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Found 48 papers, 14 papers with code
LEADRE: Multi-Faceted Knowledge Enhanced LLM Empowered Display Advertisement Recommender System
Display advertising provides significant value to advertisers, publishers, and users.
Channel Estimation for XL-IRS Assisted Wireless Systems with Double-sided Visibility Regions
To address this issue, we propose in this paper an efficient three-step XL-IRS channel estimation method.
OAPT: Offset-Aware Partition Transformer for Double JPEG Artifacts Removal
Specifically, the predictor estimates pixel offsets between the first and second compression, which are then utilized to divide different patterns.
Movable Antenna Enabled Symbiotic Radio Systems: An Opportunity for Mutualism
The utilization of MAs aims to enhance the secondary rate by optimizing the positions of MAs to improve the beam gain at the BD.
The Llama 3 Herd of Models
This paper presents a new set of foundation models, called Llama 3.
Ranked #2 on
Multi-task Language Understanding
on MMLU
QPT V2: Masked Image Modeling Advances Visual Scoring
To this end, we propose Quality- and aesthetics-aware pretraining (QPT V2), the first pretraining framework based on MIM that offers a unified solution to quality and aesthetics assessment.
A New Dataset and Framework for Real-World Blurred Images Super-Resolution
However, we find that the efficacy of recent methods obviously diminishes when employed on image data with blur, while image data with intentional blur constitute a substantial proportion of general data.
$L_p$-norm Distortion-Efficient Adversarial Attack
Adversarial examples have shown a powerful ability to make a well-trained model misclassified.
Query-Efficient Hard-Label Black-Box Attack against Vision Transformers
Moreover, we design a weight mask matrix for all patches to further optimize the perturbation on different regions of a whole image.
STAL3D: Unsupervised Domain Adaptation for 3D Object Detection via Collaborating Self-Training and Adversarial Learning
Existing 3D object detection suffers from expensive annotation costs and poor transferability to unknown data due to the domain gap, Unsupervised Domain Adaptation (UDA) aims to generalize detection models trained in labeled source domains to perform robustly on unexplored target domains, providing a promising solution for cross-domain 3D object detection.
Energy-storing analysis and fishtail stiffness optimization for a wire-driven elastic robotic fish
Results validate that the energy-storing of the active-segment elastic spine plays a vital role in improving the power fluctuations and maximum frequency of the motor by adjusting its stiffness reasonably, which is beneficial to achieving high propulsion and high speed for robotic fish.
PTM-VQA: Efficient Video Quality Assessment Leveraging Diverse PreTrained Models from the Wild
In this paper, we propose a VQA method named PTM-VQA, which leverages PreTrained Models to transfer knowledge from models pretrained on various pre-tasks, enabling benefits for VQA from different aspects.
©Plug-in Authorization for Human Content Copyright Protection in Text-to-Image Model
To mitigate this, we propose the \copyright Plug-in Authorization framework, introducing three operations: addition, extraction, and combination.
NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results
This paper reviews the NTIRE 2024 Challenge on Shortform UGC Video Quality Assessment (S-UGC VQA), where various excellent solutions are submitted and evaluated on the collected dataset KVQ from popular short-form video platform, i. e., Kuaishou/Kwai Platform.
NTIRE 2024 Challenge on Image Super-Resolution ($\times$4): Methods and Results
This paper reviews the NTIRE 2024 challenge on image super-resolution ($\times$4), highlighting the solutions proposed and the outcomes obtained.
Convergence analysis of controlled particle systems arising in deep learning: from finite to infinite sample size
Using these uniform regularity results, we show the convergence of the minima of objective functionals and optimal parameters of the neural SDEs as the sample size N tends to infinity.
CasSR: Activating Image Power for Real-World Image Super-Resolution
In particular, we develop a cascaded controllable diffusion model that aims to optimize the extraction of information from low-resolution images.
CPGA: Coding Priors-Guided Aggregation Network for Compressed Video Quality Enhancement
Specifically, the ITA module aggregates temporal information from consecutive frames and coding priors, while the MNA module globally captures spatial information guided by residual frames.
XPSR: Cross-modal Priors for Diffusion-based Image Super-Resolution
Diffusion-based methods, endowed with a formidable generative prior, have received increasing attention in Image Super-Resolution (ISR) recently.
KVQ: Kwai Video Quality Assessment for Short-form Videos
Short-form UGC video platforms, like Kwai and TikTok, have been an emerging and irreplaceable mainstream media form, thriving on user-friendly engagement, and kaleidoscope creation, etc.
Interpersonal Relationship Analysis with Dyadic EEG Signals via Learning Spatial-Temporal Patterns
Interpersonal relationship quality is pivotal in social and occupational contexts.
Decoding Mean Field Games from Population and Environment Observations By Gaussian Processes
This paper presents a Gaussian Process (GP) framework, a non-parametric technique widely acknowledged for regression and classification tasks, to address inverse problems in mean field games (MFGs).
Forecasting Volatility with Machine Learning and Rough Volatility: Example from the Crypto-Winter
We extend the application and test the performance of a recently introduced volatility prediction framework encompassing LSTM and rough volatility.
