86 research outputs found
MIANet: Aggregating Unbiased Instance and General Information for Few-Shot Semantic Segmentation
Existing few-shot segmentation methods are based on the meta-learning
strategy and extract instance knowledge from a support set and then apply the
knowledge to segment target objects in a query set. However, the extracted
knowledge is insufficient to cope with the variable intra-class differences
since the knowledge is obtained from a few samples in the support set. To
address the problem, we propose a multi-information aggregation network
(MIANet) that effectively leverages the general knowledge, i.e., semantic word
embeddings, and instance information for accurate segmentation. Specifically,
in MIANet, a general information module (GIM) is proposed to extract a general
class prototype from word embeddings as a supplement to instance information.
To this end, we design a triplet loss that treats the general class prototype
as an anchor and samples positive-negative pairs from local features in the
support set. The calculated triplet loss can transfer semantic similarities
among language identities from a word embedding space to a visual
representation space. To alleviate the model biasing towards the seen training
classes and to obtain multi-scale information, we then introduce a
non-parametric hierarchical prior module (HPM) to generate unbiased
instance-level information via calculating the pixel-level similarity between
the support and query image features. Finally, an information fusion module
(IFM) combines the general and instance information to make predictions for the
query image. Extensive experiments on PASCAL-5i and COCO-20i show that MIANet
yields superior performance and set a new state-of-the-art. Code is available
at https://github.com/Aldrich2y/MIANet.Comment: Accepted to CVPR 202
A dual-branch model with inter- and intra-branch contrastive loss for long-tailed recognition
Real-world data often exhibits a long-tailed distribution, in which head
classes occupy most of the data, while tail classes only have very few samples.
Models trained on long-tailed datasets have poor adaptability to tail classes
and the decision boundaries are ambiguous. Therefore, in this paper, we propose
a simple yet effective model, named Dual-Branch Long-Tailed Recognition
(DB-LTR), which includes an imbalanced learning branch and a Contrastive
Learning Branch (CoLB). The imbalanced learning branch, which consists of a
shared backbone and a linear classifier, leverages common imbalanced learning
approaches to tackle the data imbalance issue. In CoLB, we learn a prototype
for each tail class, and calculate an inter-branch contrastive loss, an
intra-branch contrastive loss and a metric loss. CoLB can improve the
capability of the model in adapting to tail classes and assist the imbalanced
learning branch to learn a well-represented feature space and discriminative
decision boundary. Extensive experiments on three long-tailed benchmark
datasets, i.e., CIFAR100-LT, ImageNet-LT and Places-LT, show that our DB-LTR is
competitive and superior to the comparative methods.Comment: Published at Neural Network
Improving Robustness of LiDAR-Camera Fusion Model against Weather Corruption from Fusion Strategy Perspective
In recent years, LiDAR-camera fusion models have markedly advanced 3D object
detection tasks in autonomous driving. However, their robustness against common
weather corruption such as fog, rain, snow, and sunlight in the intricate
physical world remains underexplored. In this paper, we evaluate the robustness
of fusion models from the perspective of fusion strategies on the corrupted
dataset. Based on the evaluation, we further propose a concise yet practical
fusion strategy to enhance the robustness of the fusion models, namely flexibly
weighted fusing features from LiDAR and camera sources to adapt to varying
weather scenarios. Experiments conducted on four types of fusion models, each
with two distinct lightweight implementations, confirm the broad applicability
and effectiveness of the approach.Comment: 17 page
Personalization as a Shortcut for Few-Shot Backdoor Attack against Text-to-Image Diffusion Models
Although recent personalization methods have democratized high-resolution
image synthesis by enabling swift concept acquisition with minimal examples and
lightweight computation, they also present an exploitable avenue for high
accessible backdoor attacks. This paper investigates a critical and unexplored
aspect of text-to-image (T2I) diffusion models - their potential vulnerability
to backdoor attacks via personalization. Our study focuses on a zero-day
backdoor vulnerability prevalent in two families of personalization methods,
epitomized by Textual Inversion and DreamBooth.Compared to traditional backdoor
attacks, our proposed method can facilitate more precise, efficient, and easily
