68 research outputs found
The Application of Augmented Reality Technology for the Anesthesiology Major
Anesthesiology is an important subject for in-depth research in the fields of clinical anesthesia, critical care medicine, first-aid and resuscitation, and pain treatment. As an important branch of clinical medicine, it has strong practicality and applicability. It has the commonality of clinical medicine and the specialty of anesthesiology. Carrying out anesthesiology practice teaching using augmented reality (AR) to simulate the experimental environment and scene simulation is of great significance to promoting the development of anesthesia practice teaching. This article mainly introduces the augmented reality technology. It not only analyzes the main forms of augmented reality technology in anesthesiology, but also explores the application of augmented reality technology for anesthesiology in the new era
Representation Disparity-aware Distillation for 3D Object Detection
In this paper, we focus on developing knowledge distillation (KD) for compact
3D detectors. We observe that off-the-shelf KD methods manifest their efficacy
only when the teacher model and student counterpart share similar intermediate
feature representations. This might explain why they are less effective in
building extreme-compact 3D detectors where significant representation
disparity arises due primarily to the intrinsic sparsity and irregularity in 3D
point clouds. This paper presents a novel representation disparity-aware
distillation (RDD) method to address the representation disparity issue and
reduce performance gap between compact students and over-parameterized
teachers. This is accomplished by building our RDD from an innovative
perspective of information bottleneck (IB), which can effectively minimize the
disparity of proposal region pairs from student and teacher in features and
logits. Extensive experiments are performed to demonstrate the superiority of
our RDD over existing KD methods. For example, our RDD increases mAP of
CP-Voxel-S to 57.1% on nuScenes dataset, which even surpasses teacher
performance while taking up only 42% FLOPs.Comment: Accepted by ICCV2023. arXiv admin note: text overlap with
arXiv:2205.15156 by other author
Shadow-Aware Dynamic Convolution for Shadow Removal
With a wide range of shadows in many collected images, shadow removal has
aroused increasing attention since uncontaminated images are of vital
importance for many downstream multimedia tasks. Current methods consider the
same convolution operations for both shadow and non-shadow regions while
ignoring the large gap between the color mappings for the shadow region and the
non-shadow region, leading to poor quality of reconstructed images and a heavy
computation burden. To solve this problem, this paper introduces a novel
plug-and-play Shadow-Aware Dynamic Convolution (SADC) module to decouple the
interdependence between the shadow region and the non-shadow region. Inspired
by the fact that the color mapping of the non-shadow region is easier to learn,
our SADC processes the non-shadow region with a lightweight convolution module
in a computationally cheap manner and recovers the shadow region with a more
complicated convolution module to ensure the quality of image reconstruction.
Given that the non-shadow region often contains more background color
information, we further develop a novel intra-convolution distillation loss to
strengthen the information flow from the non-shadow region to the shadow
region. Extensive experiments on the ISTD and SRD datasets show our method
achieves better performance in shadow removal over many state-of-the-arts. Our
code is available at https://github.com/xuyimin0926/SADC
1xN Pattern for Pruning Convolutional Neural Networks
Though network pruning receives popularity in reducing the complexity of
convolutional neural networks (CNNs), it remains an open issue to concurrently
maintain model accuracy as well as achieve significant speedups on general
CPUs. In this paper, we propose a novel 1xN pruning pattern to break this
limitation. In particular, consecutive N output kernels with the same input
channel index are grouped into one block, which serves as a basic pruning
granularity of our pruning pattern. Our 1xN pattern prunes these blocks
considered unimportant. We also provide a workflow of filter rearrangement that
first rearranges the weight matrix in the output channel dimension to derive
more influential blocks for accuracy improvements and then applies similar
rearrangement to the next-layer weights in the input channel dimension to
ensure correct convolutional operations. Moreover, the output computation after
our 1xN pruning can be realized via a parallelized block-wise vectorized
operation, leading to significant speedups on general CPUs. The efficacy of our
pruning pattern is proved with experiments on ILSVRC-2012. For example, Given
the pruning rate of 50% and N=4, our pattern obtains about 3.0% improvements
over filter pruning in the top-1 accuracy of MobileNet-V2. Meanwhile, it
obtains 56.04ms inference savings on Cortex-A7 CPU over weight pruning. Our
project is made available at https://github.com/lmbxmu/1xN
Knowledge Condensation Distillation
Knowledge Distillation (KD) transfers the knowledge from a high-capacity
teacher network to strengthen a smaller student. Existing methods focus on
excavating the knowledge hints and transferring the whole knowledge to the
student. However, the knowledge redundancy arises since the knowledge shows
different values to the student at different learning stages. In this paper, we
propose Knowledge Condensation Distillation (KCD). Specifically, the knowledge
value on each sample is dynamically estimated, based on which an
Expectation-Maximization (EM) framework is forged to iteratively condense a
