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Found 141 papers, 67 papers with code
LL-ICM: Image Compression for Low-level Machine Vision via Large Vision-Language Model
By jointly optimizing compression and LL tasks, the proposed LL-ICM not only enriches its encoding ability in generalizing to versatile LL tasks but also optimizes the processing ability of down-stream LL task models, achieving mutual adaptation for image codecs and LL task models.
An Information-Theoretic Regularizer for Lossy Neural Image Compression
Lossy image compression networks aim to minimize the latent entropy of images while adhering to specific distortion constraints.
Compact Visual Data Representation for Green Multimedia -- A Human Visual System Perspective
The Human Visual System (HVS), with its intricate sophistication, is capable of achieving ultra-compact information compression for visual signals.
Mitigating Perception Bias: A Training-Free Approach to Enhance LMM for Image Quality Assessment
By applying these specific distortions to the query or test images, we ensure that the degraded images are recognized as poor quality while their semantics remain.
Large Language Models for Lossless Image Compression: Next-Pixel Prediction in Language Space is All You Need
We have recently witnessed that ``Intelligence" and `` Compression" are the two sides of the same coin, where the language large model (LLM) with unprecedented intelligence is a general-purpose lossless compressor for various data modalities.
Standardizing Generative Face Video Compression using Supplemental Enhancement Information
This paper proposes a Generative Face Video Compression (GFVC) approach using Supplemental Enhancement Information (SEI), where a series of compact spatial and temporal representations of a face video signal (i. e., 2D/3D key-points, facial semantics and compact features) can be coded using SEI message and inserted into the coded video bitstream.
Test-time adaptation for image compression with distribution regularization
Current test- or compression-time adaptation image compression (TTA-IC) approaches, which leverage both latent and decoder refinements as a two-step adaptation scheme, have potentially enhanced the rate-distortion (R-D) performance of learned image compression models on cross-domain compression tasks, \textit{e. g.,} from natural to screen content images.
Generative Human Video Compression with Multi-granularity Temporal Trajectory Factorization
In this paper, we propose a novel Multi-granularity Temporal Trajectory Factorization framework for generative human video compression, which holds great potential for bandwidth-constrained human-centric video communication.
Compressing Scene Dynamics: A Generative Approach
This paper proposes to learn generative priors from the motion patterns instead of video contents for generative video compression.
Beyond GFVC: A Progressive Face Video Compression Framework with Adaptive Visual Tokens
Recently, deep generative models have greatly advanced the progress of face video coding towards promising rate-distortion performance and diverse application functionalities.
Multimodal Clickbait Detection by De-confounding Biases Using Causal Representation Inference
Considering these features are often mixed up with unknown biases, we then disentangle three kinds of latent factors from them, including the invariant factor that indicates intrinsic bait intention; the causal factor which reflects deceptive patterns in a certain scenario, and non-causal noise.
Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyond
Large language models (LLMs) have not only revolutionized the field of natural language processing (NLP) but also have the potential to bring a paradigm shift in many other fields due to their remarkable abilities of language understanding, as well as impressive generalization capabilities and reasoning skills.
Horizon-Length Prediction: Advancing Fill-in-the-Middle Capabilities for Code Generation with Lookahead Planning
Moreover, model performance on FIM tasks deteriorates significantly without these unrealistic assumptions.
RCNet: Deep Recurrent Collaborative Network for Multi-View Low-Light Image Enhancement
Recent single image-based enhancement methods may not be able to provide consistently desirable restoration performance for all views due to the ignorance of potential feature correspondence among different views.
Image Provenance Analysis via Graph Encoding with Vision Transformer
Recent advances in AI-powered image editing tools have significantly lowered the barrier to image modification, raising pressing security concerns those related to spreading misinformation and disinformation on social platforms.
Unrolled Decomposed Unpaired Learning for Controllable Low-Light Video Enhancement
To address the above challenge, we propose the Unrolled Decomposed Unpaired Network (UDU-Net) for enhancing low-light videos by unrolling the optimization functions into a deep network to decompose the signal into spatial and temporal-related factors, which are updated iteratively.
Reward Difference Optimization For Sample Reweighting In Offline RLHF
Experiments with 7B LLMs on the HH and TL;DR datasets substantiate the effectiveness of our method in both automatic metrics and human evaluation, thereby highlighting its potential for aligning LLMs with human intent and values
Bi-Directional Deep Contextual Video Compression
In this paper, we introduce a bi-directional deep contextual video compression scheme tailored for B-frames, termed DCVC-B, to improve the compression performance of deep B-frame coding.
When Video Coding Meets Multimodal Large Language Models: A Unified Paradigm for Video Coding
Existing codecs are designed to eliminate intrinsic redundancies to create a compact representation for compression.