accessible attacks with a lower barrier to entry. We provide a comprehensive
review of personalization in T2I diffusion models, highlighting the operation
and exploitation potential of this backdoor vulnerability. To be specific, by
studying the prompt processing of Textual Inversion and DreamBooth, we have
devised dedicated backdoor attacks according to the different ways of dealing
with unseen tokens and analyzed the influence of triggers and concept images on
the attack effect. Through comprehensive empirical study, we endorse the
utilization of the nouveau-token backdoor attack due to its impressive
effectiveness, stealthiness, and integrity, markedly outperforming the
legacy-token backdoor attack.Comment: 16 pages, accepted by AAAI 202
Safety risk assessment of subway shield construction under-crossing a river using CFA and FER
Numerous subway projects are planned by China's city governments, and more subways can hardly avoid under-crossing rivers. While often being located in complex natural and social environments, subway shield construction under-crossing a river (SSCUR) is more susceptible to safety accidents, causing substantial casualties, and monetary losses. Therefore, there is an urgent need to investigate safety risks during SSCUR. The paper identified the safety risks during SSCUR by using a literature review and experts' evaluation, proposed a new safety risk assessment model by integrating confirmatory factor analysis (CFA) and fuzzy evidence reasoning (FER), and then selected a project to validate the feasibility of the proposed model. Research results show that (a) a safety risk list of SSCUR was identified, including 5 first-level safety risks and 38 second-level safety risks; (b) the proposed safety risk assessment model can be used to assess the safety risk of SSCUR; (c) safety inspection, safety organization and duty, quicksand layer, and high-pressure phreatic water were the high-level risks, and the onsite total safety risk was at the medium level; (d) management-type safety risks, environment-type safety risks, and personnel-type safety risks have higher expected utility values, and manager-type safety risks were expected have higher risk-utility values when compared to worker-type safety risks. The research can enrich the theoretical knowledge of SSCUR safety risk assessment and provide references to safety managers for conducting scientific and effective safety management on the construction site when a subway crosses under a river
SPH-FEM Design of Laminated Plies under Bird-Strike Impact
Composite laminates can potentially reduce the weight of aircrafts; however, they are subjected to bird strike hazards in civil aviation. To handle their nonlinear dynamic behaviour, in this study, the impact damage of composite laminates were numerically evaluated and designed by means of smoothed particle hydrodynamics (SPH) and the finite element method (FEM) to simulate the interaction between bird projectiles and the laminates. Attention was mainly focused on the different damage modes in various laminates’ plies induced by bird impact on a square laminated plate. A continuum damage mechanics approach was exploited to simulate damage initiation and evolution in composite laminates. Damage maps were computed with respect to different ply angles, i.e., 0°, 45° and −45°. The damage distributions were comparatively investigated, and then the ply design was considered for crashworthiness improvement. The results aim to serve as a design guideline for future prototype-scale bird strike studies of complex laminated structures
Impact-Damage Equivalency for Twisted Composite Blades with Symmetrical Configurations
In spite of potential advantages for aircraft structures, composite laminates can be subjected to bird-strike hazard in civil aviation. For purpose of future surrogate experiments, in this study, impact-damage equivalency for twisted composite blades is numerically investigated by Smoothed Particle Hydrodynamics (SPH) and finite element method (FEM). Cantilever slender flat plates are usually used for basic impact tests, the impact-damage equivalency is being considered by comparing damage modes and energies of three impact configurations: (1) twisted blade; (2) flat blade (axisymmetric); and (3) inclined flat blade (centrosymmetric). The damage maps and energy variations were comparatively investigated. Results indicate that both symmetrical flat and inclined flat blades can be, to a certain extent, regarded as alternatives for real twisted blades under bird impact; however, both types of blade have their own merits and drawbacks, and hence should be used carefully. These results aim to serve as tentative design guideline for future prototype or model experimental study of laminated blades in real aeronautical structures
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