suki7

LearningToCompare_FSL

PyTorch code for CVPR 2018 paper: Learning to Compare: Relation Network for Few-Shot Learning (Few-Shot Learning part)

Embodied-One-Shot-Video-Recognition

The code of paper ACM Multimedia 2019: "Embodied One-Shot Video Recognition: Learning from Actions of a Virtual Embodied Agent"

ECN

Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification CVPR 2019

EveryPixelMatters

Implementation of ECCV 2020 paper "Every Pixel Matters: Center-aware Feature Alignment for Domain Adaptive Object Detector"

Local-Mid-Propagation

Code for ACM MM Workshop paper "Online Video Object Detection via Local and Mid-Range Feature Propagation"

video_object_detection_paper

update some video object detection papers (视频目标检测论文和代码整理)

STEP

STEP: Spatio-Temporal Progressive Learning for Video Action Detection. CVPR'19 (Oral)

AGNN

Zero-shot Video Object Segmentation via Attentive Graph Neural Networks (ICCV2019 Oral)

Robust-and-efficient-post-processing-for-video-object-detection

Thundernet_Pytorch

Implementation Thundernet

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.

models

Models and examples built with TensorFlow

LSTS

[ECCV 2020] Learning Where to Focus for Efficient Video Object Detection

mega.pytorch

Memory Enhanced Global-Local Aggregation for Video Object Detection, CVPR2020

cascade-rcnn

Caffe implementation of multiple popular object detection frameworks

mmdetection

OpenMMLab Detection Toolbox and Benchmark

awesome-object-detection

Awesome Object Detection based on handong1587 github: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html

PytorchInsight

a pytorch lib with state-of-the-art architectures, pretrained models and real-time updated results

PAN-PyTorch

[Codes of paper]: PAN: Towards Fast Action Recognition via Learning Persistence of Appearance

Fewshot_Detection

Few-shot Object Detection via Feature Reweighting

GAL-fWSD

Generative Adversarial Learning Towards Fast Weakly Supervised Detection

WSOD_Paper_List

A list of recent WSOD paper

wetectron

Weakly-supervised object detection.

pytorch_MELM

The pytorch implementation of the Min-Entropy Latent Model for Weakly Supervised Object Detection

MutualNet

[ECCV'20 Oral] MutualNet: Adaptive ConvNet via Mutual Learning from Network Width and Resolution

X-Temporal

A general video understanding codebase from SenseTime X-Lab

filter-grafting

Filter Grafting for Deep Neural Networks(CVPR 2020)

SEPC

Scale-equalizing Pyramid Convolution for object detection(CVPR2020)

Teacher-Assistant-Knowledge-Distillation

Using Teacher Assistants to Improve Knowledge Distillation: https://arxiv.org/pdf/1902.03393.pdf

DO-Conv

Depthwise Over-parameterized Convolutional Layer