High Efficiency Image Compression for Large Visual-Language Models
In this paper, we pioneer to propose a variable bitrate image compression framework consisting of a pre-editing module and an end-to-end codec to achieve promising rate-accuracy performance for different LVLMs.
DDR: Exploiting Deep Degradation Response as Flexible Image Descriptor
In this paper, we present Deep Degradation Response (DDR), a method to quantify changes in image deep features under varying degradation conditions.
Opinion-Unaware Blind Image Quality Assessment using Multi-Scale Deep Feature Statistics
To bridge these gaps, we propose integrating deep features from pre-trained visual models with a statistical analysis model into a Multi-scale Deep Feature Statistics (MDFS) model for achieving opinion-unaware BIQA (OU-BIQA), thereby eliminating the reliance on human rating data and significantly improving training efficiency.
Adaptive Image Quality Assessment via Teaching Large Multimodal Model to Compare
Extensive experiments on nine IQA datasets validate that the Compare2Score effectively bridges text-defined comparative levels during training with converted single image quality score for inference, surpassing state-of-the-art IQA models across diverse scenarios.
Training LLMs to Better Self-Debug and Explain Code
We thus propose an automated pipeline to collect a high-quality dataset for code explanation and refinement by generating a number of explanations and refinement trajectories and filtering via execution verification.
Exposing AI-generated Videos: A Benchmark Dataset and a Local-and-Global Temporal Defect Based Detection Method
The generative model has made significant advancements in the creation of realistic videos, which causes security issues.
Gradient-Congruity Guided Federated Sparse Training
However, FL also faces challenges such as high computational and communication costs regarding resource-constrained devices, and poor generalization performance due to the heterogeneity of data across edge clients and the presence of out-of-distribution data.
FakeBench: Probing Explainable Fake Image Detection via Large Multimodal Models
Meanwhile, large multimodal models (LMMs) have exhibited immense visual-text capabilities on various tasks, bringing the potential for explainable fake image detection.
CompGS: Efficient 3D Scene Representation via Compressed Gaussian Splatting
Gaussian splatting, renowned for its exceptional rendering quality and efficiency, has emerged as a prominent technique in 3D scene representation.
MoE-FFD: Mixture of Experts for Generalized and Parameter-Efficient Face Forgery Detection
Deepfakes have recently raised significant trust issues and security concerns among the public.
CodeFort: Robust Training for Code Generation Models
Code generation models are not robust to small perturbations, which often lead to incorrect generations and significantly degrade the performance of these models.
PKU-DyMVHumans: A Multi-View Video Benchmark for High-Fidelity Dynamic Human Modeling
To facilitate the development of these fields, in this paper, we present PKU-DyMVHumans, a versatile human-centric dataset for high-fidelity reconstruction and rendering of dynamic human scenarios from dense multi-view videos.
Token Alignment via Character Matching for Subword Completion
Generative models, widely utilized in various applications, can often struggle with prompts corresponding to partial tokens.
Towards Open-ended Visual Quality Comparison
Comparative settings (e. g. pairwise choice, listwise ranking) have been adopted by a wide range of subjective studies for image quality assessment (IQA), as it inherently standardizes the evaluation criteria across different observers and offer more clear-cut responses.
SPC-NeRF: Spatial Predictive Compression for Voxel Based Radiance Field
Representing the Neural Radiance Field (NeRF) with the explicit voxel grid (EVG) is a promising direction for improving NeRFs.
ScreenAgent: A Vision Language Model-driven Computer Control Agent
Additionally, we construct the ScreenAgent Dataset, which collects screenshots and action sequences when completing a variety of daily computer tasks.
Generative Visual Compression: A Review
Artificial Intelligence Generated Content (AIGC) is leading a new technical revolution for the acquisition of digital content and impelling the progress of visual compression towards competitive performance gains and diverse functionalities over traditional codecs.
2AFC Prompting of Large Multimodal Models for Image Quality Assessment
While abundant research has been conducted on improving high-level visual understanding and reasoning capabilities of large multimodal models~(LMMs), their visual quality assessment~(IQA) ability has been relatively under-explored.
Code-Aware Prompting: A study of Coverage Guided Test Generation in Regression Setting using LLM
Recent works using large language models (LLMs) for test generation have focused on improving generation quality through optimizing the test generation context and correcting errors in model outputs, but use fixed prompting strategies that prompt the model to generate tests without additional guidance.
Deep Shape-Texture Statistics for Completely Blind Image Quality Evaluation
The perceptual quality is quantified by the variant Mahalanobis Distance between the inner and outer Shape-Texture Statistics (DSTS), wherein the inner and outer statistics respectively describe the quality fingerprints of the distorted image and natural images.
