30,514 research outputs found
Learning Video Object Segmentation with Visual Memory
This paper addresses the task of segmenting moving objects in unconstrained
videos. We introduce a novel two-stream neural network with an explicit memory
module to achieve this. The two streams of the network encode spatial and
temporal features in a video sequence respectively, while the memory module
captures the evolution of objects over time. The module to build a "visual
memory" in video, i.e., a joint representation of all the video frames, is
realized with a convolutional recurrent unit learned from a small number of
training video sequences. Given a video frame as input, our approach assigns
each pixel an object or background label based on the learned spatio-temporal
features as well as the "visual memory" specific to the video, acquired
automatically without any manually-annotated frames. The visual memory is
implemented with convolutional gated recurrent units, which allows to propagate
spatial information over time. We evaluate our method extensively on two
benchmarks, DAVIS and Freiburg-Berkeley motion segmentation datasets, and show
state-of-the-art results. For example, our approach outperforms the top method
on the DAVIS dataset by nearly 6%. We also provide an extensive ablative
analysis to investigate the influence of each component in the proposed
framework
Sim2Real View Invariant Visual Servoing by Recurrent Control
Humans are remarkably proficient at controlling their limbs and tools from a
wide range of viewpoints and angles, even in the presence of optical
distortions. In robotics, this ability is referred to as visual servoing:
moving a tool or end-point to a desired location using primarily visual
feedback. In this paper, we study how viewpoint-invariant visual servoing
skills can be learned automatically in a robotic manipulation scenario. To this
end, we train a deep recurrent controller that can automatically determine
which actions move the end-point of a robotic arm to a desired object. The
problem that must be solved by this controller is fundamentally ambiguous:
under severe variation in viewpoint, it may be impossible to determine the
actions in a single feedforward operation. Instead, our visual servoing system
must use its memory of past movements to understand how the actions affect the
robot motion from the current viewpoint, correcting mistakes and gradually
moving closer to the target. This ability is in stark contrast to most visual
servoing methods, which either assume known dynamics or require a calibration
phase. We show how we can learn this recurrent controller using simulated data
and a reinforcement learning objective. We then describe how the resulting
model can be transferred to a real-world robot by disentangling perception from
control and only adapting the visual layers. The adapted model can servo to
previously unseen objects from novel viewpoints on a real-world Kuka IIWA
robotic arm. For supplementary videos, see:
https://fsadeghi.github.io/Sim2RealViewInvariantServoComment: Supplementary video:
https://fsadeghi.github.io/Sim2RealViewInvariantServ
Delving Deeper into Convolutional Networks for Learning Video Representations
We propose an approach to learn spatio-temporal features in videos from
intermediate visual representations we call "percepts" using
Gated-Recurrent-Unit Recurrent Networks (GRUs).Our method relies on percepts
that are extracted from all level of a deep convolutional network trained on
the large ImageNet dataset. While high-level percepts contain highly
discriminative information, they tend to have a low-spatial resolution.
Low-level percepts, on the other hand, preserve a higher spatial resolution
from which we can model finer motion patterns. Using low-level percepts can
leads to high-dimensionality video representations. To mitigate this effect and
control the model number of parameters, we introduce a variant of the GRU model
that leverages the convolution operations to enforce sparse connectivity of the
model units and share parameters across the input spatial locations.
We empirically validate our approach on both Human Action Recognition and
Video Captioning tasks. In particular, we achieve results equivalent to
state-of-art on the YouTube2Text dataset using a simpler text-decoder model and
without extra 3D CNN features.Comment: ICLR 201
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