ICCV 2015

Holistically-Nested Edge Detection

ICCV 2015 s9xie/hed

We develop a new edge detection algorithm that tackles two important issues in this long-standing vision problem: (1) holistic image training and prediction; and (2) multi-scale and multi-level feature learning.

BOUNDARY DETECTION EDGE DETECTION

Describing Videos by Exploiting Temporal Structure

ICCV 2015 tsenghungchen/SA-tensorflow

In this context, we propose an approach that successfully takes into account both the local and global temporal structure of videos to produce descriptions.

ACTION RECOGNITION VIDEO DESCRIPTION

Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

ICCV 2015 zonetrooper32/VDCNN

In this work, we study rectifier neural networks for image classification from two aspects.

IMAGE CLASSIFICATION

DeepBox: Learning Objectness with Convolutional Networks

ICCV 2015 weichengkuo/DeepBox

Existing object proposal approaches use primarily bottom-up cues to rank proposals, while we believe that objectness is in fact a high level construct.

Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books

ICCV 2015 soskek/homemade_bookcorpus

Books are a rich source of both fine-grained information, how a character, an object or a scene looks like, as well as high-level semantics, what someone is thinking, feeling and how these states evolve through a story.

SENTENCE EMBEDDING

Learning like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images

ICCV 2015 mjhucla/TF-mRNN

In particular, we propose a transposed weight sharing scheme, which not only improves performance on image captioning, but also makes the model more suitable for the novel concept learning task.

IMAGE CAPTIONING

Object Detection via a Multi-Region and Semantic Segmentation-Aware CNN Model

ICCV 2015 gidariss/mrcnn-object-detection

We propose an object detection system that relies on a multi-region deep convolutional neural network (CNN) that also encodes semantic segmentation-aware features.

OBJECT DETECTION OBJECT LOCALIZATION SEMANTIC SEGMENTATION

Discriminative Learning of Deep Convolutional Feature Point Descriptors

ICCV 2015 etrulls/deepdesc-release

Deep learning has revolutionalized image-level tasks such as classification, but patch-level tasks, such as correspondence, still rely on hand-crafted features, e. g. SIFT.

DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers

ICCV 2015 aghodrati/deepproposal

We generate hypotheses in a sliding-window fashion over different activation layers and show that the final convolutional layers can find the object of interest with high recall but poor localization due to the coarseness of the feature maps.