CLIB-FIQA: Face Image Quality Assessment with Confidence Calibration
Furthermore to alleviate the issue of the model placing excessive trust in inaccurate quality anchors we propose a confidence calibration method to correct the quality distribution by exploiting to the fullest extent of these objective quality factors characterized as the merged-factor distribution during training.
Mitigating the Impact of False Negatives in Dense Retrieval with Contrastive Confidence Regularization
Hard negative sampling, which is commonly used to improve contrastive learning, can introduce more noise in training.
Scalable Face Image Coding via StyleGAN Prior: Towards Compression for Human-Machine Collaborative Vision
The accelerated proliferation of visual content and the rapid development of machine vision technologies bring significant challenges in delivering visual data on a gigantic scale, which shall be effectively represented to satisfy both human and machine requirements.
Generative Face Video Coding Techniques and Standardization Efforts: A Review
Generative Face Video Coding (GFVC) techniques can exploit the compact representation of facial priors and the strong inference capability of deep generative models, achieving high-quality face video communication in ultra-low bandwidth scenarios.
Pixel-Inconsistency Modeling for Image Manipulation Localization
Digital image forensics plays a crucial role in image authentication and manipulation localization.
Semantic Face Compression for Metaverse: A Compact 3D Descriptor Based Approach
In this letter, we envision a new metaverse communication paradigm for virtual avatar faces, and develop the semantic face compression with compact 3D facial descriptors.
Perceptual Quality Assessment of 360$^\circ$ Images Based on Generative Scanpath Representation
To address these issues, we introduce a unique generative scanpath representation (GSR) for effective quality inference of 360$^\circ$ images, which aggregates varied perceptual experiences of multi-hypothesis users under a predefined viewing condition.
Extreme Image Compression using Fine-tuned VQGANs
Remarkably, even with the loss of up to $20\%$ of indices, the images can be effectively restored with minimal perceptual loss.
Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive Recommendation
Through theoretical analyses, we find that the conservatism of existing methods fails in pursuing users' long-term satisfaction.
Glow in the Dark: Low-Light Image Enhancement with External Memory
Benefiting from the learned memory, more complex distributions of reference images in the entire dataset can be “remembered” to facilitate the adjustment of the testing samples more adaptively.
Ranked #2 on
Low-Light Image Enhancement
on LOL-v2
Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language Models
Large Language Models (LLMs) show promising results in language generation and instruction following but frequently "hallucinate", making their outputs less reliable.
The Age of Synthetic Realities: Challenges and Opportunities
Synthetic realities are digital creations or augmentations that are contextually generated through the use of Artificial Intelligence (AI) methods, leveraging extensive amounts of data to construct new narratives or realities, regardless of the intent to deceive.
Neuron Activation Coverage: Rethinking Out-of-distribution Detection and Generalization
The out-of-distribution (OOD) problem generally arises when neural networks encounter data that significantly deviates from the training data distribution, i. e., in-distribution (InD).
Geometric Prior Based Deep Human Point Cloud Geometry Compression
The emergence of digital avatars has raised an exponential increase in the demand for human point clouds with realistic and intricate details.
Optimization-Inspired Cross-Attention Transformer for Compressive Sensing
And, PGCA block achieves an enhanced information interaction, which introduces the inertia force into the gradient descent step through a cross attention block.
Perceptual Quality Assessment of Face Video Compression: A Benchmark and An Effective Method
In this paper, we introduce the large-scale Compressed Face Video Quality Assessment (CFVQA) database, which is the first attempt to systematically understand the perceptual quality and diversified compression distortions in face videos.
Greener yet Powerful: Taming Large Code Generation Models with Quantization
Such large models incur significant resource usage (in terms of memory, latency, and dollars) as well as carbon footprint.
Enhancing General Face Forgery Detection via Vision Transformer with Low-Rank Adaptation
Nowadays, forgery faces pose pressing security concerns over fake news, fraud, impersonation, etc.
Gap-closing Matters: Perceptual Quality Evaluation and Optimization of Low-Light Image Enhancement
Subsequently, we propose an objective quality assessment measure that plays a critical role in bridging the gap between visual quality and enhancement.
Interactive Face Video Coding: A Generative Compression Framework
In this paper, we propose a novel framework for Interactive Face Video Coding (IFVC), which allows humans to interact with the intrinsic visual representations instead of the signals.
Are Diffusion Models Vulnerable to Membership Inference Attacks?
In this paper, we investigate the vulnerability of diffusion models to Membership Inference Attacks (MIAs), a common privacy concern.
M3FAS: An Accurate and Robust MultiModal Mobile Face Anti-Spoofing System
Face presentation attacks (FPA), also known as face spoofing, have brought increasing concerns to the public through various malicious applications, such as financial fraud and privacy leakage.
