ICCV 2015

Fast R-CNN

ICCV 2015 facebookresearch/detectron

Fast R-CNN builds on previous work to efficiently classify object proposals using deep convolutional networks.

OBJECT DETECTION

Holistically-Nested Edge Detection

ICCV 2015 tensorpack/tensorpack

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

Conditional Random Fields as Recurrent Neural Networks

ICCV 2015 torrvision/crfasrnn

Pixel-level labelling tasks, such as semantic segmentation, play a central role in image understanding.

REAL-TIME SEMANTIC SEGMENTATION

FlowNet: Learning Optical Flow with Convolutional Networks

ICCV 2015 msracver/Deep-Feature-Flow

Optical flow estimation has not been among the tasks where CNNs were successful.

OPTICAL FLOW ESTIMATION

VQA: Visual Question Answering

ICCV 2015 ramprs/grad-cam

Given an image and a natural language question about the image, the task is to provide an accurate natural language answer.

IMAGE CAPTIONING VISUAL QUESTION ANSWERING

Multimodal Convolutional Neural Networks for Matching Image and Sentence

ICCV 2015 ryankiros/visual-semantic-embedding

In this paper, we propose multimodal convolutional neural networks (m-CNNs) for matching image and sentence.

Learning Deconvolution Network for Semantic Segmentation

ICCV 2015 HyeonwooNoh/DeconvNet

We propose a novel semantic segmentation algorithm by learning a deconvolution network.

SEMANTIC SEGMENTATION

Describing Videos by Exploiting Temporal Structure

ICCV 2015 yaoli/arctic-capgen-vid

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

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