2,116 research outputs found
ParseNet: Looking Wider to See Better
We present a technique for adding global context to deep convolutional
networks for semantic segmentation. The approach is simple, using the average
feature for a layer to augment the features at each location. In addition, we
study several idiosyncrasies of training, significantly increasing the
performance of baseline networks (e.g. from FCN). When we add our proposed
global feature, and a technique for learning normalization parameters, accuracy
increases consistently even over our improved versions of the baselines. Our
proposed approach, ParseNet, achieves state-of-the-art performance on SiftFlow
and PASCAL-Context with small additional computational cost over baselines, and
near current state-of-the-art performance on PASCAL VOC 2012 semantic
segmentation with a simple approach. Code is available at
https://github.com/weiliu89/caffe/tree/fcn .Comment: ICLR 2016 submissio
Solving Visual Madlibs with Multiple Cues
This paper focuses on answering fill-in-the-blank style multiple choice
questions from the Visual Madlibs dataset. Previous approaches to Visual
Question Answering (VQA) have mainly used generic image features from networks
trained on the ImageNet dataset, despite the wide scope of questions. In
contrast, our approach employs features derived from networks trained for
specialized tasks of scene classification, person activity prediction, and
person and object attribute prediction. We also present a method for selecting
sub-regions of an image that are relevant for evaluating the appropriateness of
a putative answer. Visual features are computed both from the whole image and
from local regions, while sentences are mapped to a common space using a simple
normalized canonical correlation analysis (CCA) model. Our results show a
significant improvement over the previous state of the art, and indicate that
answering different question types benefits from examining a variety of image
cues and carefully choosing informative image sub-regions
Synthesizing Training Data for Object Detection in Indoor Scenes
Detection of objects in cluttered indoor environments is one of the key
enabling functionalities for service robots. The best performing object
detection approaches in computer vision exploit deep Convolutional Neural
Networks (CNN) to simultaneously detect and categorize the objects of interest
in cluttered scenes. Training of such models typically requires large amounts
of annotated training data which is time consuming and costly to obtain. In
this work we explore the ability of using synthetically generated composite
images for training state-of-the-art object detectors, especially for object
instance detection. We superimpose 2D images of textured object models into
images of real environments at variety of locations and scales. Our experiments
evaluate different superimposition strategies ranging from purely image-based
blending all the way to depth and semantics informed positioning of the object
models into real scenes. We demonstrate the effectiveness of these object
detector training strategies on two publicly available datasets, the
GMU-Kitchens and the Washington RGB-D Scenes v2. As one observation, augmenting
some hand-labeled training data with synthetic examples carefully composed onto
scenes yields object detectors with comparable performance to using much more
hand-labeled data. Broadly, this work charts new opportunities for training
detectors for new objects by exploiting existing object model repositories in
either a purely automatic fashion or with only a very small number of
human-annotated examples.Comment: Added more experiments and link to project webpag
Video Highlight Prediction Using Audience Chat Reactions
Sports channel video portals offer an exciting domain for research on
multimodal, multilingual analysis. We present methods addressing the problem of
automatic video highlight prediction based on joint visual features and textual
analysis of the real-world audience discourse with complex slang, in both
English and traditional Chinese. We present a novel dataset based on League of
Legends championships recorded from North American and Taiwanese Twitch.tv
channels (will be released for further research), and demonstrate strong
results on these using multimodal, character-level CNN-RNN model architectures.Comment: EMNLP 201
Fast Single Shot Detection and Pose Estimation
For applications in navigation and robotics, estimating the 3D pose of
objects is as important as detection. Many approaches to pose estimation rely
on detecting or tracking parts or keypoints [11, 21]. In this paper we build on
a recent state-of-the-art convolutional network for slidingwindow detection
[10] to provide detection and rough pose estimation in a single shot, without
intermediate stages of detecting parts or initial bounding boxes. While not the
first system to treat pose estimation as a categorization problem, this is the
first attempt to combine detection and pose estimation at the same level using
a deep learning approach. The key to the architecture is a deep convolutional
network where scores for the presence of an object category, the offset for its
location, and the approximate pose are all estimated on a regular grid of
locations in the image. The resulting system is as accurate as recent work on
pose estimation (42.4% 8 View mAVP on Pascal 3D+ [21] ) and significantly
faster (46 frames per second (FPS) on a TITAN X GPU). This approach to
detection and rough pose estimation is fast and accurate enough to be widely
applied as a pre-processing step for tasks including high-accuracy pose
estimation, object tracking and localization, and vSLAM
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