6,326 research outputs found
Activity recognition from videos with parallel hypergraph matching on GPUs
In this paper, we propose a method for activity recognition from videos based
on sparse local features and hypergraph matching. We benefit from special
properties of the temporal domain in the data to derive a sequential and fast
graph matching algorithm for GPUs.
Traditionally, graphs and hypergraphs are frequently used to recognize
complex and often non-rigid patterns in computer vision, either through graph
matching or point-set matching with graphs. Most formulations resort to the
minimization of a difficult discrete energy function mixing geometric or
structural terms with data attached terms involving appearance features.
Traditional methods solve this minimization problem approximately, for instance
with spectral techniques.
In this work, instead of solving the problem approximatively, the exact
solution for the optimal assignment is calculated in parallel on GPUs. The
graphical structure is simplified and regularized, which allows to derive an
efficient recursive minimization algorithm. The algorithm distributes
subproblems over the calculation units of a GPU, which solves them in parallel,
allowing the system to run faster than real-time on medium-end GPUs
Mutual Information Regularization for Weakly-supervised RGB-D Salient Object Detection
In this paper, we present a weakly-supervised RGB-D salient object detection
model via scribble supervision. Specifically, as a multimodal learning task, we
focus on effective multimodal representation learning via inter-modal mutual
information regularization. In particular, following the principle of
disentangled representation learning, we introduce a mutual information upper
bound with a mutual information minimization regularizer to encourage the
disentangled representation of each modality for salient object detection.
Based on our multimodal representation learning framework, we introduce an
asymmetric feature extractor for our multimodal data, which is proven more
effective than the conventional symmetric backbone setting. We also introduce
multimodal variational auto-encoder as stochastic prediction refinement
techniques, which takes pseudo labels from the first training stage as
supervision and generates refined prediction. Experimental results on benchmark
RGB-D salient object detection datasets verify both effectiveness of our
explicit multimodal disentangled representation learning method and the
stochastic prediction refinement strategy, achieving comparable performance
with the state-of-the-art fully supervised models. Our code and data are
available at: https://github.com/baneitixiaomai/MIRV.Comment: IEEE Transactions on Circuits and Systems for Video Technology 202
Visual-hint Boundary to Segment Algorithm for Image Segmentation
Image segmentation has been a very active research topic in image analysis
area. Currently, most of the image segmentation algorithms are designed based
on the idea that images are partitioned into a set of regions preserving
homogeneous intra-regions and inhomogeneous inter-regions. However, human
visual intuition does not always follow this pattern. A new image segmentation
method named Visual-Hint Boundary to Segment (VHBS) is introduced, which is
more consistent with human perceptions. VHBS abides by two visual hint rules
based on human perceptions: (i) the global scale boundaries tend to be the real
boundaries of the objects; (ii) two adjacent regions with quite different
colors or textures tend to result in the real boundaries between them. It has
been demonstrated by experiments that, compared with traditional image
segmentation method, VHBS has better performance and also preserves higher
computational efficiency.Comment: 45 page
Superpixel segmentation based on anisotropic edge strength
Superpixel segmentation can benefit from the use of an appropriate method to measure edge strength. In this paper, we present such a method based on the first derivative of anisotropic Gaussian kernels. The kernels can capture the position, direction, prominence, and scale of the edge to be detected. We incorporate the anisotropic edge strength into the distance measure between neighboring superpixels, thereby improving the performance of an existing graph-based superpixel segmentation method. Experimental results validate the superiority of our method in generating superpixels over the competing methods. It is also illustrated that the proposed superpixel segmentation method can facilitate subsequent saliency detection
PiCANet: Learning Pixel-wise Contextual Attention for Saliency Detection
Contexts play an important role in the saliency detection task. However,
given a context region, not all contextual information is helpful for the final
task. In this paper, we propose a novel pixel-wise contextual attention
network, i.e., the PiCANet, to learn to selectively attend to informative
context locations for each pixel. Specifically, for each pixel, it can generate
an attention map in which each attention weight corresponds to the contextual
relevance at each context location. An attended contextual feature can then be
constructed by selectively aggregating the contextual information. We formulate
the proposed PiCANet in both global and local forms to attend to global and
local contexts, respectively. Both models are fully differentiable and can be
embedded into CNNs for joint training. We also incorporate the proposed models
with the U-Net architecture to detect salient objects. Extensive experiments
show that the proposed PiCANets can consistently improve saliency detection
performance. The global and local PiCANets facilitate learning global contrast
and homogeneousness, respectively. As a result, our saliency model can detect
salient objects more accurately and uniformly, thus performing favorably
against the state-of-the-art methods
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