175 research outputs found
Optimization on fixed low latency implementation of GBT protocol in FPGA
In the upgrade of ATLAS experiment, the front-end electronics components are
subjected to a large radiation background. Meanwhile high speed optical links
are required for the data transmission between the on-detector and off-detector
electronics. The GBT architecture and the Versatile Link (VL) project are
designed by CERN to support the 4.8 Gbps line rate bidirectional high-speed
data transmission which is called GBT link. In the ATLAS upgrade, besides the
link with on-detector, the GBT link is also used between different off-detector
systems. The GBTX ASIC is designed for the on-detector front-end,
correspondingly for the off-detector electronics, the GBT architecture is
implemented in Field Programmable Gate Arrays (FPGA). CERN launches the
GBT-FPGA project to provide examples in different types of FPGA. In the ATLAS
upgrade framework, the Front-End LInk eXchange (FELIX) system is used to
interface the front-end electronics of several ATLAS subsystems. The GBT link
is used between them, to transfer the detector data and the timing, trigger,
control and monitoring information. The trigger signal distributed in the
down-link from FELIX to the front-end requires a fixed and low latency. In this
paper, several optimizations on the GBT-FPGA IP core are introduced, to achieve
a lower fixed latency. For FELIX, a common firmware will be used to interface
different front-ends with support of both GBT modes: the forward error
correction mode and the wide mode. The modified GBT-FPGA core has the ability
to switch between the GBT modes without FPGA reprogramming. The system clock
distribution of the multi-channel FELIX firmware is also discussed in this
paper
Learning Sim-to-Real Dense Object Descriptors for Robotic Manipulation
It is crucial to address the following issues for ubiquitous robotics
manipulation applications: (a) vision-based manipulation tasks require the
robot to visually learn and understand the object with rich information like
dense object descriptors; and (b) sim-to-real transfer in robotics aims to
close the gap between simulated and real data. In this paper, we present
Sim-to-Real Dense Object Nets (SRDONs), a dense object descriptor that not only
understands the object via appropriate representation but also maps simulated
and real data to a unified feature space with pixel consistency. We proposed an
object-to-object matching method for image pairs from different scenes and
different domains. This method helps reduce the effort of training data from
real-world by taking advantage of public datasets, such as GraspNet. With
sim-to-real object representation consistency, our SRDONs can serve as a
building block for a variety of sim-to-real manipulation tasks. We demonstrate
in experiments that pre-trained SRDONs significantly improve performances on
unseen objects and unseen visual environments for various robotic tasks with
zero real-world training.Comment: Accepted to International Conference on Robotics and Automation
(ICRA) 202
TediGAN: Text-Guided Diverse Face Image Generation and Manipulation
In this work, we propose TediGAN, a novel framework for multi-modal image
generation and manipulation with textual descriptions. The proposed method
consists of three components: StyleGAN inversion module, visual-linguistic
similarity learning, and instance-level optimization. The inversion module maps
real images to the latent space of a well-trained StyleGAN. The
visual-linguistic similarity learns the text-image matching by mapping the
image and text into a common embedding space. The instance-level optimization
is for identity preservation in manipulation. Our model can produce diverse and
high-quality images with an unprecedented resolution at 1024. Using a control
mechanism based on style-mixing, our TediGAN inherently supports image
synthesis with multi-modal inputs, such as sketches or semantic labels, with or
without instance guidance. To facilitate text-guided multi-modal synthesis, we
propose the Multi-Modal CelebA-HQ, a large-scale dataset consisting of real
face images and corresponding semantic segmentation map, sketch, and textual
descriptions. Extensive experiments on the introduced dataset demonstrate the
superior performance of our proposed method. Code and data are available at
https://github.com/weihaox/TediGAN.Comment: CVPR 2021. Code: https://github.com/weihaox/TediGAN Data:
https://github.com/weihaox/Multi-Modal-CelebA-HQ Video:
https://youtu.be/L8Na2f5viA
BSN++: Complementary Boundary Regressor with Scale-Balanced Relation Modeling for Temporal Action Proposal Generation
Generating human action proposals in untrimmed videos is an important yet
challenging task with wide applications. Current methods often suffer from the
noisy boundary locations and the inferior quality of confidence scores used for
proposal retrieving. In this paper, we present BSN++, a new framework which
exploits complementary boundary regressor and relation modeling for temporal
proposal generation. First, we propose a novel boundary regressor based on the
complementary characteristics of both starting and ending boundary classifiers.
Specifically, we utilize the U-shaped architecture with nested skip connections
to capture rich contexts and introduce bi-directional boundary matching
mechanism to improve boundary precision. Second, to account for the
proposal-proposal relations ignored in previous methods, we devise a proposal
relation block to which includes two self-attention modules from the aspects of
position and channel. Furthermore, we find that there inevitably exists data
imbalanced problems in the positive/negative proposals and temporal durations,
which harm the model performance on tail distributions. To relieve this issue,
we introduce the scale-balanced re-sampling strategy. Extensive experiments are
conducted on two popular benchmarks: ActivityNet-1.3 and THUMOS14, which
demonstrate that BSN++ achieves the state-of-the-art performance.Comment: Submitted to AAAI21. arXiv admin note: substantial text overlap with
arXiv:2007.0988
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