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

https://github.com/amir-aghdam/ActAlign

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ActAlign: Zero-Shot Fine-Grained Video Classification via Language-Guided Sequence Alignment

https://github.com/amir4g/clvos23

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CLVOS23: A Long Video Object Segmentation Dataset for Continual Learning

https://github.com/amitgalor18/stc_tracker

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Strong-TransCenter: Improved Multi-Object Tracking based on Transformers with Dense Representations

https://github.com/amodaresi/memllm

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MemLLM: Finetuning LLMs to Use An Explicit Read-Write Memory

https://github.com/amshaker/mavos

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Efficient Video Object Segmentation via Modulated Cross-Attention Memory

https://github.com/amshaker/mobile-videogpt

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Mobile-VideoGPT: Fast and Accurate Video Understanding Language Model

https://github.com/andongdeng/bear

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A Large-scale Study of Spatiotemporal Representation Learning with a New Benchmark on Action Recognition

https://github.com/andr345/frtm-vos

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Learning Fast and Robust Target Models for Video Object Segmentation

https://github.com/andreYoo/PeGCNs

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Predictively Encoded Graph Convolutional Network for Noise-Robust Skeleton-based Action Recognition

https://github.com/andrea-cavallo-98/sparse-vnn

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Sparse Covariance Neural Networks

https://github.com/andrebola/skip-challenge-wsdm

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Skip prediction using boosting trees based on acoustic features of tracks in sessions

https://github.com/andrewjywang/SEENet

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Order Matters: Shuffling Sequence Generation for Video Prediction

https://github.com/andy-cheng/tempura

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TEMPURA: Temporal Event Masked Prediction and Understanding for Reasoning in Action

https://github.com/andyplus1/conmo

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ConMo: Controllable Motion Disentanglement and Recomposition for Zero-Shot Motion Transfer

https://github.com/anguelos/tormentor

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TorMentor: Deterministic dynamic-path, data augmentations with fractals

https://github.com/anirudhs123/home

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HomE: Homography-Equivariant Video Representation Learning

https://github.com/annusha/xmic

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X-MIC: Cross-Modal Instance Conditioning for Egocentric Action Generalization

https://github.com/anokiai/contextiq-paper

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ContextIQ: A Multimodal Expert-Based Video Retrieval System for Contextual Advertising

https://github.com/anonrep/iba-for-video-sequences

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Extending Information Bottleneck Attribution to Video Sequences

https://github.com/anonymous-p/Flickering_Adversarial_Video

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Over-the-Air Adversarial Flickering Attacks against Video Recognition Networks

https://github.com/anonymous202203/moso

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MOSO: Decomposing MOtion, Scene and Object for Video Prediction

https://github.com/anshitag/memit_csk

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Editing Common Sense in Transformers

https://github.com/anshulbshah/PoseAction

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Pose And Joint-Aware Action Recognition

https://github.com/anshulbshah/halp

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HaLP: Hallucinating Latent Positives for Skeleton-based Self-Supervised Learning of Actions

https://github.com/answers111/opencarboneval

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OpenCarbonEval: A Unified Carbon Emission Estimation Framework in Large-Scale AI Models

https://github.com/antgroup/echomimic_v2

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EchoMimicV2: Towards Striking, Simplified, and Semi-Body Human Animation

https://github.com/antoine77340/LOUPE

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Learnable pooling with Context Gating for video classification

https://github.com/antoine77340/MIL-NCE_HowTo100M

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End-to-End Learning of Visual Representations from Uncurated Instructional Videos

https://github.com/antoine77340/Mixture-of-Embedding-Experts

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Learning a Text-Video Embedding from Incomplete and Heterogeneous Data

https://github.com/antoine77340/Youtube-8M-WILLOW

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Learnable pooling with Context Gating for video classification

https://github.com/antoyang/TubeDETR

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TubeDETR: Spatio-Temporal Video Grounding with Transformers

https://github.com/antran89/two-stream-fcan

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Two-stream Flow-guided Convolutional Attention Networks for Action Recognition

https://github.com/aofrancani/tsformer-vo

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Transformer-Based Model for Monocular Visual Odometry: A Video Understanding Approach

https://github.com/apartresearch/specificityplus

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Detecting Edit Failures In Large Language Models: An Improved Specificity Benchmark

https://github.com/apple/ml-cvnets

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CVNets: High Performance Library for Computer Vision

https://github.com/apple/ml-slowfast-llava

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SlowFast-LLaVA: A Strong Training-Free Baseline for Video Large Language Models

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