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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/mxbi/youtube8m-2019

공식 코드

Learning to Localize Temporal Events in Large-scale Video Data

https://github.com/myniuuu/mofa-video

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MOFA-Video: Controllable Image Animation via Generative Motion Field Adaptions in Frozen Image-to-Video Diffusion Model

https://github.com/myungsub/meta-interpolation

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Scene-Adaptive Video Frame Interpolation via Meta-Learning

https://github.com/mzhaoshuai/CenterCLIP

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CenterCLIP: Token Clustering for Efficient Text-Video Retrieval

https://github.com/mzolfaghari/ECO-efficient-video-understanding

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ECO: Efficient Convolutional Network for Online Video Understanding

https://github.com/nachiket92/PGP

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Multimodal Trajectory Prediction Conditioned on Lane-Graph Traversals

https://github.com/nadeemlab/CEP

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RT-GAN: Recurrent Temporal GAN for Adding Lightweight Temporal Consistency to Frame-Based Domain Translation Approaches

https://github.com/nadezola/intothefog_mot17

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Into the Fog: Evaluating Robustness of Multiple Object Tracking

https://github.com/nagejacob/flornn

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Unidirectional Video Denoising by Mimicking Backward Recurrent Modules with Look-ahead Forward Ones

https://github.com/nail-hnu/esvo2

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ESVO2: Direct Visual-Inertial Odometry with Stereo Event Cameras

https://github.com/nanguoshun/IVG

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Interventional Video Grounding with Dual Contrastive Learning

https://github.com/nanh5837/lettucemots

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Segmentation and Tracking of Vegetable Plants by Exploiting Vegetable Shape Feature for Precision Spray of Agricultural Robots

https://github.com/nankepan/VIPMT

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Multi-grained Temporal Prototype Learning for Few-shot Video Object Segmentation

https://github.com/naraysa/3c-net

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3C-Net: Category Count and Center Loss for Weakly-Supervised Action Localization

https://github.com/naraysa/D2-Net

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D2-Net: Weakly-Supervised Action Localization via Discriminative Embeddings and Denoised Activations

https://github.com/naraysa/gzsl-od

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Out-of-Distribution Detection for Generalized Zero-Shot Action Recognition

https://github.com/nasib-ullah/THVC

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Thinking Hallucination for Video Captioning

https://github.com/naver-ai/tc-clip

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Leveraging Temporal Contextualization for Video Action Recognition

https://github.com/nawidsayed/Cross-and-Learn

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Cross and Learn: Cross-Modal Self-Supervision

https://github.com/nazmul-karim170/FIP-Fisher-Backdoor-Removal

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Fisher Information guided Purification against Backdoor Attacks

https://github.com/nazmul-karim170/Free-Editor

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Free-Editor: Zero-shot Text-driven 3D Scene Editing

https://github.com/nazmul-karim170/NFT-Augmented-Backdoor-Purification

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Augmented Neural Fine-Tuning for Efficient Backdoor Purification

https://github.com/nbahador/-fine_tuned_gpt2_model_on_epilepsy_related_pubmed_abstracts

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Localized Definitions and Distributed Reasoning: A Proof-of-Concept Mechanistic Interpretability Study via Activation Patching

https://github.com/nbei/Deep-Flow-Guided-Video-Inpainting

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Deep Flow-Guided Video Inpainting

https://github.com/ncgarcia/DMCL

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DMCL: Distillation Multiple Choice Learning for Multimodal Action Recognition

https://github.com/ncgarcia/modality-distillation

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Modality Distillation with Multiple Stream Networks for Action Recognition

https://github.com/ndrwmlnk/awesome-video-diffusion-models

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Video Diffusion Models: A Survey

https://github.com/necla-ml/cater-h

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Hopper: Multi-hop Transformer for Spatiotemporal Reasoning

https://github.com/necla-ml/cpr

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Self-supervised Video Representation Learning with Cascade Positive Retrieval

https://github.com/necolizer/facial-prior-based-fomm

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Facial Prior Based First Order Motion Model for Micro-expression Generation

https://github.com/neelumadan/vifm_survey

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Foundation Models for Video Understanding: A Survey

https://github.com/nematoli/t3vip

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T3VIP: Transformation-based 3D Video Prediction

https://github.com/nercms-mmap/friendsqa

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FriendsQA: A New Large-Scale Deep Video Understanding Dataset with Fine-grained Topic Categorization for Story Videos

https://github.com/nervjack2/propertyneurons

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Property Neurons in Self-Supervised Speech Transformers

https://github.com/netflix/videoannotator

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Video Annotator: A framework for efficiently building video classifiers using vision-language models and active learning

https://github.com/neuripss2020/kccotgan

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Conditional COT-GAN for Video Prediction with Kernel Smoothing

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.