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Found 42 papers, 13 papers with code
IDET: Iterative Difference-Enhanced Transformers for High-Quality Change Detection
Change detection (CD) aims to detect change regions within an image pair captured at different times, playing a significant role for diverse real-world applications.
Single Object Tracking Research: A Survey
Visual object tracking is an important task in computer vision, which has many real-world applications, e. g., video surveillance, visual navigation.
NPC: Neuron Path Coverage via Characterizing Decision Logic of Deep Neural Networks
Inspired by software testing, a number of structural coverage criteria are designed and proposed to measure the test adequacy of DNNs.
MISF: Multi-level Interactive Siamese Filtering for High-Fidelity Image Inpainting
As a result, the final method takes the advantage of effective semantic & image-level filling for high-fidelity inpainting.
Masked Faces with Faced Masks
Modern face recognition systems (FRS) still fall short when the subjects are wearing facial masks, a common theme in the age of respiratory pandemics.
ALA: Adversarial Lightness Attack via Naturalness-aware Regularizations
To generate unrestricted adversarial examples with high image quality and good transferability, in this paper, we propose Adversarial Lightness Attack (ALA), a white-box unrestricted adversarial attack that focuses on modifying the lightness of the images.
Uncertainty-Aware Cascaded Dilation Filtering for High-Efficiency Deraining
First, we propose the uncertainty-aware cascaded predictive filtering (UC-PFilt) that can identify the difficulties of reconstructing clean pixels via predicted kernels and remove the residual rain traces effectively.
Benchmarking Shadow Removal for Facial Landmark Detection and Beyond
The observation of this work motivates us to design a novel detection-aware shadow removal framework, which empowers shadow removal to achieve higher restoration quality and enhance the shadow robustness of deployed facial landmark detectors.
ArchRepair: Block-Level Architecture-Oriented Repairing for Deep Neural Networks
In this work, as the first attempt, we initiate to repair DNNs by jointly optimizing the architecture and weights at a higher (i. e., block) level.
AdvBokeh: Learning to Adversarially Defocus Blur
Bokeh effect is a natural shallow depth-of-field phenomenon that blurs the out-of-focus part in photography.
Adversarial Relighting Against Face Recognition
To this end, we first propose the physical model-based adversarial relighting attack (ARA) denoted as albedo-quotient-based adversarial relighting attack (AQ-ARA).
CarveNet: Carving Point-Block for Complex 3D Shape Completion
3D point cloud completion is very challenging because it heavily relies on the accurate understanding of the complex 3D shapes (e. g., high-curvature, concave/convex, and hollowed-out 3D shapes) and the unknown & diverse patterns of the partially available point clouds.
Learning to Adversarially Blur Visual Object Tracking
In this work, we explore the robustness of visual object trackers against motion blur from a new angle, i. e., adversarial blur attack (ABA).
AdvFilter: Predictive Perturbation-aware Filtering against Adversarial Attack via Multi-domain Learning
To this end, we first comprehensively investigate two kinds of pixel denoising methods for adversarial robustness enhancement (i. e., existing additive-based and unexplored filtering-based methods) under the loss functions of image-level and semantic-level, respectively, showing that pixel-wise filtering can obtain much higher image quality (e. g., higher PSNR) as well as higher robustness (e. g., higher accuracy on adversarial examples) than existing pixel-wise additive-based method.
JPGNet: Joint Predictive Filtering and Generative Network for Image Inpainting
In this paper, for the first time, we formulate image inpainting as a mix of two problems, predictive filtering and deep generation.
Tight Mutual Information Estimation With Contrastive Fenchel-Legendre Optimization
Successful applications of InfoNCE and its variants have popularized the use of contrastive variational mutual information (MI) estimators in machine learning.
Simpler, Faster, Stronger: Breaking The log-K Curse On Contrastive Learners With FlatNCE
InfoNCE-based contrastive representation learners, such as SimCLR, have been tremendously successful in recent years.
Sparta: Spatially Attentive and Adversarially Robust Activation
In both cases, Sparta leads to CNNs with higher robustness than the vanilla ReLU, verifying the flexibility and versatility of the proposed method.
AVA: Adversarial Vignetting Attack against Visual Recognition
Moreover, we propose the geometry-aware level-set optimization method to solve the adversarial vignetting regions and physical parameters jointly.
Let There be Light: Improved Traffic Surveillance via Detail Preserving Night-to-Day Transfer
As one of the state-of-the-art perception approaches, detecting the interested objects in each frame of video surveillance is widely desired by ITS.
AdvHaze: Adversarial Haze Attack
We also visualize the correlation matrices, which inspire us to jointly apply different perturbations to improve the success rate of the attack.
DeepMix: Online Auto Data Augmentation for Robust Visual Object Tracking
Online updating of the object model via samples from historical frames is of great importance for accurate visual object tracking.
