497 research outputs found
Data Poisoning Attacks in Contextual Bandits
We study offline data poisoning attacks in contextual bandits, a class of
reinforcement learning problems with important applications in online
recommendation and adaptive medical treatment, among others. We provide a
general attack framework based on convex optimization and show that by slightly
manipulating rewards in the data, an attacker can force the bandit algorithm to
pull a target arm for a target contextual vector. The target arm and target
contextual vector are both chosen by the attacker. That is, the attacker can
hijack the behavior of a contextual bandit. We also investigate the feasibility
and the side effects of such attacks, and identify future directions for
defense. Experiments on both synthetic and real-world data demonstrate the
efficiency of the attack algorithm.Comment: GameSec 201
Prototypical Contrastive Learning-based CLIP Fine-tuning for Object Re-identification
This work aims to adapt large-scale pre-trained vision-language models, such
as contrastive language-image pretraining (CLIP), to enhance the performance of
object reidentification (Re-ID) across various supervision settings. Although
prompt learning has enabled a recent work named CLIP-ReID to achieve promising
performance, the underlying mechanisms and the necessity of prompt learning
remain unclear due to the absence of semantic labels in ReID tasks. In this
work, we first analyze the role prompt learning in CLIP-ReID and identify its
limitations. Based on our investigations, we propose a simple yet effective
approach to adapt CLIP for supervised object Re-ID. Our approach directly
fine-tunes the image encoder of CLIP using a prototypical contrastive learning
(PCL) loss, eliminating the need for prompt learning. Experimental results on
both person and vehicle Re-ID datasets demonstrate the competitiveness of our
method compared to CLIP-ReID. Furthermore, we extend our PCL-based CLIP
fine-tuning approach to unsupervised scenarios, where we achieve state-of-the
art performance
Transformer Based Multi-Grained Features for Unsupervised Person Re-Identification
Multi-grained features extracted from convolutional neural networks (CNNs)
have demonstrated their strong discrimination ability in supervised person
re-identification (Re-ID) tasks. Inspired by them, this work investigates the
way of extracting multi-grained features from a pure transformer network to
address the unsupervised Re-ID problem that is label-free but much more
challenging. To this end, we build a dual-branch network architecture based
upon a modified Vision Transformer (ViT). The local tokens output in each
branch are reshaped and then uniformly partitioned into multiple stripes to
generate part-level features, while the global tokens of two branches are
averaged to produce a global feature. Further, based upon offline-online
associated camera-aware proxies (O2CAP) that is a top-performing unsupervised
Re-ID method, we define offline and online contrastive learning losses with
respect to both global and part-level features to conduct unsupervised
learning. Extensive experiments on three person Re-ID datasets show that the
proposed method outperforms state-of-the-art unsupervised methods by a
considerable margin, greatly mitigating the gap to supervised counterparts.
Code will be available soon at https://github.com/RikoLi/WACV23-workshop-TMGF.Comment: Accepted by WACVW 2023, 3rd Workshop on Real-World Surveillance:
Applications and Challenge
On Feature-Based SAR Image Registration: Appropriate Feature and Retrieval Algorithm
An investigation on the appropriate feature and parameter retrieval algorithm is conducted for feature-based registration of synthetic aperture radar (SAR) images. The commonly used features such as tie points, Harris corner, SIFT, and SURF are comprehensively evaluated. SURF is shown to outperform others on criteria such as the geometrical invariance of feature and descriptor, the extraction and matching speed, the localization accuracy, as well as the robustness to decorrelation and speckling. The processing result reveals that SURF has nice flexibility to SAR speckles for the potential relationship between Fast-Hessian detector and refined Lee filter. Moreover, the use of Fast-Hessian to oversampled images with unaltered sampling step helps to improve the registration accuracy to subpixel (i.e., <1 pixel). As for parameter retrieval, the widely used random sample consensus (RANSAC) is inappropriate because it may trap into local occlusion and result in uncertain estimation. An extended fast least trimmed squares (EF-LTS) is proposed, which behaves stable and averagely better than RANSAC. Fitting SURF features with EF-LTS is hence suggested for SAR image registration. The nice performance of this scheme is validated on both InSAR and MiniSAR image pairs
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Thank God That Regressing Y on X is Not the Same as Regressing X on Y: Direct and Indirect Residual Augmentations
What does regressing Y on X versus regressing X on Y have to do with MCMC? It turns out that many strategies for speeding up data-augmentation type algorithms can be understood as fostering independence or “de-correlation” between a regression function and the corresponding residual, thereby reducing or even eliminating dependence among MCMC iterates. There are two general classes of algorithms, those corresponding to regressing parameters on augmented data/auxiliary variables and those that operate the other way around. The interweaving strategy (Yu and Meng, 2011, JCGS) provides a general recipe to automatically take advantage of both, and it is the existence of two different types of residuals that makes the interweaving strategy seemingly magical in some cases and promising in general. The concept of residuals—which depends on actual data—also highlights the potential for substantial improvements when data augmentation schemes are allowed to depend on the observed data. At the same time, there is an intriguing phase transition type of phenomenon regarding choosing (partially) residual augmentation schemes, reminding us once more of the prevailing issue of trade-off between robustness and efficiency. This article reports on these latest theoretical investigations (using a class of normal/independence models) and empirical findings (using a posterior sampling for a Probit regression) in the search for effective residual augmentations—and ultimately more MCMC algorithms—that meet the 3-S criterion: simple, stable, and speedy.Statistic
A Semantic Graph-Based Approach for Mining Common Topics From Multiple Asynchronous Text Streams
In the age of Web 2.0, a substantial amount of unstructured
content are distributed through multiple text streams in an
asynchronous fashion, which makes it increasingly difficult
to glean and distill useful information. An effective way to
explore the information in text streams is topic modelling,
which can further facilitate other applications such as search,
information browsing, and pattern mining. In this paper, we
propose a semantic graph based topic modelling approach
for structuring asynchronous text streams. Our model in-
tegrates topic mining and time synchronization, two core
modules for addressing the problem, into a unified model.
Specifically, for handling the lexical gap issues, we use global
semantic graphs of each timestamp for capturing the hid-
den interaction among entities from all the text streams.
For dealing with the sources asynchronism problem, local
semantic graphs are employed to discover similar topics of
different entities that can be potentially separated by time
gaps. Our experiment on two real-world datasets shows that
the proposed model significantly outperforms the existing
ones
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