CVPR 2015

Going Deeper with Convolutions

CVPR 2015 tensorflow/models

We propose a deep convolutional neural network architecture codenamed "Inception", which was responsible for setting the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC 2014).

IMAGE CLASSIFICATION OBJECT DETECTION OBJECT RECOGNITION

FaceNet: A Unified Embedding for Face Recognition and Clustering

CVPR 2015 davidsandberg/facenet

On the widely used Labeled Faces in the Wild (LFW) dataset, our system achieves a new record accuracy of 99. 63%.

FACE IDENTIFICATION FACE RECOGNITION FACE VERIFICATION

Show and Tell: A Neural Image Caption Generator

CVPR 2015 karpathy/neuraltalk

Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions.

IMAGE CAPTIONING TEXT GENERATION

Deep Visual-Semantic Alignments for Generating Image Descriptions

CVPR 2015 karpathy/neuraltalk2

Our approach leverages datasets of images and their sentence descriptions to learn about the inter-modal correspondences between language and visual data.

IMAGE CAPTIONING

CIDEr: Consensus-based Image Description Evaluation

CVPR 2015 tylin/coco-caption

We propose a novel paradigm for evaluating image descriptions that uses human consensus.

IMAGE CAPTIONING

Convolutional Feature Masking for Joint Object and Stuff Segmentation

CVPR 2015 daijifeng001/MNC

The current leading approaches for semantic segmentation exploit shape information by extracting CNN features from masked image regions.

SEMANTIC SEGMENTATION

Fusion Moves for Correlation Clustering

CVPR 2015 opengm/opengm

Correlation clustering, or multicut partitioning, is widely used in image segmentation for partitioning an undirected graph or image with positive and negative edge weights such that the sum of cut edge weights is minimized.

SEMANTIC SEGMENTATION

Fully Convolutional Networks for Semantic Segmentation

CVPR 2015 andyzeng/apc-vision-toolbox

Convolutional networks are powerful visual models that yield hierarchies of features.

SEMANTIC SEGMENTATION

Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images

CVPR 2015 utkuozbulak/pytorch-cnn-adversarial-attacks

Here we show a related result: it is easy to produce images that are completely unrecognizable to humans, but that state-of-the-art DNNs believe to be recognizable objects with 99. 99% confidence (e. g. labeling with certainty that white noise static is a lion).

Deformable Part Models are Convolutional Neural Networks

CVPR 2015 rbgirshick/DeepPyramid

Deformable part models (DPMs) and convolutional neural networks (CNNs) are two widely used tools for visual recognition.