CVPR 2016 全部文章摘要阅读

1 Deep CompositionalCaptioning: Describing Novel Object Categories Without Paired Training Data.

Lisa Anne Hendricks, Subhashini Venugopalan, Marcus Rohrbach, Raymond Mooney, Kate Saenko, Trevor Darrell

一般的工作是他们可以拍照-还描述了句子库中未出现的物体。In this work, we propose the Deep Compositional Captioner (DCC) to address the task of generating descriptions of novel objects which are not present in paired image-sentence datasets.

2 Generation and Comprehension of Unambiguous ObjectDescriptions.

Junhua Mao, Jonathan Huang, Alexander Toshev, Oana Camburu, Alan L. Yuille, Kevin Murphy

也是图像描述的一种形式，作者指出，这种图像描述可以通过客观的评价指标进行。We propose a method that can generate an unambiguous description (known as a referring expression) of a specific object or region in an image, and which can also comprehend or interpret such an expression to infer which object is being described.

3   Stacked Attention Networks for Image Question Answering. 

Zichao Yang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Smola

文章用于图像的问答，比如提问图片中有几个人，做出相应回答，感觉更难啊。这里的创新是采用了堆栈式网络。This paper presents stacked attention networks (SANs) that learn to answer natural language questions from images. 

4   Image Question Answering Using Convolutional Neural Network With Dynamic Parameter Prediction. 

Hyeonwoo Noh, Paul Hongsuck Seo, Bohyung Han

图像问答，这里的创新是添加了一个自适应参数层，其中自适用参数采用GRU学习。We tackle image question answering (ImageQA) problem by learning a convolutional neural network (CNN) with a dynamic parameter layer whose weights are determined adaptively based on questions.

5   Neural Module Networks. 

Jacob Andreas, Marcus Rohrbach, Trevor Darrell, Dan Klein

目的也是图像问答，创新是同时考虑两方面内容：表示问题与语言模型（囧，不都是同时考虑这两方面么）。还没仔细看好。Our approach decomposes questions into their linguistic substructures, and uses these structures to dynamically instantiate modular networks (with reusable components for recognizing dogs, classifying colors, etc.)

SPOTLIGHT SESSION

Language and Vision

Monday, June 27th, 10:05AM - 10:30AM.

6   Learning Deep Representations of Fine-Grained Visual Descriptions. 

Scott Reed, Zeynep Akata, Honglak Lee , Bernt Schiele

处理Zero-Shot问题，没有仔细看明白创新点。大致是分为两部分，从题目看，其实就是采用深度学习获得细粒度特征表示。Our proposed models train end-to-end to align with the fine-grained and category-specific content of images. Natural language provides a flexible and compact way of encoding only the salient visual aspects for distinguishing categories. 

7   Multi-Cue Zero-Shot Learning With Strong Supervision. 

Zeynep Akata, Mateusz Malinowski, Mario Fritz, Bernt Schiele

虽然是Zero-Shot，但是不相关。不是深度学习的处理方式。

8   Latent Embeddings for Zero-Shot Classification. 

Yongqin Xian, Zeynep Akata, Gaurav Sharma, Quynh Nguyen, Matthias Hein, Bernt Schiele

虽然是Zero-Shot，但是不相关。不是深度学习的处理方式。

9   One-Shot Learning of Scene Locations via Feature Trajectory Transfer. 

Roland Kwitt, Sebastian Hegenbart, Marc Niethammer

虽然是One-Shot，但是不相关。不是深度学习的处理方式。

10   Learning Attributes Equals Multi-Source Domain Generalization. 

Chuang Gan, Tianbao Yang, Boqing Gong

不是深度学习的处理方式。

11   Anticipating Visual Representations From Unlabeled Video. 

Carl Vondrick, Hamed Pirsiavash, Antonio Torralba

核心内容就是利用深度学习预测下一时刻或者时间段的行为活动。We present a framework that capitalizes on temporal structure in unlabeled video to learn to anticipate human actions and objects. The key idea behind our approach is that we can train deep networks to predict the visual representation of images in the future.

ORAL SESSION

Matching and Alignment

Monday, June 27th, 9:00AM - 10:05AM.

12   Learning to Assign Orientations to Feature Points. 

Kwang Moo Yi, Yannick Verdie, Pascal Fua, Vincent Lepetit

内容是利用深度学习给特征点标定方向，用于匹配算法，并且提出了一种新的Activation函数。We show how to train a Convolutional Neural Network to assign a canonical orientation to feature points given an image patch centered on the feature point.

13   Learning Dense Correspondence via 3D-Guided Cycle Consistency. 

Tinghui Zhou, Philipp Krähenbuhl, Mathieu Aubry, Qixing Huang, Alexei A. Efros

也是利用深度学习，目的是探究跨实例相似性。 We exploit this consistency as a supervisory signal to train a convolutional neural network to predict cross-instance correspondences between pairs of images depicting objects of the same category.

