24,384 research outputs found
Reversible Recursive Instance-level Object Segmentation
In this work, we propose a novel Reversible Recursive Instance-level Object
Segmentation (R2-IOS) framework to address the challenging instance-level
object segmentation task. R2-IOS consists of a reversible proposal refinement
sub-network that predicts bounding box offsets for refining the object proposal
locations, and an instance-level segmentation sub-network that generates the
foreground mask of the dominant object instance in each proposal. By being
recursive, R2-IOS iteratively optimizes the two sub-networks during joint
training, in which the refined object proposals and improved segmentation
predictions are alternately fed into each other to progressively increase the
network capabilities. By being reversible, the proposal refinement sub-network
adaptively determines an optimal number of refinement iterations required for
each proposal during both training and testing. Furthermore, to handle multiple
overlapped instances within a proposal, an instance-aware denoising autoencoder
is introduced into the segmentation sub-network to distinguish the dominant
object from other distracting instances. Extensive experiments on the
challenging PASCAL VOC 2012 benchmark well demonstrate the superiority of
R2-IOS over other state-of-the-art methods. In particular, the
over classes at IoU achieves , which significantly
outperforms the results of by PFN~\cite{PFN} and
by~\cite{liu2015multi}.Comment: 9 page
Coupled Depth Learning
In this paper we propose a method for estimating depth from a single image
using a coarse to fine approach. We argue that modeling the fine depth details
is easier after a coarse depth map has been computed. We express a global
(coarse) depth map of an image as a linear combination of a depth basis learned
from training examples. The depth basis captures spatial and statistical
regularities and reduces the problem of global depth estimation to the task of
predicting the input-specific coefficients in the linear combination. This is
formulated as a regression problem from a holistic representation of the image.
Crucially, the depth basis and the regression function are {\bf coupled} and
jointly optimized by our learning scheme. We demonstrate that this results in a
significant improvement in accuracy compared to direct regression of depth
pixel values or approaches learning the depth basis disjointly from the
regression function. The global depth estimate is then used as a guidance by a
local refinement method that introduces depth details that were not captured at
the global level. Experiments on the NYUv2 and KITTI datasets show that our
method outperforms the existing state-of-the-art at a considerably lower
computational cost for both training and testing.Comment: 10 pages, 3 Figures, 4 Tables with quantitative evaluation
Scene Graph Generation with External Knowledge and Image Reconstruction
Scene graph generation has received growing attention with the advancements
in image understanding tasks such as object detection, attributes and
relationship prediction,~\etc. However, existing datasets are biased in terms
of object and relationship labels, or often come with noisy and missing
annotations, which makes the development of a reliable scene graph prediction
model very challenging. In this paper, we propose a novel scene graph
generation algorithm with external knowledge and image reconstruction loss to
overcome these dataset issues. In particular, we extract commonsense knowledge
from the external knowledge base to refine object and phrase features for
improving generalizability in scene graph generation. To address the bias of
noisy object annotations, we introduce an auxiliary image reconstruction path
to regularize the scene graph generation network. Extensive experiments show
that our framework can generate better scene graphs, achieving the
state-of-the-art performance on two benchmark datasets: Visual Relationship
Detection and Visual Genome datasets.Comment: 10 pages, 5 figures, Accepted in CVPR 201
Joint coarse-and-fine reasoning for deep optical flow
© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.We propose a novel representation for dense pixel-wise estimation tasks using CNNs that boosts accuracy and reduces training time, by explicitly exploiting joint coarse-and-fine reasoning. The coarse reasoning is performed over a discrete classification space to obtain a general rough solution, while the fine details of the solution are obtained over a continuous regression space. In our approach both components are jointly estimated, which proved to be beneficial for improving estimation accuracy. Additionally, we propose a new network architecture, which combines coarse and fine components by treating the fine estimation as a refinement built on top of the coarse solution, and therefore adding details to the general prediction. We apply our approach to the challenging problem of optical flow estimation and empirically validate it against state-of-the-art CNN-based solutions trained from scratch and tested on large optical flow datasets.Peer ReviewedPostprint (author's final draft
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