172 research outputs found
Multi-Attribute Decision Making Method Based on Aggregated Neutrosophic Set
Multi-attribute decision-making refers to the decision-making problem of selecting the optimal alternative or sorting the scheme when considering multiple attributes, which is widely used in engineering design, economy, management and military, etc. But in real application, the attribute information of many objects is often inaccurate or uncertain, so it is very important for us to find a useful and efficient method to solve the problem
Towards Privacy-Preserving Person Re-identification via Person Identify Shift
Recently privacy concerns of person re-identification (ReID) raise more and
more attention and preserving the privacy of the pedestrian images used by ReID
methods become essential. De-identification (DeID) methods alleviate privacy
issues by removing the identity-related of the ReID data. However, most of the
existing DeID methods tend to remove all personal identity-related information
and compromise the usability of de-identified data on the ReID task. In this
paper, we aim to develop a technique that can achieve a good trade-off between
privacy protection and data usability for person ReID. To achieve this, we
propose a novel de-identification method designed explicitly for person ReID,
named Person Identify Shift (PIS). PIS removes the absolute identity in a
pedestrian image while preserving the identity relationship between image
pairs. By exploiting the interpolation property of variational auto-encoder,
PIS shifts each pedestrian image from the current identity to another with a
new identity, resulting in images still preserving the relative identities.
Experimental results show that our method has a better trade-off between
privacy-preserving and model performance than existing de-identification
methods and can defend against human and model attacks for data privacy
Invisible Backdoor Attack with Dynamic Triggers against Person Re-identification
In recent years, person Re-identification (ReID) has rapidly progressed with
wide real-world applications, but also poses significant risks of adversarial
attacks. In this paper, we focus on the backdoor attack on deep ReID models.
Existing backdoor attack methods follow an all-to-one/all attack scenario,
where all the target classes in the test set have already been seen in the
training set. However, ReID is a much more complex fine-grained open-set
recognition problem, where the identities in the test set are not contained in
the training set. Thus, previous backdoor attack methods for classification are
not applicable for ReID. To ameliorate this issue, we propose a novel backdoor
attack on deep ReID under a new all-to-unknown scenario, called Dynamic
Triggers Invisible Backdoor Attack (DT-IBA). Instead of learning fixed triggers
for the target classes from the training set, DT-IBA can dynamically generate
new triggers for any unknown identities. Specifically, an identity hashing
network is proposed to first extract target identity information from a
reference image, which is then injected into the benign images by image
steganography. We extensively validate the effectiveness and stealthiness of
the proposed attack on benchmark datasets, and evaluate the effectiveness of
several defense methods against our attack
Viewpoint-Aware Loss with Angular Regularization for Person Re-Identification
Although great progress in supervised person re-identification (Re-ID) has
been made recently, due to the viewpoint variation of a person, Re-ID remains a
massive visual challenge. Most existing viewpoint-based person Re-ID methods
project images from each viewpoint into separated and unrelated sub-feature
spaces. They only model the identity-level distribution inside an individual
viewpoint but ignore the underlying relationship between different viewpoints.
To address this problem, we propose a novel approach, called
\textit{Viewpoint-Aware Loss with Angular Regularization }(\textbf{VA-reID}).
Instead of one subspace for each viewpoint, our method projects the feature
from different viewpoints into a unified hypersphere and effectively models the
feature distribution on both the identity-level and the viewpoint-level. In
addition, rather than modeling different viewpoints as hard labels used for
conventional viewpoint classification, we introduce viewpoint-aware adaptive
label smoothing regularization (VALSR) that assigns the adaptive soft label to
feature representation. VALSR can effectively solve the ambiguity of the
viewpoint cluster label assignment. Extensive experiments on the Market1501 and
DukeMTMC-reID datasets demonstrated that our method outperforms the
state-of-the-art supervised Re-ID methods
Learning Domain Invariant Prompt for Vision-Language Models
Prompt learning is one of the most effective and trending ways to adapt
powerful vision-language foundation models like CLIP to downstream datasets by
tuning learnable prompt vectors with very few samples. However, although prompt
learning achieves excellent performance over in-domain data, it still faces the
major challenge of generalizing to unseen classes and domains. Some existing
prompt learning methods tackle this issue by adaptively generating different
prompts for different tokens or domains but neglecting the ability of learned
prompts to generalize to unseen domains. In this paper, we propose a novel
prompt learning paradigm that directly generates \emph{domain invariant} prompt
that can be generalized to unseen domains, called MetaPrompt. Specifically, a
dual-modality prompt tuning network is proposed to generate prompts for input
from both image and text modalities. With a novel asymmetric contrastive loss,
the representation from the original pre-trained vision-language model acts as
supervision to enhance the generalization ability of the learned prompt. More
importantly, we propose a meta-learning-based prompt tuning algorithm that
explicitly constrains the task-specific prompt tuned for one domain or class to
also achieve good performance in another domain or class. Extensive experiments
on 11 datasets for base-to-new generalization and 4 datasets for domain
generalization demonstrate that our method consistently and significantly
outperforms existing methods.Comment: 12 pages, 6 figures, 5 table
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