Search Results for author:
Found 144 papers, 54 papers with code
A survey of using EHR as real-world evidence for discovering and validating new drug indications
Electronic Health Records (EHRs) have been increasingly used as real-world evidence (RWE) to support the discovery and validation of new drug indications.
LAFR: Efficient Diffusion-based Blind Face Restoration via Latent Codebook Alignment Adapter
Blind face restoration from low-quality (LQ) images is a challenging task that requires not only high-fidelity image reconstruction but also the preservation of facial identity.
Grounding Language with Vision: A Conditional Mutual Information Calibrated Decoding Strategy for Reducing Hallucinations in LVLMs
Large Vision-Language Models (LVLMs) are susceptible to hallucinations, where generated responses seem semantically plausible yet exhibit little or no relevance to the input image.
Your Language Model Can Secretly Write Like Humans: Contrastive Paraphrase Attacks on LLM-Generated Text Detectors
By subtracting the machine-like patterns from the human-like distribution during the decoding process, CoPA is able to produce sentences that are less discernible by text detectors.
$γ$-FedHT: Stepsize-Aware Hard-Threshold Gradient Compression in Federated Learning
Contemporary state-of-the-art sparse compressors, such as Top-$k$, exhibit high computational complexity, up to $\mathcal{O}(d\log_2{k})$, where $d$ is the number of model parameters.
High Quality Underwater Image Compression with Adaptive Correction and Codebook-based Augmentation
With the increasing exploration and exploitation of the underwater world, underwater images have become a critical medium for human interaction with marine environments, driving extensive research into their efficient transmission and storage.
Towards Facial Image Compression with Consistency Preserving Diffusion Prior
Simply adapting diffusion-based compression methods to facial compression tasks results in reconstructed images that perform poorly in downstream applications due to insufficient preservation of high-frequency information.
DeCLIP: Decoupled Learning for Open-Vocabulary Dense Perception
To address this issue, we propose DeCLIP, a novel framework that enhances CLIP by decoupling the self-attention module to obtain ``content'' and ``context'' features respectively.
Embracing Collaboration Over Competition: Condensing Multiple Prompts for Visual In-Context Learning
Visual In-Context Learning (VICL) enables adaptively solving vision tasks by leveraging pixel demonstrations, mimicking human-like task completion through analogy.
GaussTrap: Stealthy Poisoning Attacks on 3D Gaussian Splatting for Targeted Scene Confusion
As 3D Gaussian Splatting (3DGS) emerges as a breakthrough in scene representation and novel view synthesis, its rapid adoption in safety-critical domains (e. g., autonomous systems, AR/VR) urgently demands scrutiny of potential security vulnerabilities.
Hua-Chen New Theory of Economic Optimization
Between 1957-1985, Chinese mathematician Loo-Keng Hua pioneered economic optimization theory through three key contributions: establishing economic stability's fundamental theorem, proving the uniqueness of equilibrium solutions in economic systems, and developing a consumption-integrated model 50 days before his death.
SlimPipe: Memory-Thrifty and Efficient Pipeline Parallelism for Long-Context LLM Training
For example, on the Llama 70B model, compared to state-of-the-art methods, SlimPipe significantly boosts the Model FLOPs Utilization (MFU) to up to $1. 57\times$ for a context length of 512K.
AutoSSVH: Exploring Automated Frame Sampling for Efficient Self-Supervised Video Hashing
To address this limitation, we propose a new framework, termed AutoSSVH, that employs adversarial frame sampling with hash-based contrastive learning.
CTSR: Controllable Fidelity-Realness Trade-off Distillation for Real-World Image Super Resolution
Real-world image super-resolution is a critical image processing task, where two key evaluation criteria are the fidelity to the original image and the visual realness of the generated results.
GeoRSMLLM: A Multimodal Large Language Model for Vision-Language Tasks in Geoscience and Remote Sensing
The application of Vision-Language Models (VLMs) in remote sensing (RS) has demonstrated significant potential in traditional tasks such as scene classification, object detection, and image captioning.
Multi-Agent Image Restoration
Image restoration (IR) is challenging due to the complexity of real-world degradations.
