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style_avatar

A repository for generating stylized talking 3D and 3D face

supergluepretrainednetwork

SuperGlue: Learning Feature Matching with Graph Neural Networks (CVPR 2020, Oral)

superpoint

Efficient neural feature detector and descriptor

superpointpretrainednetwork

PyTorch pre-trained model for real-time interest point detection, description, and sparse tracking (https://arxiv.org/abs/1712.07629)

talking-face_pc-avs

Code for Pose-Controllable Talking Face Generation by Implicitly Modularized Audio-Visual Representation (CVPR 2021)

talking-smart-glass-for-blind

td-flow-abs

Real-time Flow-based Image and Video Abstraction in TouchDesigner.

tensorflow-object-detection-api-tutorial-train-multiple-objects-windows-10

How to train a TensorFlow Object Detection Classifier for multiple object detection on Windows

tensorflow-yolov4-tflite

YOLOv4, YOLOv4-tiny, YOLOv3, YOLOv3-tiny Implemented in Tensorflow 2.0, Android. Convert YOLO v4 .weights tensorflow, tensorrt and tflite

tensormask

Instance Segmentation, TensorMask

tensorrt-yolov4

tensorrt5, yolov4, yolov3,yolov3-tniy,yolov3-tniy-prn

text

Data loaders and abstractions for text and NLP

thundernet-review

Real-time generic object detection on mobile platforms is a crucial but challenging computer vision task. However, previous CNN-based detectors suffer from enormous computational cost, which hinders them from real-time inference in computation-constrained scenarios. In this paper, we investigate the effectiveness of two-stage detectors in real-time generic detection and propose a lightweight twostage detector named ThunderNet. In the backbone part, we analyze the drawbacks in previous lightweight backbones and present a lightweight backbone designed for object detection. In the detection part, we exploit an extremely efficient RPN and detection head design. To generate more discriminative feature representation, we design two efficient architecture blocks, Context Enhancement Module and Spatial Attention Module. At last, we investigate the balance between the input resolution, the backbone, and the detection head. Compared with lightweight one-stage detectors, ThunderNet achieves superior performance with only 40% of the computational cost on PASCAL VOC and COCO benchmarks. Without bells and whistles, our model runs at 24.1 fps on an ARM-based device. To the best of our knowledge, this is the first real-time detector reported on ARM platforms. Code will be released for paper reproduction.

torch-attention-ocr

tsd

1st place models in Google OpenImage Detection Challenge 2019

tts

:robot: :speech_balloon: Deep learning for Text to Speech (Discussion forum: https://discourse.mozilla.org/c/tts)

tusimple-benchmark

Download Datasets and Ground Truths: https://github.com/TuSimple/tusimple-benchmark/issues/3

u-2-net

The code for our newly accepted paper in Pattern Recognition 2020: "U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection."

ultra-fast-lane-detection

The implementation of paper "Ultra Fast Structure-aware Deep Lane Detection"

unitytemplate

unpaired-portrait-drawing

Code for Unpaired Portrait Drawing Generation via Asymmetric Cycle Mapping (CVPR 2020)

unrealtext

Synthetic Scene Text from 3D Engines

vedastr

A scene text recognition toolbox based on pytorch

vhproject

Virtual human (avatar and agent) procedural animation toolkit for Unity. https://www.youtube.com/watch?v=mstLuzTw790

video-highlight

video-highlight-detection

video highlight detection with deep networks

video-removal--logo

video-summarization

Entropy and Memorability Based Video Summarization

video-to-pose3d

Convert video to 3D pose in one-key.

videoabstraction

video abstraction with Flash/Flex and Pixel Bender