Feature Space Renormalization for Semi-supervised Learning
The common framework among recent approaches is to train the model on a large amount of unlabelled data with consistency regularization to constrain the model predictions to be invariant to input perturbation.
Dimension Independent Mixup for Hard Negative Sample in Collaborative Filtering
To address this limitation, we propose Dimension Independent Mixup for Hard Negative Sampling (DINS), which is the first Area-wise sampling method for training CF-based models.
Robust Equilibrium Strategy for Mean-Variance Portfolio Selection
Under our definition, a classical solution to the corresponding PDE system implies a robust equilibrium strategy.
OcTr: Octree-based Transformer for 3D Object Detection
A key challenge for LiDAR-based 3D object detection is to capture sufficient features from large scale 3D scenes especially for distant or/and occluded objects.
Paraphrase Identification with Deep Learning: A Review of Datasets and Methods
The rapid progress of Natural Language Processing (NLP) technologies has led to the widespread availability and effectiveness of text generation tools such as ChatGPT and Claude.
Object-Attentional Untargeted Adversarial Attack
Specifically, we first generate an object region by intersecting the object detection region from YOLOv4 with the salient object detection (SOD) region from HVPNet.
Robust control problems of BSDEs coupled with value functions
A robust control problem is considered in this paper, where the controlled stochastic differential equations (SDEs) include ambiguity parameters and their coefficients satisfy non-Lipschitz continuous and non-linear growth conditions, the objective function is expressed as a backward stochastic differential equation (BSDE) with the generator depending on the value function.
Power Forward Performance in Semimartingale Markets with Stochastic Integrated Factors
We study the forward investment performance process (FIPP) in an incomplete semimartingale market model with closed and convex portfolio constraints, when the investor's risk preferences are of the power form.
Robust lEarned Shrinkage-Thresholding (REST): Robust unrolling for sparse recover
In this paper, we consider deep neural networks for solving inverse problems that are robust to forward model mis-specifications.
Mean Field Portfolio Games
We study mean field portfolio games with random market parameters, where each player is concerned with not only her own wealth but also relative performance to her competitors.
The Discovery of Dynamics via Linear Multistep Methods and Deep Learning: Error Estimation
We put forward error estimates for these methods using the approximation property of deep networks.
Relative wealth concerns with partial information and heterogeneous priors
We observe that the agent with the most accurate prior estimate is likely to lead the herd, and the effect of competition on heterogeneous agents varies more with market characteristics compared to the homogeneous case.
Hyperspectral Unmixing Network Inspired by Unfolding an Optimization Problem
We first consider a linear mixture model with sparsity constraint, then we unfold Alternating Direction Method of Multipliers (ADMM) algorithm to construct the unmixing network structures.
Deep learning for smart fish farming: applications, opportunities and challenges
This change can create new opportunities and a series of challenges for information and data processing in smart fish farming.
Balance Between Efficient and Effective Learning: Dense2Sparse Reward Shaping for Robot Manipulation with Environment Uncertainty
The experiment results show that the Dense2Sparse method obtained higher expected reward compared with the ones using standalone dense reward or sparse reward, and it also has a superior tolerance of system uncertainty.
Lifetime Ruin under High-watermark Fees and Drift Uncertainty
This paper aims to make a new contribution to the study of lifetime ruin problem by considering investment in two hedge funds with high-watermark fees and drift uncertainty.
Comyco: Quality-Aware Adaptive Video Streaming via Imitation Learning
Using trace-driven and real-world experiments, we demonstrate significant improvements of Comyco's sample efficiency in comparison to prior work, with 1700x improvements in terms of the number of samples required and 16x improvements on training time required.
Portfolio diversification and model uncertainty: a robust dynamic mean-variance approach
The dynamic setting allows us to consider time varying ambiguity sets, which include the cases where the drift and correlation are estimated on a rolling window of historical data or when the investor takes into account learning on the ambiguity.
Pressure Predictions of Turbine Blades with Deep Learning
Deep learning has been used in many areas, such as feature detections in images and the game of go.
Segmentation of Drosophila Heart in Optical Coherence Microscopy Images Using Convolutional Neural Networks
Convolutional neural networks are powerful tools for image segmentation and classification.
Unsupervised Learning of Stereo Matching
However, due to the limitations of these datasets and the difficulty of collecting new stereo data, current methods fail in real-life cases.
Zero-order Reverse Filtering
In this paper, we study an unconventional but practically meaningful reversibility problem of commonly used image filters.
High-Quality Correspondence and Segmentation Estimation for Dual-Lens Smart-Phone Portraits
Estimating correspondence between two images and extracting the foreground object are two challenges in computer vision.
Binary Funding Impacts in Derivative Valuation
It is often believed that considering both DVA and funding benefits results in a double-counting issue but it will be shown that the two components are affected by different mathematical structures of derivative transactions.
Mutual-Structure for Joint Filtering
Previous joint/guided filters directly transfer the structural information in the reference image to the target one.