OASIS: Order-Augmented Strategy for Improved Code Search
However, due to the sparse nature of code contexts, training solely by comparing the major differences between positive and negative pairs may fail to capture deeper semantic nuances.
Grammar-Based Code Representation: Is It a Worthy Pursuit for LLMs?
Further analysis shows that grammar-based representations enhance LLMs' ability to discern subtle code differences, reducing semantic errors caused by minor variations.
Predicting Nonlinear Interference for Short-Blocklength 4D Probabilistic Shaping
We derive a heuristic nonlinear interference model for 4D probabilistic shaping considering the polarization and time correlation of the 4D symbols.
Retrievals Can Be Detrimental: A Contrastive Backdoor Attack Paradigm on Retrieval-Augmented Diffusion Models
Subsequently, a malicious variant of contrastive learning is adopted to inject backdoors into the retriever, which builds shortcuts from triggers to the toxicity surrogates.
PMA: Towards Parameter-Efficient Point Cloud Understanding via Point Mamba Adapter
To overcome this limitation, we propose an orthogonal solution: Point Mamba Adapter (PMA), which constructs an ordered feature sequence from all layers of the pre-trained model and leverages Mamba to fuse all complementary semantics, thereby promoting comprehensive point cloud understanding.
Hierarchical Features Matter: A Deep Exploration of Progressive Parameterization Method for Dataset Distillation
Current parameterization methods achieve enhanced performance under extremely high compression ratio by optimizing determined synthetic dataset in informative feature domain.
Level-Navi Agent: A Framework and benchmark for Chinese Web Search Agents
To address these issues, we propose a general-purpose and training-free web search agent by level-aware navigation, Level-Navi Agent, accompanied by a well-annotated dataset (Web24) and a suitable evaluation metric.
On Shaping Gain of Multidimensional Constellation in Linear and Nonlinear Optical Fiber Channel
In this paper, we introduce an analytical nonlinear interference (NLI) power model-based shaping gain estimation method to enable a fast performance evaluation of various MD modulation formats in coherent dual-polarization (DP) optical transmission system.
Efficient Dataset Distillation via Diffusion-Driven Patch Selection for Improved Generalization
Dataset distillation offers an efficient way to reduce memory and computational costs by optimizing a smaller dataset with performance comparable to the full-scale original.
Going Beyond Feature Similarity: Effective Dataset Distillation based on Class-Aware Conditional Mutual Information
Dataset distillation (DD) aims to minimize the time and memory consumption needed for training deep neural networks on large datasets, by creating a smaller synthetic dataset that has similar performance to that of the full real dataset.
RealOSR: Latent Unfolding Boosting Diffusion-based Real-world Omnidirectional Image Super-Resolution
Omnidirectional image super-resolution (ODISR) aims to upscale low-resolution (LR) omnidirectional images (ODIs) to high-resolution (HR), addressing the growing demand for detailed visual content across a $180^{\circ}\times360^{\circ}$ viewport.
OmniGuard: Hybrid Manipulation Localization via Augmented Versatile Deep Image Watermarking
To address these challenges, we propose OmniGuard, a novel augmented versatile watermarking approach that integrates proactive embedding with passive, blind extraction for robust copyright protection and tamper localization.
Editable-DeepSC: Reliable Cross-Modal Semantic Communications for Facial Editing
However, the communication challenges associated with Semantic Facial Editing, one of the most important real-time CV applications on social media, still remain largely unexplored.
OSMamba: Omnidirectional Spectral Mamba with Dual-Domain Prior Generator for Exposure Correction
Exposure correction is a fundamental problem in computer vision and image processing.
Practical Compact Deep Compressed Sensing
Recent years have witnessed the success of deep networks in compressed sensing (CS), which allows for a significant reduction in sampling cost and has gained growing attention since its inception.
Multi-path Exploration and Feedback Adjustment for Text-to-Image Person Retrieval
Text-based person retrieval aims to identify the specific persons using textual descriptions as queries.
A Data-driven Crowd Simulation Framework Integrating Physics-informed Machine Learning with Navigation Potential Fields
Our approach leverages the strengths of both physical models and PIML.