Troubleshooting Ethnic Quality Bias with Curriculum Domain Adaptation for Face Image Quality Assessment
Furthermore, we design an easy-to-hard training scheduler based on the inter-domain uncertainty and intra-domain quality margin as well as the ranking-based domain adversarial network to enhance the effectiveness of transfer learning and further reduce the source risk in domain adaptation.
ScanDMM: A Deep Markov Model of Scanpath Prediction for 360deg Images
Scanpath prediction for 360deg images aims to produce dynamic gaze behaviors based on the human visual perception mechanism.
ReCode: Robustness Evaluation of Code Generation Models
Most existing works on robustness in text or code tasks have focused on classification, while robustness in generation tasks is an uncharted area and to date there is no comprehensive benchmark for robustness in code generation.
Two-branch Multi-scale Deep Neural Network for Generalized Document Recapture Attack Detection
The image recapture attack is an effective image manipulation method to erase certain forensic traces, and when targeting on personal document images, it poses a great threat to the security of e-commerce and other web applications.
Generalization Beyond Feature Alignment: Concept Activation-Guided Contrastive Learning
Specifically, by treating feature elements as neuron activation states, we show that conventional alignment methods tend to deteriorate the diversity of learned invariant features, as they indiscriminately minimize all neuron activation differences.
DeepDC: Deep Distance Correlation as a Perceptual Image Quality Evaluator
ImageNet pre-trained deep neural networks (DNNs) show notable transferability for building effective image quality assessment (IQA) models.
Multi-lingual Evaluation of Code Generation Models
Using these benchmarks, we are able to assess the performance of code generation models in a multi-lingual fashion, and discovered generalization ability of language models on out-of-domain languages, advantages of multi-lingual models over mono-lingual, the ability of few-shot prompting to teach the model new languages, and zero-shot translation abilities even on mono-lingual settings.
Digital and Physical Face Attacks: Reviewing and One Step Further
With the rapid progress over the past five years, face authentication has become the most pervasive biometric recognition method.
Learning from Mixed Datasets: A Monotonic Image Quality Assessment Model
Deep learning based image quality assessment (IQA) models usually learn to predict image quality from a single dataset, leading the model to overfit specific scenes.
Just Noticeable Difference Modeling for Face Recognition System
In particular, in order to maximum redundancy removal without impairment of robust identity information, we apply the encoder with multiple feature extraction and attention-based feature decomposition modules to progressively decompose face features into two uncorrelated components, i. e., identity and residual features, via self-supervised learning.
Deep Feature Statistics Mapping for Generalized Screen Content Image Quality Assessment
The underlying mechanism of the proposed approach is based upon the mild assumption that the SCIs, which are not physically acquired, still obey certain statistics that could be understood in a learning fashion.
A Weakly Supervised Learning Framework for Salient Object Detection via Hybrid Labels
In this paper, we focus on a new weakly-supervised SOD task under hybrid labels, where the supervision labels include a large number of coarse labels generated by the traditional unsupervised method and a small number of real labels.
Ranked #6 on
RGB Salient Object Detection
on PASCAL-S
High Dynamic Range Image Quality Assessment Based on Frequency Disparity
In this paper, a novel and effective image quality assessment (IQA) algorithm based on frequency disparity for high dynamic range (HDR) images is proposed, termed as local-global frequency feature-based model (LGFM).
General Cutting Planes for Bound-Propagation-Based Neural Network Verification
Our generalized bound propagation method, GCP-CROWN, opens up the opportunity to apply general cutting plane methods for neural network verification while benefiting from the efficiency and GPU acceleration of bound propagation methods.
DeepWSD: Projecting Degradations in Perceptual Space to Wasserstein Distance in Deep Feature Space
Existing deep learning-based full-reference IQA (FR-IQA) models usually predict the image quality in a deterministic way by explicitly comparing the features, gauging how severely distorted an image is by how far the corresponding feature lies from the space of the reference images.
Cycle-Interactive Generative Adversarial Network for Robust Unsupervised Low-Light Enhancement
Getting rid of the fundamental limitations in fitting to the paired training data, recent unsupervised low-light enhancement methods excel in adjusting illumination and contrast of images.
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.
Ranked #34 on
Video Quality Assessment
on MSU SR-QA Dataset
Full-Reference Image Quality Assessment
Image Super-Resolution
+1
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 via Gradient Alignment
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.
Ranked #31 on
Video Quality Assessment
on MSU SR-QA Dataset
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.
Ranked #58 on
Domain Generalization
on PACS
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).
Improving Object Detection with Region Similarity Learning
Object detection aims to identify instances of semantic objects of a certain class in images or videos.
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.
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.
