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Found 64 papers, 26 papers with code
Generalizing to Evolving Domains with Latent Structure-Aware Sequential Autoencoder
Domain generalization aims to improve the generalization capability of machine learning systems to out-of-distribution (OOD) data.
CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender System
The basic idea is to first learn a causal user model on historical data to capture the overexposure effect of items on user satisfaction.
The Loop Game: Quality Assessment and Optimization for Low-Light Image Enhancement
In particular, we create a large-scale database for QUality assessment Of The Enhanced LOw-Light Image (QUOTE-LOL), which serves as the foundation in studying and developing objective quality assessment measures.
Distortion-Aware Loop Filtering of Intra 360^o Video Coding with Equirectangular Projection
In this paper, we propose a distortion-aware loop filtering model to improve the performance of intra coding for 360$^o$ videos projected via equirectangular projection (ERP) format.
Who Are the Best Adopters? User Selection Model for Free Trial Item Promotion
By providing users with opportunities to experience goods without charge, a free trial makes adopters know more about products and thus encourages their willingness to buy.
Rethinking Attention-Model Explainability through Faithfulness Violation Test
Attention mechanisms are dominating the explainability of deep models.
Generalized Visual Quality Assessment of GAN-Generated Face Images
However, little work has been dedicated to automatic quality assessment of such GAN-generated face images (GFIs), even less have been devoted to generalized and robust quality assessment of GFIs generated with unseen GAN model.
CSformer: Bridging Convolution and Transformer for Compressive Sensing
The proposed approach is an end-to-end compressive image sensing method, composed of adaptive sampling and recovery.
Locally Adaptive Structure and Texture Similarity for Image Quality Assessment
The latest advances in full-reference image quality assessment (IQA) involve unifying structure and texture similarity based on deep representations.
A Branch and Bound Framework for Stronger Adversarial Attacks of ReLU Networks
In this work, we formulate an adversarial attack using a branch-and-bound (BaB) procedure on ReLU neural networks and search adversarial examples in the activation space corresponding to binary variables in a mixed integer programming (MIP) formulation.
Towards Top-Down Just Noticeable Difference Estimation of Natural Images
Instead of explicitly formulating and fusing different masking effects in a bottom-up way, the proposed JND estimation model dedicates to first predicting a critical perceptual lossless (CPL) counterpart of the original image and then calculating the difference map between the original image and the predicted CPL image as the JND map.
No-Reference Image Quality Assessment by Hallucinating Pristine Features
In this paper, we propose a no-reference (NR) image quality assessment (IQA) method via feature level pseudo-reference (PR) hallucination.
Detect and Locate: Exposing Face Manipulation by Semantic- and Noise-level Telltales
The technological advancements of deep learning have enabled sophisticated face manipulation schemes, raising severe trust issues and security concerns in modern society.
End-to-end Compression Towards Machine Vision: Network Architecture Design and Optimization
In this paper, we show that the design and optimization of network architecture could be further improved for compression towards machine vision.
Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Neural Network Robustness Verification
We develop $\beta$-CROWN, a new bound propagation based method that can fully encode neuron split constraints in branch-and-bound (BaB) based complete verification via optimizable parameters $\beta$.
Learning Security Classifiers with Verified Global Robustness Properties
Since data distribution shift is very common in security applications, e. g., often observed for malware detection, local robustness cannot guarantee that the property holds for unseen inputs at the time of deploying the classifier.
Privacy-Preserving Constrained Domain Generalization for Medical Image Classification
However, due to the issue of limited dataset availability and the strict legal and ethical requirements for patient privacy protection, the broad applications of medical imaging classification driven by DNN with large-scale training data have been largely hindered.
Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Robustness Verification
Compared to the typically tightest but very costly semidefinite programming (SDP) based incomplete verifiers, we obtain higher verified accuracy with three orders of magnitudes less verification time.
Neuron Coverage-Guided Domain Generalization
More specifically, by treating the DNN as a program and each neuron as a functional point of the code, during the network training we aim to improve the generalization capability by maximizing the neuron coverage of DNN with the gradient similarity regularization between the original and augmented samples.
Camera Invariant Feature Learning for Generalized Face Anti-spoofing
The first branch aims to learn the camera invariant spoofing features via feature level decomposition in the high frequency domain.
