66 research outputs found
Fooling Polarization-based Vision using Locally Controllable Polarizing Projection
Polarization is a fundamental property of light that encodes abundant
information regarding surface shape, material, illumination and viewing
geometry. The computer vision community has witnessed a blossom of
polarization-based vision applications, such as reflection removal,
shape-from-polarization, transparent object segmentation and color constancy,
partially due to the emergence of single-chip mono/color polarization sensors
that make polarization data acquisition easier than ever. However, is
polarization-based vision vulnerable to adversarial attacks? If so, is that
possible to realize these adversarial attacks in the physical world, without
being perceived by human eyes? In this paper, we warn the community of the
vulnerability of polarization-based vision, which can be more serious than
RGB-based vision. By adapting a commercial LCD projector, we achieve locally
controllable polarizing projection, which is successfully utilized to fool
state-of-the-art polarization-based vision algorithms for glass segmentation
and color constancy. Compared with existing physical attacks on RGB-based
vision, which always suffer from the trade-off between attack efficacy and eye
conceivability, the adversarial attackers based on polarizing projection are
contact-free and visually imperceptible, since naked human eyes can rarely
perceive the difference of viciously manipulated polarizing light and ordinary
illumination. This poses unprecedented risks on polarization-based vision, both
in the monochromatic and trichromatic domain, for which due attentions should
be paid and counter measures be considered
Clearer Frames, Anytime: Resolving Velocity Ambiguity in Video Frame Interpolation
Existing video frame interpolation (VFI) methods blindly predict where each
object is at a specific timestep t ("time indexing"), which struggles to
predict precise object movements. Given two images of a baseball, there are
infinitely many possible trajectories: accelerating or decelerating, straight
or curved. This often results in blurry frames as the method averages out these
possibilities. Instead of forcing the network to learn this complicated
time-to-location mapping implicitly together with predicting the frames, we
provide the network with an explicit hint on how far the object has traveled
between start and end frames, a novel approach termed "distance indexing". This
method offers a clearer learning goal for models, reducing the uncertainty tied
to object speeds. We further observed that, even with this extra guidance,
objects can still be blurry especially when they are equally far from both
input frames (i.e., halfway in-between), due to the directional ambiguity in
long-range motion. To solve this, we propose an iterative reference-based
estimation strategy that breaks down a long-range prediction into several
short-range steps. When integrating our plug-and-play strategies into
state-of-the-art learning-based models, they exhibit markedly sharper outputs
and superior perceptual quality in arbitrary time interpolations, using a
uniform distance indexing map in the same format as time indexing.
Additionally, distance indexing can be specified pixel-wise, which enables
temporal manipulation of each object independently, offering a novel tool for
video editing tasks like re-timing.Comment: Project page: https://zzh-tech.github.io/InterpAny-Clearer/ ; Code:
https://github.com/zzh-tech/InterpAny-Cleare
Detection of the Diffuse Supernova Neutrino Background with JUNO
As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO
Bringing Rolling Shutter Images Alive with Dual Reversed Distortion
Rolling shutter (RS) distortion can be interpreted as the result of picking a
row of pixels from instant global shutter (GS) frames over time during the
exposure of the RS camera. This means that the information of each instant GS
frame is partially, yet sequentially, embedded into the row-dependent
distortion. Inspired by this fact, we address the challenging task of reversing
this process, i.e., extracting undistorted GS frames from images suffering from
RS distortion. However, since RS distortion is coupled with other factors such
as readout settings and the relative velocity of scene elements to the camera,
models that only exploit the geometric correlation between temporally adjacent
images suffer from poor generality in processing data with different readout
settings and dynamic scenes with both camera motion and object motion. In this
paper, instead of two consecutive frames, we propose to exploit a pair of
images captured by dual RS cameras with reversed RS directions for this highly
challenging task. Grounded on the symmetric and complementary nature of dual
reversed distortion, we develop a novel end-to-end model, IFED, to generate
dual optical flow sequence through iterative learning of the velocity field
during the RS time. Extensive experimental results demonstrate that IFED is
superior to naive cascade schemes, as well as the state-of-the-art which
utilizes adjacent RS images. Most importantly, although it is trained on a
synthetic dataset, IFED is shown to be effective at retrieving GS frame
sequences from real-world RS distorted images of dynamic scenes. Code is
available at https://github.com/zzh-tech/Dual-Reversed-RS.Comment: ECCV2022 Ora
Development of Robot Patient Lower Limbs to Reproduce the Sit-to-Stand Movement with Correct and Incorrect Applications of Transfer Skills by Nurses
Recently, human patient simulators have been widely developed as substitutes for real patients with the objective of applying them as training tools in nursing education. Such simulated training is perceived as beneficial for imparting the required practical skills to students. Considering the aging world population, this study aimed to develop a robot patient for training nursing students in the sit-to-stand (STS) transfer skill, which is indispensable in caring for elderly people. To assess a student’s skill, the robot patient should be able to access the skill correctness and behave according to whether the skill is correctly or incorrectly implemented. Accordingly, an STS control method was proposed to reproduce the different STS movements during correct and incorrect applications of the skill by the nurses. The lower limbs of a prototype robot were redesigned to provide an active joint with a compliant unit, which enables the measurement of external torque and flexibility of the human joint to be reproduced. An experiment was conducted with four nurse teachers, each of whom was asked to demonstrate both correct and incorrect STS transfer skills. The results of the external torque and joint torque measured in robot’s lower limbs revealed that a significant difference (p < 0.05) between correct and incorrect skills. It also indicates the introduction of the proposed control method for the robot can satisfy the requirement of the assessment of STS skill. Among the various measurements conducted, the external torque of the hip joint exhibited the most significant difference and therefore represented the most robust measure for assessing whether the STS transfer skill was correctly applied
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