121,000 research outputs found
Clothing Co-Parsing by Joint Image Segmentation and Labeling
This paper aims at developing an integrated system of clothing co-parsing, in
order to jointly parse a set of clothing images (unsegmented but annotated with
tags) into semantic configurations. We propose a data-driven framework
consisting of two phases of inference. The first phase, referred as "image
co-segmentation", iterates to extract consistent regions on images and jointly
refines the regions over all images by employing the exemplar-SVM (E-SVM)
technique [23]. In the second phase (i.e. "region co-labeling"), we construct a
multi-image graphical model by taking the segmented regions as vertices, and
incorporate several contexts of clothing configuration (e.g., item location and
mutual interactions). The joint label assignment can be solved using the
efficient Graph Cuts algorithm. In addition to evaluate our framework on the
Fashionista dataset [30], we construct a dataset called CCP consisting of 2098
high-resolution street fashion photos to demonstrate the performance of our
system. We achieve 90.29% / 88.23% segmentation accuracy and 65.52% / 63.89%
recognition rate on the Fashionista and the CCP datasets, respectively, which
are superior compared with state-of-the-art methods.Comment: 8 pages, 5 figures, CVPR 201
Generative Face Completion
In this paper, we propose an effective face completion algorithm using a deep
generative model. Different from well-studied background completion, the face
completion task is more challenging as it often requires to generate
semantically new pixels for the missing key components (e.g., eyes and mouths)
that contain large appearance variations. Unlike existing nonparametric
algorithms that search for patches to synthesize, our algorithm directly
generates contents for missing regions based on a neural network. The model is
trained with a combination of a reconstruction loss, two adversarial losses and
a semantic parsing loss, which ensures pixel faithfulness and local-global
contents consistency. With extensive experimental results, we demonstrate
qualitatively and quantitatively that our model is able to deal with a large
area of missing pixels in arbitrary shapes and generate realistic face
completion results.Comment: Accepted by CVPR 201
An Expressive Deep Model for Human Action Parsing from A Single Image
This paper aims at one newly raising task in vision and multimedia research:
recognizing human actions from still images. Its main challenges lie in the
large variations in human poses and appearances, as well as the lack of
temporal motion information. Addressing these problems, we propose to develop
an expressive deep model to naturally integrate human layout and surrounding
contexts for higher level action understanding from still images. In
particular, a Deep Belief Net is trained to fuse information from different
noisy sources such as body part detection and object detection. To bridge the
semantic gap, we used manually labeled data to greatly improve the
effectiveness and efficiency of the pre-training and fine-tuning stages of the
DBN training. The resulting framework is shown to be robust to sometimes
unreliable inputs (e.g., imprecise detections of human parts and objects), and
outperforms the state-of-the-art approaches.Comment: 6 pages, 8 figures, ICME 201
Change blindness: eradication of gestalt strategies
Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task
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