https://github.com/google-deepmind/neptune
공식 코드Neptune: The Long Orbit to Benchmarking Long Video Understanding
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/google-deepmind/physics-IQ-benchmark
공식 코드https://github.com/google-research-datasets/i3-video
공식 코드Beyond Instructional Videos: Probing for More Diverse Visual-Textual Grounding on YouTube
https://github.com/google-research/fitvid
공식 코드https://github.com/google-research/frame-interpolation
공식 코드https://github.com/google-research/google-research
공식 코드Unsupervised Learning of Object Structure and Dynamics from Videos
https://github.com/google-research/google-research/tree/master/comisr
공식 코드https://github.com/google-research/google-research/tree/master/infinite_nature
공식 코드Infinite Nature: Perpetual View Generation of Natural Scenes from a Single Image
https://github.com/google-research/google-research/tree/master/musiq
공식 코드https://github.com/google-research/google-research/tree/master/socraticmodels
공식 코드Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language
https://github.com/google-research/google-research/tree/master/tiny_video_nets
공식 코드https://github.com/google-research/google-research/tree/master/vatt
공식 코드VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text
https://github.com/google-research/google-research/tree/master/vct
공식 코드https://github.com/google-research/magvit
공식 코드https://github.com/google-research/pix2seq
공식 코드https://github.com/google-research/scenic
공식 코드https://github.com/google-research/scenic/tree/main/scenic/projects/mbt
공식 코드https://github.com/google-research/scenic/tree/main/scenic/projects/verbs_in_action
공식 코드Verbs in Action: Improving verb understanding in video-language models
https://github.com/google-research/torchsde
공식 코드https://github.com/google-research/zapbench
공식 코드ZAPBench: A Benchmark for Whole-Brain Activity Prediction in Zebrafish
https://github.com/google/belief-localization
공식 코드https://github.com/google/graph_distillation
공식 코드Graph Distillation for Action Detection with Privileged Modalities
https://github.com/google/next-prediction
공식 코드Peeking into the Future: Predicting Future Person Activities and Locations in Videos
https://github.com/google/sandwiched_compression
공식 코드Sandwiched Compression: Repurposing Standard Codecs with Neural Network Wrappers
https://github.com/google/storybench
공식 코드StoryBench: A Multifaceted Benchmark for Continuous Story Visualization
https://github.com/google/video-localized-narratives
공식 코드Connecting Vision and Language with Video Localized Narratives
https://github.com/gorjanradevski/multimodal-distillation
공식 코드https://github.com/gorjanradevski/revisiting-spatial-temporal-layouts
공식 코드Revisiting spatio-temporal layouts for compositional action recognition
https://github.com/grasshlw/snn-neural-similarity-movie
공식 코드https://github.com/griffbr/BubbleNets
공식 코드https://github.com/griffbr/ODMS
공식 코드Learning Object Depth from Camera Motion and Video Object Segmentation
https://github.com/griffbr/TIS
공식 코드https://github.com/griffbr/supervoxel-gerrymandering
공식 코드Video Object Segmentation using Supervoxel-Based Gerrymandering
https://github.com/grishavak/PaCNet-denoiser
공식 코드Patch Craft: Video Denoising by Deep Modeling and Patch Matching
https://github.com/gsaponaro/tcds-gestures
공식 코드Beyond the Self: Using Grounded Affordances to Interpret and Describe Others' Actions
https://github.com/gsig/actor-observer
공식 코드Actor and Observer: Joint Modeling of First and Third-Person Videos
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.