BoostAdapter: Improving Vision-Language Test-Time Adaptation via Regional Bootstrapping
The historical samples are filtered from the testing data stream and serve to extract useful information from the target distribution, while the boosting samples are drawn from regional bootstrapping and capture the knowledge of the test sample itself.
Block-to-Scene Pre-training for Point Cloud Hybrid-Domain Masked Autoencoders
We first propose a hybrid-domain masked autoencoder consisting of an encoder and decoder belonging to the scene domain and object domain, respectively.
Towards Scalable Semantic Representation for Recommendation
With recent advances in large language models (LLMs), there has been emerging numbers of research in developing Semantic IDs based on LLMs to enhance the performance of recommendation systems.
CALoR: Towards Comprehensive Model Inversion Defense
Model Inversion Attacks (MIAs) aim at recovering privacy-sensitive training data from the knowledge encoded in the released machine learning models.
ReFIR: Grounding Large Restoration Models with Retrieval Augmentation
In this paper, we propose an orthogonal solution called the Retrieval-augmented Framework for Image Restoration (ReFIR), which incorporates retrieved images as external knowledge to extend the knowledge boundary of existing LRMs in generating details faithful to the original scene.
MIBench: A Comprehensive Framework for Benchmarking Model Inversion Attack and Defense
Model Inversion (MI) attacks aim at leveraging the output information of target models to reconstruct privacy-sensitive training data, raising critical concerns regarding the privacy vulnerabilities of Deep Neural Networks (DNNs).
COSMIC: Compress Satellite Images Efficiently via Diffusion Compensation
We first design a lightweight encoder (i. e. reducing FLOPs by 2. 6~5x) on satellite to achieve a high image compression ratio to save satellite-to-ground links.
Multidimensional Voronoi Constellations vs. Short Blocklength Probabilistic Shaping: A Comparison for Multilevel Coding Approach
Performance of concatenated multilevel coding with probabilistic shaping (PS) and Voronoi constellations (VCs) is analysed over AWGN channel.
3D-LMVIC: Learning-based Multi-View Image Coding with 3D Gaussian Geometric Priors
Furthermore, we introduce a depth map compression model to minimize geometric redundancy across views, along with a multi-view sequence ordering strategy based on a defined distance measure between views to enhance correlations between adjacent views.
GaussianSR: High Fidelity 2D Gaussian Splatting for Arbitrary-Scale Image Super-Resolution
Although these approaches have shown promising results, their performance is constrained by the limited representation ability of discrete latent codes in the encoded features.
A Closer Look at GAN Priors: Exploiting Intermediate Features for Enhanced Model Inversion Attacks
Model Inversion (MI) attacks aim to reconstruct privacy-sensitive training data from released models by utilizing output information, raising extensive concerns about the security of Deep Neural Networks (DNNs).
CLIP-Guided Generative Networks for Transferable Targeted Adversarial Attacks
Transferable targeted adversarial attacks aim to mislead models into outputting adversary-specified predictions in black-box scenarios.
Experimental Demonstration of 16D Voronoi Constellation with Two-Level Coding over 50km Four-Core Fiber
A 16-dimensional Voronoi constellation concatenated with multilevel coding is experimentally demonstrated over a 50km four-core fiber transmission system.
A Joint Approach to Local Updating and Gradient Compression for Efficient Asynchronous Federated Learning
Asynchronous Federated Learning (AFL) confronts inherent challenges arising from the heterogeneity of devices (e. g., their computation capacities) and low-bandwidth environments, both potentially causing stale model updates (e. g., local gradients) for global aggregation.
MEAT: Median-Ensemble Adversarial Training for Improving Robustness and Generalization
Self-ensemble adversarial training methods improve model robustness by ensembling models at different training epochs, such as model weight averaging (WA).
Hierarchical Features Matter: A Deep Exploration of GAN Priors for Improved Dataset Distillation
Dataset distillation is an emerging dataset reduction method, which condenses large-scale datasets while maintaining task accuracy.
One Perturbation is Enough: On Generating Universal Adversarial Perturbations against Vision-Language Pre-training Models
In this paper, we reveal that VLP models are also vulnerable to the instance-agnostic universal adversarial perturbation (UAP).
