154 research outputs found
LE2Fusion: A novel local edge enhancement module for infrared and visible image fusion
Infrared and visible image fusion task aims to generate a fused image which
contains salient features and rich texture details from multi-source images.
However, under complex illumination conditions, few algorithms pay attention to
the edge information of local regions which is crucial for downstream tasks. To
this end, we propose a fusion network based on the local edge enhancement,
named LE2Fusion. Specifically, a local edge enhancement (LE2) module is
proposed to improve the edge information under complex illumination conditions
and preserve the essential features of image. For feature extraction, a
multi-scale residual attention (MRA) module is applied to extract rich
features. Then, with LE2, a set of enhancement weights are generated which are
utilized in feature fusion strategy and used to guide the image reconstruction.
To better preserve the local detail information and structure information, the
pixel intensity loss function based on the local region is also presented. The
experiments demonstrate that the proposed method exhibits better fusion
performance than the state-of-the-art fusion methods on public datasets
WavePF: A Novel Fusion Approach based on Wavelet-guided Pooling for Infrared and Visible Images
Infrared and visible image fusion aims to generate synthetic images
simultaneously containing salient features and rich texture details, which can
be used to boost downstream tasks. However, existing fusion methods are
suffering from the issues of texture loss and edge information deficiency,
which result in suboptimal fusion results. Meanwhile, the straight-forward
up-sampling operator can not well preserve the source information from
multi-scale features. To address these issues, a novel fusion network based on
the wavelet-guided pooling (wave-pooling) manner is proposed, termed as WavePF.
Specifically, a wave-pooling based encoder is designed to extract multi-scale
image and detail features of source images at the same time. In addition, the
spatial attention model is used to aggregate these salient features. After
that, the fused features will be reconstructed by the decoder, in which the
up-sampling operator is replaced by the wave-pooling reversed operation.
Different from the common max-pooling technique, image features after the
wave-pooling layer can retain abundant details information, which can benefit
the fusion process. In this case, rich texture details and multi-scale
information can be maintained during the reconstruction phase. The experimental
results demonstrate that our method exhibits superior fusion performance over
the state-of-the-arts on multiple image fusion benchmark
Towards Robust Aspect-based Sentiment Analysis through Non-counterfactual Augmentations
While state-of-the-art NLP models have demonstrated excellent performance for
aspect based sentiment analysis (ABSA), substantial evidence has been presented
on their lack of robustness. This is especially manifested as significant
degradation in performance when faced with out-of-distribution data. Recent
solutions that rely on counterfactually augmented datasets show promising
results, but they are inherently limited because of the lack of access to
explicit causal structure. In this paper, we present an alternative approach
that relies on non-counterfactual data augmentation. Our proposal instead
relies on using noisy, cost-efficient data augmentations that preserve
semantics associated with the target aspect. Our approach then relies on
modelling invariances between different versions of the data to improve
robustness. A comprehensive suite of experiments shows that our proposal
significantly improves upon strong pre-trained baselines on both standard and
robustness-specific datasets. Our approach further establishes a new
state-of-the-art on the ABSA robustness benchmark and transfers well across
domains.Comment: 10pages,1 figure,10 table
Symbolic Priors for RNN-based Semantic Parsing
International audienceSeq2seq models based on Recurrent Neural Networks (RNNs) have recently received a lot of attention in the domain of Semantic Parsing. While in principle they can be trained directly on pairs (natural language utterances, logical forms), their performance is limited by the amount of available data. To alleviate this problem, we propose to exploit various sources of prior knowledge: the well-formedness of the logical forms is modeled by a weighted context-free grammar; the likelihood that certain entities present in the input utterance are also present in the logical form is modeled by weighted finite-state automata. The grammar and automata are combined together through an efficient intersection algorithm to form a soft guide (" background ") to the RNN. We test our method on an extension of the Overnight dataset and show that it not only strongly improves over an RNN base-line, but also outperforms non-RNN models based on rich sets of hand-crafted features
Sequence-based Structured Prediction for Semantic Parsing
International audienceWe propose an approach for semantic parsing that uses a recurrent neural network to map a natural language question into a logical form representation of a KB query. Building on recent work by (Wang et al., 2015), the interpretable logical forms, which are structured objects obeying certain constraints, are enumerated by an underlying grammar and are paired with their canonical realizations. In order to use sequence prediction, we need to sequentialize these logical forms. We compare three sequentializations: a direct linearization of the logical form, a linearization of the associated canonical realization, and a sequence consisting of derivation steps relative to the underlying grammar. We also show how grammatical constraints on the derivation sequence can easily be integrated inside the RNN-based sequential predictor. Our experiments show important improvements over previous results for the same dataset, and also demonstrate the advantage of incorporating the grammatical constraints
Safety benefit of cooperative control for heterogeneous traffic on-ramp merging
The safety of heterogeneous traffic is a vital topic in the oncoming era of autonomous vehicles (AVs). The cooperative vehicle infrastructure system (CVIS) is considered to improve heterogeneous traffic safety by connecting and controlling AVs cooperatively, and the connected AVs are so-called connected and automated vehicles (CAVs). However, the safety impact of cooperative control strategy on the heterogeneous traffic with CAVs and human-driving vehicles (HVs) has not been well investigated. In this paper, based on the traffic simulator SUMO, we designed a typical highway scenario of on-ramp merging and adopted a cooperative control method for CAVs. We then compared the safety performance for two different heterogeneous traffic systems, i.e. AV and HV, CAV and HV, respectively, to illustrate the safety benefits of the cooperative control strategy. We found that the safety performance of the CAV and HV traffic system does not always outperform that of AV and HV. With random departSpeed and higher arrival rate, the proposed cooperative control method would decrease the conflicts significantly whereas the penetration rate is over 80%. We further investigated the conflicts in terms of the leading and following vehicle types, and found that the risk of a AV/CAV followed by a HV is twice that of a HV followed by another HV. We also considered the safety effect of communication failure, and found that there is no significant impact until the packet loss probability is greater than 30%, while communication delay\u27s impact on safety can be ignored according to our experiments
ModelObfuscator: Obfuscating Model Information to Protect Deployed ML-based Systems
More and more edge devices and mobile apps are leveraging deep learning (DL)
capabilities. Deploying such models on devices -- referred to as on-device
models -- rather than as remote cloud-hosted services, has gained popularity
because it avoids transmitting user data off of the device and achieves high
response time. However, on-device models can be easily attacked, as they can be
accessed by unpacking corresponding apps and the model is fully exposed to
attackers. Recent studies show that attackers can easily generate
white-box-like attacks for an on-device model or even inverse its training
data. To protect on-device models from white-box attacks, we propose a novel
technique called model obfuscation. Specifically, model obfuscation hides and
obfuscates the key information -- structure, parameters and attributes -- of
models by renaming, parameter encapsulation, neural structure obfuscation
obfuscation, shortcut injection, and extra layer injection. We have developed a
prototype tool ModelObfuscator to automatically obfuscate on-device TFLite
models. Our experiments show that this proposed approach can dramatically
improve model security by significantly increasing the difficulty of parsing
models inner information, without increasing the latency of DL models. Our
proposed on-device model obfuscation has the potential to be a fundamental
technique for on-device model deployment. Our prototype tool is publicly
available at: https://github.com/zhoumingyi/ModelObfuscator.Comment: Published In Proceedings of the 32nd ACM SIGSOFT International
Symposium on Software Testing and Analysis (ISSTA23
- …