https://github.com/qmme/stablevqa
공식 코드StableVQA: A Deep No-Reference Quality Assessment Model for Video Stability
Papers with Code 아카이브
Optical Flow Estimation
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Optical Flow Estimation is a computer vision task that involves computing the motion of objects in an image or a video sequence. The goal of optical flow estimation is to determine the movement of pixels or features in the image, which can be used for various applications such as object tracking, motion analysis, and video compression.
Approaches for optical flow estimation include correlation-based, block-matching, feature tracking, energy-based, and more recently gradient-based.
Further readings:
Definition source: Devon: Deformable Volume Network for Learning Optical Flow
Image credit: Optical Flow Estimation
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/rate-accuracy-mvcnn/main
공식 코드Rate-Accuracy Trade-Off In Video Classification With Deep Convolutional Neural Networks
https://github.com/raymondwang987/fmnet
공식 코드Less is More: Consistent Video Depth Estimation with Masked Frames Modeling
https://github.com/redwannewaz/ds_mshvit
공식 코드Dual-Stream Attention Transformers for Sewer Defect Classification
https://github.com/rehg-lab/dope_selfsup
공식 코드Learning Dense Object Descriptors from Multiple Views for Low-shot Category Generalization
https://github.com/rfonod/geo-trax
공식 코드Advanced computer vision for extracting georeferenced vehicle trajectories from drone imagery
https://github.com/ria1159/facialflownet
공식 코드https://github.com/riaduli/thresholded_event_vision_face_dataset
공식 코드Descriptor: Face Detection Dataset for Programmable Threshold-Based Sparse-Vision
https://github.com/riponazad/echotracker
공식 코드EchoTracker: Advancing Myocardial Point Tracking in Echocardiography
https://github.com/rpmsnu/srgb-tir
공식 코드https://github.com/rrrajjjj/flowgrad
공식 코드https://github.com/ruhig6/ibvc
공식 코드https://github.com/ruiyinglu/ofanet-for-dual-view-sci
공식 코드Dual-view Snapshot Compressive Imaging via Optical Flow Aided Recurrent Neural Network
https://github.com/ruizhao26/ste-flownet
공식 코드Spatio-Temporal Recurrent Networks for Event-Based Optical Flow Estimation
https://github.com/rwightman/posenet-python
공식 코드Three-Stream 3D/1D CNN for Fine-Grained Action Classification and Segmentation in Table Tennis
https://github.com/s-gupta/fast-rcnn
공식 코드https://github.com/samiarja/ev_deep_motion_segmentation
공식 코드https://github.com/sbelharbi/tcam-wsol-video
공식 코드TCAM: Temporal Class Activation Maps for Object Localization in Weakly-Labeled Unconstrained Videos
https://github.com/sbsws88/tfs_nerf
공식 코드TFS-NeRF: Template-Free NeRF for Semantic 3D Reconstruction of Dynamic Scene
https://github.com/schellmi42/WFlowToF
공식 코드Weakly-Supervised Optical Flow Estimation for Time-of-Flight
https://github.com/sczhou/propainter
공식 코드ProPainter: Improving Propagation and Transformer for Video Inpainting
https://github.com/seleucia/goca
공식 코드GOCA: Guided Online Cluster Assignment for Self-Supervised Video Representation Learning
https://github.com/serycjon/WOFT
공식 코드https://github.com/serycjon/mftiq
공식 코드MFTIQ: Multi-Flow Tracker with Independent Matching Quality Estimation
https://github.com/shaharzuler/CardioSpectrum
공식 코드https://github.com/shaharzuler/cardio_volume_skewer
공식 코드Synthetic Data Generation for 3D Myocardium Deformation Analysis
https://github.com/shankhanil006/vision
공식 코드https://github.com/sharpiless/mpi-flow
공식 코드MPI-Flow: Learning Realistic Optical Flow with Multiplane Images
https://github.com/shashankskagnihotri/adv-corrected-ddcat-cospgd
공식 코드CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasks
https://github.com/shashankskagnihotri/cospgd
공식 코드CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasks
https://github.com/shengyangsun/MSBT
공식 코드Multi-scale Bottleneck Transformer for Weakly Supervised Multimodal Violence Detection
https://github.com/shicy17/ido-vfi
공식 코드IDO-VFI: Identifying Dynamics via Optical Flow Guidance for Video Frame Interpolation with Events
https://github.com/shiqiyu/opengait
공식 코드Exploring More from Multiple Gait Modalities for Human Identification
https://github.com/sign-language-processing/transcription
공식 코드https://github.com/simonmeister/UnFlow
공식 코드UnFlow: Unsupervised Learning of Optical Flow with a Bidirectional Census Loss
https://github.com/sniklaus/sepconv-slomo
공식 코드Video Frame Interpolation via Adaptive Separable Convolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.