GI-NAS: Boosting Gradient Inversion Attacks through Adaptive Neural Architecture Search
In this paper, we further exploit such implicit prior knowledge by proposing Gradient Inversion via Neural Architecture Search (GI-NAS), which adaptively searches the network and captures the implicit priors behind neural architectures.
OV-DQUO: Open-Vocabulary DETR with Denoising Text Query Training and Open-World Unknown Objects Supervision
However, existing open-vocabulary detectors trained on base category data tend to assign higher confidence to trained categories and confuse novel categories with the background.
Ranked #3 on
Open Vocabulary Object Detection
on LVIS v1.0
LCM: Locally Constrained Compact Point Cloud Model for Masked Point Modeling
Considering the varying information density between masked and unmasked patches in the decoder inputs of MPM, we introduce a locally constrained Mamba-based decoder.
Augmented Risk Prediction for the Onset of Alzheimer's Disease from Electronic Health Records with Large Language Models
Specifically, we develop a collaborative pipeline that combines SLs and LLMs via a confidence-driven decision-making mechanism, leveraging the strengths of SLs in clear-cut cases and LLMs in more complex scenarios.
MambaVC: Learned Visual Compression with Selective State Spaces
Learned visual compression is an important and active task in multimedia.
Decoding at the Speed of Thought: Harnessing Parallel Decoding of Lexical Units for LLMs
The core of our approach is the observation that a pre-trained language model can confidently predict multiple contiguous tokens, forming the basis for a \textit{lexical unit}, in which these contiguous tokens could be decoded in parallel.
GMMFormer v2: An Uncertainty-aware Framework for Partially Relevant Video Retrieval
Given a text query, partially relevant video retrieval (PRVR) aims to retrieve untrimmed videos containing relevant moments.
Invertible Residual Rescaling Models
To address this issue, we propose Invertible Residual Rescaling Models (IRRM) for image rescaling by learning a bijection between a high-resolution image and its low-resolution counterpart with a specific distribution.
ResVR: Joint Rescaling and Viewport Rendering of Omnidirectional Images
With the advent of virtual reality technology, omnidirectional image (ODI) rescaling techniques are increasingly embraced for reducing transmitted and stored file sizes while preserving high image quality.
Weakly Supervised Deep Hyperspherical Quantization for Image Retrieval
Deep quantization methods have shown high efficiency on large-scale image retrieval.
Learning Multiple Representations with Inconsistency-Guided Detail Regularization for Mask-Guided Matting
Our framework and model introduce the following key aspects: (1) to learn real-world adaptive semantic representation for objects with diverse and complex structures under real-world scenes, we introduce extra semantic segmentation and edge detection tasks on more diverse real-world data with segmentation annotations; (2) to avoid overfitting on low-level details, we propose a module to utilize the inconsistency between learned segmentation and matting representations to regularize detail refinement; (3) we propose a novel background line detection task into our auxiliary learning framework, to suppress interference of background lines or textures.
Invertible Diffusion Models for Compressed Sensing
To enable such memory-intensive end-to-end fine-tuning, we propose a novel two-level invertible design to transform both (1) multi-step sampling process and (2) noise estimation U-Net in each step into invertible networks.
Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off
This capability implies that our FedCEO can effectively recover the disrupted semantic information by smoothing the global semantic space for different privacy settings and continuous training processes.
Privacy Leakage on DNNs: A Survey of Model Inversion Attacks and Defenses
However, Model Inversion (MI) attacks, which disclose private information about the training dataset by abusing access to the trained models, have emerged as a formidable privacy threat.
Conversational Crowdsensing: A Parallel Intelligence Powered Novel Sensing Approach
The transition from CPS-based Industry 4. 0 to CPSS-based Industry 5. 0 brings new requirements and opportunities to current sensing approaches, especially in light of recent progress in Chatbots and Large Language Models (LLMs).
An Efficient Implicit Neural Representation Image Codec Based on Mixed Autoregressive Model for Low-Complexity Decoding
To the best of our knowledge, our method is the first INR-based codec comparable with Hyperprior in both decoding speed and quality while maintaining low complexity.