Towards Unsupervised Deep Image Enhancement with Generative Adversarial Network
In this paper, we present an unsupervised image enhancement generative adversarial network (UEGAN), which learns the corresponding image-to-image mapping from a set of images with desired characteristics in an unsupervised manner, rather than learning on a large number of paired images.
Unpaired Image Enhancement with Quality-Attention Generative Adversarial Network
The key novelty of the proposed QAGAN lies in the injected QAM for the generator such that it learns domain-relevant quality attention directly from the two domains.
Learning Generalized Spatial-Temporal Deep Feature Representation for No-Reference Video Quality Assessment
In this work, we propose a no-reference video quality assessment method, aiming to achieve high-generalization capability in cross-content, -resolution and -frame rate quality prediction.
Adaptive Verifiable Training Using Pairwise Class Similarity
On CIFAR10, a non-robust LeNet model has a 21. 63% error rate, while a model created using verifiable training and a L-infinity robustness criterion of 8/255, has an error rate of 57. 10%.
Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers
Formal verification of neural networks (NNs) is a challenging and important problem.
Conceptual Compression via Deep Structure and Texture Synthesis
To this end, we propose a novel conceptual compression framework that encodes visual data into compact structure and texture representations, then decodes in a deep synthesis fashion, aiming to achieve better visual reconstruction quality, flexible content manipulation, and potential support for various vision tasks.
Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization
Recently, we have witnessed great progress in the field of medical imaging classification by adopting deep neural networks.
DRL-FAS: A Novel Framework Based on Deep Reinforcement Learning for Face Anti-Spoofing
Inspired by the philosophy employed by human beings to determine whether a presented face example is genuine or not, i. e., to glance at the example globally first and then carefully observe the local regions to gain more discriminative information, for the face anti-spoofing problem, we propose a novel framework based on the Convolutional Neural Network (CNN) and the Recurrent Neural Network (RNN).
Light Can Hack Your Face! Black-box Backdoor Attack on Face Recognition Systems
In this paper, we propose a novel black-box backdoor attack technique on face recognition systems, which can be conducted without the knowledge of the targeted DNN model.
No-reference Screen Content Image Quality Assessment with Unsupervised Domain Adaptation
Here, we develop the first unsupervised domain adaptation based no reference quality assessment method for SCIs, leveraging rich subjective ratings of the natural images (NIs).
Towards Modality Transferable Visual Information Representation with Optimal Model Compression
Compactly representing the visual signals is of fundamental importance in various image/video-centered applications.
Towards Understanding Fast Adversarial Training
Current neural-network-based classifiers are susceptible to adversarial examples.
Iterative Network for Image Super-Resolution
A novel iterative super-resolution network (ISRN) is proposed on top of the iterative optimization.
Comparison of Image Quality Models for Optimization of Image Processing Systems
The performance of objective image quality assessment (IQA) models has been evaluated primarily by comparing model predictions to human quality judgments.
Towards Analysis-friendly Face Representation with Scalable Feature and Texture Compression
In particular, we study the feature and texture compression in a scalable coding framework, where the base layer serves as the deep learning feature and enhancement layer targets to perfectly reconstruct the texture.
Image Quality Assessment: Unifying Structure and Texture Similarity
Objective measures of image quality generally operate by comparing pixels of a "degraded" image to those of the original.
Towards Practical Lottery Ticket Hypothesis for Adversarial Training
Recent research has proposed the lottery ticket hypothesis, suggesting that for a deep neural network, there exist trainable sub-networks performing equally or better than the original model with commensurate training steps.
HYDRA: Pruning Adversarially Robust Neural Networks
We demonstrate that our approach, titled HYDRA, achieves compressed networks with state-of-the-art benign and robust accuracy, simultaneously.
ARMA Nets: Expanding Receptive Field for Dense Prediction
In this work, we propose to replace any traditional convolutional layer with an autoregressive moving-average (ARMA) layer, a novel module with an adjustable receptive field controlled by the learnable autoregressive coefficients.
End-to-End Facial Deep Learning Feature Compression with Teacher-Student Enhancement
In this paper, we propose a novel end-to-end feature compression scheme by leveraging the representation and learning capability of deep neural networks, towards intelligent front-end equipped analysis with promising accuracy and efficiency.
Single Image Deraining: From Model-Based to Data-Driven and Beyond
The goal of single-image deraining is to restore the rain-free background scenes of an image degraded by rain streaks and rain accumulation.
