CVPR 2015

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

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

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.

Deep Filter Banks for Texture Recognition and Segmentation

CVPR 2015 mcimpoi/deep-fbanks

Research in texture recognition often concentrates on the problem of material recognition in uncluttered conditions, an assumption rarely met by applications.

MATERIAL RECOGNITION SCENE RECOGNITION

Saliency-Aware Geodesic Video Object Segmentation

CVPR 2015 shenjianbing/videoseg15

Building on the observation that foreground areas are surrounded by the regions with high spatiotemporal edge values, geodesic distance provides an initial estimation for foreground and background.

SEMANTIC SEGMENTATION VIDEO OBJECT SEGMENTATION VIDEO SEMANTIC SEGMENTATION

Understanding Deep Image Representations by Inverting Them

CVPR 2015 KamitaniLab/icnn

Image representations, from SIFT and Bag of Visual Words to Convolutional Neural Networks (CNNs), are a crucial component of almost any image understanding system.

From Captions to Visual Concepts and Back

CVPR 2015 Epiphqny/Multiple-instance-learning

The language model learns from a set of over 400, 000 image descriptions to capture the statistics of word usage.

IMAGE CAPTIONING LANGUAGE MODELLING MULTIPLE INSTANCE LEARNING

On learning optimized reaction diffusion processes for effective image restoration

CVPR 2015 VLOGroup/tensorflow-icg

We propose to train the parameters of the filters and the influence functions through a loss based approach.

IMAGE RESTORATION