MB-RACS: Measurement-Bounds-based Rate-Adaptive Image Compressed Sensing Network
In this paper, we propose a Measurement-Bounds-based Rate-Adaptive Image Compressed Sensing Network (MB-RACS) framework, which aims to adaptively determine the sampling rate for each image block in accordance with traditional measurement bounds theory.
FFCA-Net: Stereo Image Compression via Fast Cascade Alignment of Side Information
To address this limitation, we propose a Feature-based Fast Cascade Alignment network (FFCA-Net) to fully leverage the side information on the decoder.
Large Language Models in Medical Term Classification and Unexpected Misalignment Between Response and Reasoning
This study assesses the ability of state-of-the-art large language models (LLMs) including GPT-3. 5, GPT-4, Falcon, and LLaMA 2 to identify patients with mild cognitive impairment (MCI) from discharge summaries and examines instances where the models' responses were misaligned with their reasoning.
Towards Compact 3D Representations via Point Feature Enhancement Masked Autoencoders
Specifically, to learn more compact features, a share-parameter Transformer encoder is introduced to extract point features from the global and local unmasked patches obtained by global random and local block mask strategies, followed by a specific decoder to reconstruct.
Ranked #5 on
Few-Shot 3D Point Cloud Classification
on ModelNet40 10-way (10-shot)
(using extra training data)
Parameter Efficient Adaptation for Image Restoration with Heterogeneous Mixture-of-Experts
In this work, we introduce an alternative solution to improve the generalization of image restoration models.
Progressive Learning with Visual Prompt Tuning for Variable-Rate Image Compression
In this paper, we propose a progressive learning paradigm for transformer-based variable-rate image compression.
Perceptual Image Compression with Cooperative Cross-Modal Side Information
The explosion of data has resulted in more and more associated text being transmitted along with images.
Data-Aware Gradient Compression for FL in Communication-Constrained Mobile Computing
Recognizing the computational limitations of mobile devices, we propose the DAGC-A, which is computationally less demanding and enhances the robustness of compression in non-IID scenarios.
USDC: Unified Static and Dynamic Compression for Visual Transformer
To alleviate two disadvantages of two categories of methods, we propose to unify the static compression and dynamic compression techniques jointly to obtain an input-adaptive compressed model, which can further better balance the total compression ratios and the model performances.
ASP: Automatic Selection of Proxy dataset for efficient AutoML
Deep neural networks have gained great success due to the increasing amounts of data, and diverse effective neural network designs.
Battle of the Large Language Models: Dolly vs LLaMA vs Vicuna vs Guanaco vs Bard vs ChatGPT -- A Text-to-SQL Parsing Comparison
The success of ChatGPT has ignited an AI race, with researchers striving to develop new large language models (LLMs) that can match or surpass the language understanding and generation abilities of commercial ones.
KwaiYiiMath: Technical Report
Recent advancements in large language models (LLMs) have demonstrated remarkable abilities in handling a variety of natural language processing (NLP) downstream tasks, even on mathematical tasks requiring multi-step reasoning.
Ranked #97 on
Arithmetic Reasoning
on GSM8K
(using extra training data)
MuseChat: A Conversational Music Recommendation System for Videos
The reasoning module, equipped with the power of Large Language Model (Vicuna-7B) and extended to multi-modal inputs, is able to provide reasonable explanation for the recommended music.
GMMFormer: Gaussian-Mixture-Model Based Transformer for Efficient Partially Relevant Video Retrieval
Current PRVR methods adopt scanning-based clip construction to achieve explicit clip modeling, which is information-redundant and requires a large storage overhead.
Enhancing Keyphrase Generation by BART Finetuning with Splitting and Shuffling
Keyphrase generation is a task of identifying a set of phrases that best repre-sent the main topics or themes of a given text.
Unified Language-Vision Pretraining in LLM with Dynamic Discrete Visual Tokenization
Specifically, we introduce a well-designed visual tokenizer to translate the non-linguistic image into a sequence of discrete tokens like a foreign language that LLM can read.
Adjacency-hopping de Bruijn Sequences for Non-repetitive Coding
A special type of cyclic sequences named adjacency-hopping de Bruijn sequences is introduced in this paper.
Self-Supervised Scalable Deep Compressed Sensing
Compressed sensing (CS) is a promising tool for reducing sampling costs.