Auto-Exposure Fusion for Single-Image Shadow Removal
We conduct extensive experiments on the ISTD, ISTD+, and SRD datasets to validate our method's effectiveness and show better performance in shadow regions and comparable performance in non-shadow regions over the state-of-the-art methods.
Countering Malicious DeepFakes: Survey, Battleground, and Horizon
To fill this gap, in this paper, we provide a comprehensive overview and detailed analysis of the research work on the topic of DeepFake generation, DeepFake detection as well as evasion of DeepFake detection, with more than 318 research papers carefully surveyed.
Sparta: Spatially Attentive and Adversarially Robust Activations
Moreover, comprehensive evaluations have demonstrated two important properties of our method: First, superior transferability across DNNs.
DeepRepair: Style-Guided Repairing for DNNs in the Real-world Operational Environment
In this paper, we propose a style-guided data augmentation for repairing DNN in the operational environment.
Bias Field Poses a Threat to DNN-based X-Ray Recognition
Our method reveals the potential threat to the DNN-based X-ray automated diagnosis and can definitely benefit the development of bias-field-robust automated diagnosis system.
Adversarial Rain Attack and Defensive Deraining for DNN Perception
To defend the DNNs from the negative rain effect, we also present a defensive deraining strategy, for which we design an adversarial rain augmentation that uses mixed adversarial rain layers to enhance deraining models for downstream DNN perception.
Dodging DeepFake Detection via Implicit Spatial-Domain Notch Filtering
We believe that producing DeepFakes that are highly realistic and ``detection evasive'' can serve the ultimate goal of improving future generation DeepFake detection capabilities.
Adversarial Exposure Attack on Diabetic Retinopathy Imagery
In this paper, we study this problem from the viewpoint of adversarial attack and identify a totally new task, i. e., adversarial exposure attack generating adversarial images by tuning image exposure to mislead the DNNs with significantly high transferability.
EfficientDeRain: Learning Pixel-wise Dilation Filtering for High-Efficiency Single-Image Deraining
To fill this gap, in this paper, we regard the single-image deraining as a general image-enhancing problem and originally propose a model-free deraining method, i. e., EfficientDeRain, which is able to process a rainy image within 10~ms (i. e., around 6~ms on average), over 80 times faster than the state-of-the-art method (i. e., RCDNet), while achieving similar de-rain effects.
Can You Spot the Chameleon? Adversarially Camouflaging Images from Co-Salient Object Detection
In this paper, we address this problem from the perspective of adversarial attacks and identify a novel task: adversarial co-saliency attack.
Pasadena: Perceptually Aware and Stealthy Adversarial Denoise Attack
To this end, we initiate the very first attempt to study this problem from the perspective of adversarial attack and propose the adversarial denoise attack.
FakePolisher: Making DeepFakes More Detection-Evasive by Shallow Reconstruction
At this moment, GAN-based image generation methods are still imperfect, whose upsampling design has limitations in leaving some certain artifact patterns in the synthesized image.
DeepRhythm: Exposing DeepFakes with Attentional Visual Heartbeat Rhythms
As the GAN-based face image and video generation techniques, widely known as DeepFakes, have become more and more matured and realistic, there comes a pressing and urgent demand for effective DeepFakes detectors.
Watch out! Motion is Blurring the Vision of Your Deep Neural Networks
Besides, the attack is further enhanced by adaptively tuning the translations of object and background.
FakeLocator: Robust Localization of GAN-Based Face Manipulations
In this work, we investigate the architecture of existing GAN-based face manipulation methods and observe that the imperfection of upsampling methods therewithin could be served as an important asset for GAN-synthesized fake image detection and forgery localization.
Amora: Black-box Adversarial Morphing Attack
In this paper, we investigate and introduce a new type of adversarial attack to evade FR systems by manipulating facial content, called \textbf{\underline{a}dversarial \underline{mor}phing \underline{a}ttack} (a. k. a.
SPARK: Spatial-aware Online Incremental Attack Against Visual Tracking
We identify that online object tracking poses two new challenges: 1) it is difficult to generate imperceptible perturbations that can transfer across frames, and 2) real-time trackers require the attack to satisfy a certain level of efficiency.
Research Commentary on Recommendations with Side Information: A Survey and Research Directions
This Research Commentary aims to provide a comprehensive and systematic survey of the recent research on recommender systems with side information.
Effects of Blur and Deblurring to Visual Object Tracking
In this paper, we address these two problems by constructing a Blurred Video Tracking benchmark, which contains a variety of videos with different levels of motion blurs, as well as ground truth tracking results for evaluating trackers.
Learning Dynamic Siamese Network for Visual Object Tracking
How to effectively learn temporal variation of target appearance, to exclude the interference of cluttered background, while maintaining real-time response, is an essential problem of visual object tracking.
Ranked #5 on
Visual Object Tracking
on OTB-2013
