CVPR 2016

Rethinking the Inception Architecture for Computer Vision

CVPR 2016 tensorflow/models

Convolutional networks are at the core of most state-of-the-art computer vision solutions for a wide variety of tasks.

IMAGE CLASSIFICATION

Deep Residual Learning for Image Recognition

CVPR 2016 tensorflow/models

Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

IMAGE CLASSIFICATION OBJECT DETECTION SEMANTIC SEGMENTATION

Convolutional Pose Machines

CVPR 2016 CMU-Perceptual-Computing-Lab/openpose

Pose Machines provide a sequential prediction framework for learning rich implicit spatial models.

3D HUMAN POSE ESTIMATION STRUCTURED PREDICTION

Deeply-Recursive Convolutional Network for Image Super-Resolution

CVPR 2016 alexjc/neural-enhance

We propose an image super-resolution method (SR) using a deeply-recursive convolutional network (DRCN).

IMAGE SUPER-RESOLUTION

Context Encoders: Feature Learning by Inpainting

CVPR 2016 eriklindernoren/Keras-GAN

In order to succeed at this task, context encoders need to both understand the content of the entire image, as well as produce a plausible hypothesis for the missing part(s).

You Only Look Once: Unified, Real-Time Object Detection

CVPR 2016 thtrieu/darkflow

A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation.

REAL-TIME OBJECT DETECTION REGRESSION

Learning Deep Features for Discriminative Localization

CVPR 2016 tensorpack/tensorpack

In this work, we revisit the global average pooling layer proposed in [13], and shed light on how it explicitly enables the convolutional neural network to have remarkable localization ability despite being trained on image-level labels.

WEAKLY-SUPERVISED OBJECT LOCALIZATION

Fast Algorithms for Convolutional Neural Networks

CVPR 2016 XiaoMi/mace

The algorithms compute minimal complexity convolution over small tiles, which makes them fast with small filters and small batch sizes.

PEDESTRIAN DETECTION SELF-DRIVING CARS

Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis

CVPR 2016 awentzonline/image-analogies

This paper studies a combination of generative Markov random field (MRF) models and discriminatively trained deep convolutional neural networks (dCNNs) for synthesizing 2D images.

IMAGE GENERATION TEXTURE SYNTHESIS