On the Performance of Multidimensional Constellation Shaping for Linear and Nonlinear Optical Fiber Channel
Multidimensional constellation shaping of up to 32 dimensions with different spectral efficiencies are compared through AWGN and fiber-optic simulations.
Resource Constrained Model Compression via Minimax Optimization for Spiking Neural Networks
We propose an improved end-to-end Minimax optimization method for this sparse learning problem to better balance the model performance and the computation efficiency.
GIFD: A Generative Gradient Inversion Method with Feature Domain Optimization
Federated Learning (FL) has recently emerged as a promising distributed machine learning framework to preserve clients' privacy, by allowing multiple clients to upload the gradients calculated from their local data to a central server.
One-stage Low-resolution Text Recognition with High-resolution Knowledge Transfer
Current solutions for low-resolution text recognition (LTR) typically rely on a two-stage pipeline that involves super-resolution as the first stage followed by the second-stage recognition.
An Adaptive Model Ensemble Adversarial Attack for Boosting Adversarial Transferability
Alternatively, model ensemble adversarial attacks are proposed to fuse outputs from surrogate models with diverse architectures to get an ensemble loss, making the generated adversarial example more likely to transfer to other models as it can fool multiple models concurrently.
Deep Physics-Guided Unrolling Generalization for Compressed Sensing
By absorbing the merits of both the model- and data-driven methods, deep physics-engaged learning scheme achieves high-accuracy and interpretable image reconstruction.
Mega-cities dominate China's urban greening
Trees play a crucial role in urban environments, offering various ecosystem services that contribute to public health and human well-being.
Dynamic Path-Controllable Deep Unfolding Network for Compressive Sensing
Deep unfolding network (DUN) that unfolds the optimization algorithm into a deep neural network has achieved great success in compressive sensing (CS) due to its good interpretability and high performance.
Vision-Language Pre-training with Object Contrastive Learning for 3D Scene Understanding
In recent years, vision language pre-training frameworks have made significant progress in natural language processing and computer vision, achieving remarkable performance improvement on various downstream tasks.
Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Models
To conquer this limitation, we propose a novel Instance-aware Dynamic Prompt Tuning (IDPT) strategy for pre-trained point cloud models.
3D Parameter-Efficient Fine-Tuning for Classification
Few-Shot 3D Point Cloud Classification
+1
Unsupervised Anomaly Detection with Local-Sensitive VQVAE and Global-Sensitive Transformers
In this work, the VQVAE focus on feature extraction and reconstruction of images, and the transformers fit the manifold and locate anomalies in the latent space.
Progressive Content-aware Coded Hyperspectral Compressive Imaging
Hyperspectral imaging plays a pivotal role in a wide range of applications, like remote sensing, medicine, and cytology.
Analytical Model of Nonlinear Fiber Propagation for General Dual-Polarization Four-Dimensional Modulation Format
As an application of our model, we further study the effects of signal-noise interactions in long-haul transmission via the proposed model.
Revisiting Image Deblurring with an Efficient ConvNet
In this work, we propose a unified lightweight CNN network that features a large effective receptive field (ERF) and demonstrates comparable or even better performance than Transformers while bearing less computational costs.
Ranked #2 on
Image Defocus Deblurring
on DPD
GlowGAN: Unsupervised Learning of HDR Images from LDR Images in the Wild
Most in-the-wild images are stored in Low Dynamic Range (LDR) form, serving as a partial observation of the High Dynamic Range (HDR) visual world.
Learning Transferable Spatiotemporal Representations from Natural Script Knowledge
Pre-training on large-scale video data has become a common recipe for learning transferable spatiotemporal representations in recent years.
Learned Distributed Image Compression with Multi-Scale Patch Matching in Feature Domain
Specifically, MSFDPM consists of a side information feature extractor, a multi-scale feature domain patch matching module, and a multi-scale feature fusion network.
Multi-Scale Architectures Matter: On the Adversarial Robustness of Flow-based Lossless Compression
As a probabilistic modeling technique, the flow-based model has demonstrated remarkable potential in the field of lossless compression \cite{idf, idf++, lbb, ivpf, iflow},.
