transfer Learning”, Taesiri et al 2022
  • “Robot Peels Banana With Goal-Conditioned Dual-Action Deep Imitation Learning”, Kim et al 2022
  • “Hierarchical Perceiver”, Carreira et al 2022
  • “MuZero With Self-Competition for Rate Control in VP9 Video Compression”, Mandhane et al 2022
  • “BLIP: Bootstrapping Language-Image Pre-Training for Unified Vision-Language Understanding and Generation”, Li et al 2022
  • “MeMViT: Memory-Augmented Multiscale Vision Transformer for Efficient Long-Term Video Recognition”, Wu et al 2022
  • “CAST: Character Labeling in Animation Using Self-Supervision by Tracking”, Nir et al 2022
  • “AV-HuBERT: Learning Audio-Visual Speech Representation by Masked Multimodal Cluster Prediction”, Shi et al 2022
  • “Noether Networks: Meta-Learning Useful Conserved Quantities”, Alet et al 2021
  • “MAD: A Scalable Dataset for Language Grounding in Videos from Movie Audio Descriptions”, Soldan et al 2021
  • “MorphMLP: A Self-Attention Free, MLP-Like Backbone for Image and Video”, Zhang et al 2021
  • “Florence: A New Foundation Model for Computer Vision”, Yuan et al 2021
  • “Scaling ASR Improves Zero and Few Shot Learning”, Xiao et al 2021
  • “ADOP: Approximate Differentiable One-Pixel Point Rendering”, Rückert et al 2021
  • “VideoCLIP: Contrastive Pre-Training for Zero-Shot Video-Text Understanding”, Xu et al 2021
  • “Perceiver IO: A General Architecture for Structured Inputs & Outputs”, Jaegle et al 2021
  • “CLIP-It! Language-Guided Video Summarization”, Narasimhan et al 2021
  • “CLIP2Video: Mastering Video-Text Retrieval via Image CLIP”, Fang et al 2021
  • “Revisiting ResNets: Improved Training and Scaling Strategies”, Bello et al 2021
  • “Learning from Videos to Understand the World”, Zweig et al 2021
  • “Perceiver: General Perception With Iterative Attention”, Jaegle et al 2021
  • “Video Transformer Network”, Neimark et al 2021
  • “Automatic Curation of Large-Scale Datasets for Audio-Visual Representation Learning”, Lee et al 2021
  • “MSR-VTT: A Large Video Description Dataset for Bridging Video and Language”, Xu et al 2021
  • “CLIP: Learning Transferable Visual Models From Natural Language Supervision”, Radford et al 2021
  • “Transformers in Vision: A Survey”, Khan et al 2021
  • “Object-Based Attention for Spatio-Temporal Reasoning: Outperforming Neuro-Symbolic Models With Flexible Distributed Architectures”, Ding et al 2020
  • “Accuracy and Performance Comparison of Video Action Recognition Approaches”, Hutchinson et al 2020
  • “Self-Supervised Learning through the Eyes of a Child”, Orhan et al 2020
  • “Gesticulator: A Framework for Semantically-Aware Speech-Driven Gesture Generation”, Kucherenko et al 2020
  • “SAYCam: A Large, Longitudinal Audiovisual Dataset Recorded from the Infant’s Perspective”, Sullivan et al 2020
  • “Axial Attention in Multidimensional Transformers”, Ho et al 2019
  • “CATER: A Diagnostic Dataset for Compositional Actions and TEmporal Reasoning”, Girdhar & Ramanan2019
  • “CLEVRER: CoLlision Events for Video REpresentation and Reasoning”, Yi et al 2019
  • “Training Kinetics in 15 Minutes: Large-Scale Distributed Training on Videos”, Lin et al 2019
  • “A Short Note on the Kinetics-700 Human Action Dataset”, Carreira et al 2019
  • “Billion-Scale Semi-Supervised Learning for Image Classification”, Yalniz et al 2019
  • “VideoBERT: A Joint Model for Video and Language Representation Learning”, Sun et al 2019
  • “Real-Time Continuous Transcription With Live Transcribe”, Savla2019
  • “CCNet: Criss-Cross Attention for Semantic Segmentation”, Huang et al 2018
  • “Evolving Space-Time Neural Architectures for Videos”, Piergiovanni et al 2018
  • “Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow”, Peng et al 2018
  • “A Short Note about Kinetics-600”, Carreira et al 2018
  • “Large-Scale Visual Speech Recognition”, Shillingford et al 2018
  • “Playing Hard Exploration Games by Watching YouTube”, Aytar et al 2018
  • “BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning”, Yu et al 2018
  • “The Sound of Pixels”, Zhao et al 2018
  • “One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning”, Yu et al 2018
  • “Learning Compact Recurrent Neural Networks With Block-Term Tensor Decomposition”, Ye et al 2017
  • “Reinforced Video Captioning With Entailment Rewards”, Pasunuru & Bansal2017
  • “Tracking As Online Decision-Making: Learning a Policy from Streaming Videos With Reinforcement Learning”, III & Ramanan2017
  • “Learning to Learn from Noisy Web Videos”, Yeung et al 2017
  • “Quo Vadis, Action Recognition? A New Model I3D and the Kinetics Dataset”, Carreira & Zisserman2017
  • “The Kinetics Human Action Video Dataset”, Kay et al 2017
  • “Dense-Captioning Events in Videos”, Krishna et al 2017
  • “Time-Contrastive Networks: Self-Supervised Learning from Video”, Sermanet et al 2017
  • “LipNet: End-To-End Sentence-Level Lipreading”, Assael et al 2016
  • “Deep Visual Foresight for Planning Robot Motion”, Finn & Levine2016
  • “Temporal Convolutional Networks: A Unified Approach to Action Segmentation”, Lea et al 2016
  • “Clockwork Convnets for Video Semantic Segmentation”, Shelhamer et al 2016
  • “Artistic Style Transfer for Videos”, Ruder et al 2016
  • “YFCC100M: The New Data in Multimedia Research”, Thomee et al 2015
  • “UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild”, Soomro et al 2012
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    • video-imitation
    • video-language
    • video-representation
    • video-embedding
    • video-captioning
    • action-recognition
• Miscellaneous
• Bibliography