14   The Global Patch Collider. 

Shenlong Wang, Sean Ryan Fanello, Christoph Rhemann, Shahram Izadi, Pushmeet Kohli

不是深度学习。

15   Joint Probabilistic Matching Using m-Best Solutions. 

Seyed Hamid Rezatofighi, Anton Milan, Zhen Zhang, Qinfeng Shi, Anthony Dick, Ian Reid

不是深度学习。

16   Face Alignment Across Large Poses: A 3D Solution. 

Xiangyu Zhu, Zhen Lei, Xiaoming Liu, Hailin Shi, Stan Z. Li

采用深度学习提出一种三维人脸矫正技术。we propose a solution to the three problems in an new alignment framework, called 3D Dense Face Alignment (3DDFA), in which a dense 3D face model is fitted to the image via convolutional neutral network (CNN)

SPOTLIGHT SESSION

Segmentation and Contour Detection

Monday, June 27th, 10:05AM - 10:30AM.

17   Interactive Segmentation on RGBD Images via Cue Selection. 

Jie Feng, Brian Price, Scott Cohen, Shih-Fu Chang

不是深度学习。

18   Layered Scene Decomposition via the Occlusion-CRF. 

Chen Liu, Pushmeet Kohli, Yasutaka Furukawa

不是深度学习。

19   Affinity CNN: Learning Pixel-Centric Pairwise Relations for Figure/Ground Embedding. 

Michael Maire, Takuya Narihira, Stella X. Yu

POSTER SESSION

Poster Session 1-1. Monday, June 27th, 10:30AM - 12:30PM.

Images and Language

22   What Value Do Explicit High Level Concepts Have in Vision to Language Problems?. 

Qi Wu, Chunhua Shen, Lingqiao Liu, Anthony Dick, Anton van den Hengel

文章考虑图像描述，但由于现有的方法都是直接将图像中的物体映射为文本信息，并没有考虑高层语义信息，文章提出考虑高层信息的创新点。We propose a method of incorporating high-level concepts into the successful CNN-RNN approach, and show that it achieves a significant improvement on the state-of-the-art in both image captioning and visual question answering. 

Edge Contour Detection

23   Fast Detection of Curved Edges at Low SNR. 

Nati Ofir, Meirav Galun, Boaz Nadler, Ronen Basri

不是深度学习。解决的问题相当于是在消除噪声的同时进行边缘检测。

24   Object Skeleton Extraction in Natural Images by Fusing Scale-Associated Deep Side Outputs. 

Wei Shen, Kai Zhao, Yuan Jiang, Yan Wang, Zhijiang Zhang, Xiang Bai

利用全卷积网络提出一种骨架提取算法，貌似就是之前博客里看到的文章。In this paper, we present a fully convolutional network with multiple scale-associated side outputs to address this problem. By observing the relationship between the receptive field sizes of the sequential stages in the network and the skeleton scales they can capture, we introduce a scale-associated side output to each stage

25   Learning Relaxed Deep Supervision for Better Edge Detection. 

Yu Liu, Michael S. Lew

一种基于深度学习的边缘检测算法。We propose using relaxed deep supervision (RDS) within convolutional neural networks for edge detection. 

26   Occlusion Boundary Detection via Deep Exploration of Context. 

Huan Fu, Chaohui Wang, Dacheng Tao, Michael J. Black

遮挡边缘检测。基于深度学习 In this paper, we improve occlusion boundary detection via enhanced exploration of contextual information (e.g., local structural boundary patterns, observations from surrounding regions, and temporal context), and in doing so develop a novel approach based on convolutional neural networks (CNNs) and conditional random fields (CRFs)

27   SemiContour: A Semi-Supervised Learning Approach for Contour Detection. 

Zizhao Zhang, Fuyong Xing, Xiaoshuang Shi, Lin Yang

不是深度学习。轮廓检测。

Feature Extraction and Description

28   Learning to Localize Little Landmarks. 

Saurabh Singh, Derek Hoiem, David Forsyth

不是深度学习，特征提取。

29   InterActive: Inter-Layer Activeness Propagation. 

Lingxi Xie, Liang Zheng, Jingdong Wang, Alan L. Yuille, Qi Tian

相当于是Activation函数的改进。In this paper, we present InterActive, a novel algorithm which computes the activeness of neurons and network connections. 

30   Exploit Bounding Box Annotations for Multi-Label Object Recognition. 

Hao Yang, Joey Tianyi Zhou, Yu Zhang, Bin-Bin Gao, Jianxin Wu, Jianfei Cai