SIAD: Self-supervised Image Anomaly Detection System
To make a step forward, this paper outlines an automatic annotation system called SsaA, working in a self-supervised learning manner, for continuously making the online visual inspection in the manufacturing automation scenarios.
D3C2-Net: Dual-Domain Deep Convolutional Coding Network for Compressive Sensing
By mapping iterative optimization algorithms into neural networks (NNs), deep unfolding networks (DUNs) exhibit well-defined and interpretable structures and achieve remarkable success in the field of compressive sensing (CS).
Content-aware Scalable Deep Compressed Sensing
To more efficiently address image compressed sensing (CS) problems, we present a novel content-aware scalable network dubbed CASNet which collectively achieves adaptive sampling rate allocation, fine granular scalability and high-quality reconstruction.
Ranked #1 on
Image Compressed Sensing
on CBSD68
Cycle Self-Training for Semi-Supervised Object Detection with Distribution Consistency Reweighting
To address the coupling problem, we propose a Cycle Self-Training (CST) framework for SSOD, which consists of two teachers T1 and T2, two students S1 and S2.
Analytical SNR Prediction in Long-Haul Optical Transmission using General Dual-Polarization 4D Formats
Nonlinear interference models for dual-polarization 4D (DP-4D) modulation have only been used so far to predict signal-signal nonlinear interference.
Learning to Deblur using Light Field Generated and Real Defocus Images
We first train the network on a light field-generated dataset for its highly accurate image correspondence.
Ranked #1 on
Image Defocus Deblurring
on RealDOF
(using extra training data)
ViT-P: Rethinking Data-efficient Vision Transformers from Locality
We make vision transformers as data-efficient as convolutional neural networks by introducing multi-focal attention bias.
Hybrid Contrastive Quantization for Efficient Cross-View Video Retrieval
By performing Asymmetric-Quantized Contrastive Learning (AQ-CL) across views, HCQ aligns texts and videos at coarse-grained and multiple fine-grained levels.
Subspace modeling for fast and high-sensitivity X-ray chemical imaging
Resolving morphological chemical phase transformations at the nanoscale is of vital importance to many scientific and industrial applications across various disciplines.
Shaped Four-Dimensional Modulation Formats for Optical Fiber Communication Systems
We review the design of multidimensional modulations by maximizing generalized mutual information and compare the maximum transmission reach of recently introduced 4D formats.
Low-Complexity Geometrical Shaping for 4D Modulation Formats via Amplitude Coding
Signal shaping is vital to approach Shannon's capacity, yet it is challenging to implement at very high speeds.
Model-aided Geometrical Shaping of Dual-polarization 4D Formats in the Nonlinear Fiber Channel
The geometry of dual-polarization four-dimensional constellations is optimized in the optical fiber channel using a recent nonlinear interference model.
Memory-Augmented Deep Unfolding Network for Compressive Sensing
By understanding DUNs from the perspective of the human brain's memory processing, we find there exists two issues in existing DUNs.
Learning a self-supervised tone mapping operator via feature contrast masking loss
High Dynamic Range (HDR) content is becoming ubiquitous due to the rapid development of capture technologies.
Backdoor Attack on Hash-based Image Retrieval via Clean-label Data Poisoning
To this end, we propose the confusing perturbations-induced backdoor attack (CIBA).
Contrastive Quantization with Code Memory for Unsupervised Image Retrieval
The high efficiency in computation and storage makes hashing (including binary hashing and quantization) a common strategy in large-scale retrieval systems.
PHPQ: Pyramid Hybrid Pooling Quantization for Efficient Fine-Grained Image Retrieval
To improve fine-grained image hashing, we propose Pyramid Hybrid Pooling Quantization (PHPQ).
Unbiased IoU for Spherical Image Object Detection
In this paper, we first identify that spherical rectangles are unbiased bounding boxes for objects in spherical images, and then propose an analytical method for IoU calculation without any approximations.
COAST: COntrollable Arbitrary-Sampling NeTwork for Compressive Sensing
In this paper, we propose a novel COntrollable Arbitrary-Sampling neTwork, dubbed COAST, to solve CS problems of arbitrary-sampling matrices (including unseen sampling matrices) with one single model.