compact knowledge set from the teacher to guide the student learning. Our
approach is easy to build on top of the off-the-shelf KD methods, with no extra
training parameters and negligible computation overhead. Thus, it presents one
new perspective for KD, in which the student that actively identifies teacher's
knowledge in line with its aptitude can learn to learn more effectively and
efficiently. Experiments on standard benchmarks manifest that the proposed KCD
can well boost the performance of student model with even higher distillation
efficiency. Code is available at https://github.com/dzy3/KCD.Comment: ECCV202
Fine-grained Data Distribution Alignment for Post-Training Quantization
While post-training quantization receives popularity mostly due to its
evasion in accessing the original complete training dataset, its poor
performance also stems from scarce images. To alleviate this limitation, in
this paper, we leverage the synthetic data introduced by zero-shot quantization
with calibration dataset and propose a fine-grained data distribution alignment
(FDDA) method to boost the performance of post-training quantization. The
method is based on two important properties of batch normalization statistics
(BNS) we observed in deep layers of the trained network, (i.e.), inter-class
separation and intra-class incohesion. To preserve this fine-grained
distribution information: 1) We calculate the per-class BNS of the calibration
dataset as the BNS centers of each class and propose a BNS-centralized loss to
force the synthetic data distributions of different classes to be close to
their own centers. 2) We add Gaussian noise into the centers to imitate the
incohesion and propose a BNS-distorted loss to force the synthetic data
distribution of the same class to be close to the distorted centers. By
utilizing these two fine-grained losses, our method manifests the
state-of-the-art performance on ImageNet, especially when both the first and
last layers are quantized to the low-bit. Code is at
\url{https://github.com/zysxmu/FDDA}.Comment: ECCV202
Open Vocabulary Object Detection with Proposal Mining and Prediction Equalization
Open-vocabulary object detection (OVD) aims to scale up vocabulary size to
detect objects of novel categories beyond the training vocabulary. Recent work
resorts to the rich knowledge in pre-trained vision-language models. However,
existing methods are ineffective in proposal-level vision-language alignment.
Meanwhile, the models usually suffer from confidence bias toward base
categories and perform worse on novel ones. To overcome the challenges, we
present MEDet, a novel and effective OVD framework with proposal mining and
prediction equalization. First, we design an online proposal mining to refine
the inherited vision-semantic knowledge from coarse to fine, allowing for
proposal-level detection-oriented feature alignment. Second, based on causal
inference theory, we introduce a class-wise backdoor adjustment to reinforce
the predictions on novel categories to improve the overall OVD performance.
Extensive experiments on COCO and LVIS benchmarks verify the superiority of
MEDet over the competing approaches in detecting objects of novel categories,
e.g., 32.6% AP50 on COCO and 22.4% mask mAP on LVIS
Unified and Dynamic Graph for Temporal Character Grouping in Long Videos
Video temporal character grouping locates appearing moments of major
characters within a video according to their identities. To this end, recent
works have evolved from unsupervised clustering to graph-based supervised
clustering. However, graph methods are built upon the premise of fixed affinity
graphs, bringing many inexact connections. Besides, they extract multi-modal
features with kinds of models, which are unfriendly to deployment. In this
paper, we present a unified and dynamic graph (UniDG) framework for temporal
character grouping. This is accomplished firstly by a unified representation
network that learns representations of multiple modalities within the same
space and still preserves the modality's uniqueness simultaneously. Secondly,
we present a dynamic graph clustering where the neighbors of different
quantities are dynamically constructed for each node via a cyclic matching
strategy, leading to a more reliable affinity graph. Thirdly, a progressive
association method is introduced to exploit spatial and temporal contexts among
different modalities, allowing multi-modal clustering results to be well fused.
As current datasets only provide pre-extracted features, we evaluate our UniDG
method on a collected dataset named MTCG, which contains each character's
appearing clips of face and body and speaking voice tracks. We also evaluate
our key components on existing clustering and retrieval datasets to verify the
generalization ability. Experimental results manifest that our method can
achieve promising results and outperform several state-of-the-art approaches
Recent developments of metamaterials/metasurfaces for RCS reduction
In this paper, recent developments of metamaterials and metasurfaces for RCS reduction are reviewed, including basic theory, working principle, design formula, and experimental verification. Super-thin cloaks mediated by metasurfaces can cloak objects with minor impacts on the original electromagnetic field distribution. RCS reduction can be achieved by reconfiguring scattering patterns using coding metasurfaces. Novel radar absorbing materials can be devised based on field enhancements of metamaterials. When combined with conventional radar absorbing materials, metamaterials can expand the bandwidth, enlarge the angular range, or reduce the weight. Future tendency and major challenges are also summarized
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