Cost-Aware Robust Tree Ensembles for Security Applications
There are various costs for attackers to manipulate the features of security classifiers.
Towards Digital Retina in Smart Cities: A Model Generation, Utilization and Communication Paradigm
The digital retina in smart cities is to select what the City Eye tells the City Brain, and convert the acquired visual data from front-end visual sensors to features in an intelligent sensing manner.
Intrinsic Image Popularity Assessment
The goal of research in automatic image popularity assessment (IPA) is to develop computational models that can accurately predict the potential of a social image to go viral on the Internet.
Towards Compact and Robust Deep Neural Networks
In this work, we rigorously study the extension of network pruning strategies to preserve both benign accuracy and robustness of a network.
Enhancing Gradient-based Attacks with Symbolic Intervals
In this paper, we present interval attacks, a new technique to find adversarial examples to evaluate the robustness of neural networks.
Masked Non-Autoregressive Image Captioning
Existing captioning models often adopt the encoder-decoder architecture, where the decoder uses autoregressive decoding to generate captions, such that each token is generated sequentially given the preceding generated tokens.
On the Mathematical Understanding of ResNet with Feynman Path Integral
In this paper, we aim to understand Residual Network (ResNet) in a scientifically sound way by providing a bridge between ResNet and Feynman path integral.
Self-critical n-step Training for Image Captioning
Existing methods for image captioning are usually trained by cross entropy loss, which leads to exposure bias and the inconsistency between the optimizing function and evaluation metrics.
Image and Video Compression with Neural Networks: A Review
Deep convolution neural network (CNN) which makes the neural network resurge in recent years and has achieved great success in both artificial intelligent and signal processing fields, also provides a novel and promising solution for image and video compression.
On Training Robust PDF Malware Classifiers
A practically useful malware classifier must be robust against evasion attacks.
Scalable Facial Image Compression with Deep Feature Reconstruction
In this paper, we propose a scalable image compression scheme, including the base layer for feature representation and enhancement layer for texture representation.
MixTrain: Scalable Training of Verifiably Robust Neural Networks
Making neural networks robust against adversarial inputs has resulted in an arms race between new defenses and attacks.
Efficient Formal Safety Analysis of Neural Networks
Our approach can check different safety properties and find concrete counterexamples for networks that are 10$\times$ larger than the ones supported by existing analysis techniques.
Intermediate Deep Feature Compression: the Next Battlefield of Intelligent Sensing
The recent advances of hardware technology have made the intelligent analysis equipped at the front-end with deep learning more prevailing and practical.
Domain Generalization With Adversarial Feature Learning
In this paper, we tackle the problem of domain generalization: how to learn a generalized feature representation for an âunseenâ target domain by taking the advantage of multiple seen source-domain data.
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Domain Generalization
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Formal Security Analysis of Neural Networks using Symbolic Intervals
In this paper, we present a new direction for formally checking security properties of DNNs without using SMT solvers.
AI Oriented Large-Scale Video Management for Smart City: Technologies, Standards and Beyond
To practically facilitate deep neural network models in the large-scale video analysis, there are still unprecedented challenges for the large-scale video data management.
Spatial-Temporal Residue Network Based In-Loop Filter for Video Coding
Deep learning has demonstrated tremendous break through in the area of image/video processing.
Multimedia
Image Quality Assessment Guided Deep Neural Networks Training
For many computer vision problems, the deep neural networks are trained and validated based on the assumption that the input images are pristine (i. e., artifact-free).
Compact Descriptors for Video Analysis: the Emerging MPEG Standard
This paper provides an overview of the on-going compact descriptors for video analysis standard (CDVA) from the ISO/IEC moving pictures experts group (MPEG).
Incorporating Intra-Class Variance to Fine-Grained Visual Recognition
In this paper, we propose to leverage intra-class variance in metric learning of triplet network to improve the performance of fine-grained recognition.
Improving Object Detection with Region Similarity Learning
Object detection aims to identify instances of semantic objects of a certain class in images or videos.
Globally Variance-Constrained Sparse Representation and Its Application in Image Set Coding
Therefore, introducing an accurate rate-constraint in sparse coding and dictionary learning becomes meaningful, which has not been fully exploited in the context of sparse representation.