Universal Adversarial Head: Practical Protection against Video Data Leakage
We propose $universal \ adversarial \ head$ (UAH), which crafts adversarial query videos by prepending the original videos with a sequence of adversarial frames to perturb the normal hash codes in the Hamming space.
MoCL: Data-driven Molecular Fingerprint via Knowledge-aware Contrastive Learning from Molecular Graph
Second, the contrastive scheme only learns representations that are invariant to local perturbations and thus does not consider the global structure of the dataset, which may also be useful for downstream tasks.
Shape-driven Coordinate Ordering for Star Glyph Sets via Reinforcement Learning
Given a set of star glyphs associated to multiple class labels, we propose to use shape context descriptors to measure the perceptual distance between pairs of glyphs, and use the derived silhouette coefficient to measure the perception of class separability within the entire set.
Modeling of Nonlinear Interference Power for Dual-Polarization 4D Formats
We assess the accuracy of a recently introduced nonlinear interference model for general dual-polarization 4D formats.~ Unlike previous models for polarization-multiplexed 2D formats, an average gap from split-step Fourier simulations within 0. 1 dB is demonstrated.
Transformer-based Language Model Fine-tuning Methods for COVID-19 Fake News Detection
However, universal language models may perform weakly in these fake news detection for lack of large-scale annotated data and sufficient semantic understanding of domain-specific knowledge.
Robust Collaborative Learning with Noisy Labels
In this paper, we study the underlying mechanism of how disagreement and agreement between networks can help reduce the noise in gradients and develop a novel framework called Robust Collaborative Learning (RCL) that leverages both disagreement and agreement among networks.
Dual Refinement Feature Pyramid Networks for Object Detection
SRB learns the location and content of sampling points based on contextual information between adjacent levels.
TAP-Net: Transport-and-Pack using Reinforcement Learning
Using a learning-based approach, a trained network can learn and encode solution patterns to guide the solution of new problem instances instead of executing an expensive online search.
Machine Learning in Heliophysics and Space Weather Forecasting: A White Paper of Findings and Recommendations
The authors of this white paper met on 16-17 January 2020 at the New Jersey Institute of Technology, Newark, NJ, for a 2-day workshop that brought together a group of heliophysicists, data providers, expert modelers, and computer/data scientists.
Targeted Attack for Deep Hashing based Retrieval
In this paper, we propose a novel method, dubbed deep hashing targeted attack (DHTA), to study the targeted attack on such retrieval.
Analysis and Experimental Demonstration of Orthant-Symmetric Four-dimensional 7 bit/4D-sym Modulation for Optical Fiber Communication
We propose a new four-dimensional orthant-symmetric 128-ary modulation format (4D-OS128) with a spectral efficiency of 7bit/4D-sym.
Adversarial Attack on Deep Product Quantization Network for Image Retrieval
Deep product quantization network (DPQN) has recently received much attention in fast image retrieval tasks due to its efficiency of encoding high-dimensional visual features especially when dealing with large-scale datasets.
End-to-End Learning of Geometrical Shaping Maximizing Generalized Mutual Information
GMI-based end-to-end learning is shown to be highly nonconvex.
The Entropy of Artificial Intelligence and a Case Study of AlphaZero from Shannon's Perspective
Firstly, as there is a finite number of possibilities in the game, is there a quantifiable intelligence measurement for evaluating intelligent systems, e. g. AlphaZero?
An Unified Intelligence-Communication Model for Multi-Agent System Part-I: Overview
Motivated by Shannon's model and recent rehabilitation of self-supervised artificial intelligence having a "World Model", this paper propose an unified intelligence-communication (UIC) model for describing a single agent and any multi-agent system.
A Study of Deep Feature Fusion based Methods for Classifying Multi-lead ECG
An automatic classification method has been studied to effectively detect and recognize Electrocardiogram (ECG).
Aspects of Warped AdS$_3$/CFT$_2$ Correspondence
In this paper we apply the thermodynamics method to investigate the holographic pictures for the BTZ black hole, the spacelike and the null warped black holes in three-dimensional topologically massive gravity (TMG) and new massive gravity (NMG).
High Energy Physics - Theory General Relativity and Quantum Cosmology
