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Found 10 papers, 3 papers with code
Neural Network Assisted Lifting Steps For Improved Fully Scalable Lossy Image Compression in JPEG 2000
The proposed approach involves two steps, a high-to-low step followed by a low-to-high step.
Enhancing Long-Term Recommendation with Bi-level Learnable Large Language Model Planning
To achieve this, we propose a Bi-level Learnable LLM Planner framework, which combines macro-learning and micro-learning through a hierarchical mechanism.
Exploration of Learned Lifting-Based Transform Structures for Fully Scalable and Accessible Wavelet-Like Image Compression
Moreover, we demonstrate that employing more learned lifting steps and more layers in each learned lifting operator do not contribute strongly to the compression performance.
Iris Liveness Detection Competition (LivDet-Iris) -- The 2023 Edition
New elements in this fifth competition include (1) GAN-generated iris images as a category of presentation attack instruments (PAI), and (2) an evaluation of human accuracy at detecting PAI as a reference benchmark.
Assessing cognitive function among older adults using machine learning and wearable device data: a feasibility study
Timely implementation of interventions to slow cognitive decline among older adults requires accurate monitoring to detect changes in cognitive function.
Bias Behind the Wheel: Fairness Analysis of Autonomous Driving Systems
This paper analyzes fairness in automated pedestrian detection, a crucial but under-explored issue in autonomous driving systems.
Camouflaged Object Detection with Feature Grafting and Distractor Aware
In order to better explore the advantages of the two encoders, we design a cross-attention-based Feature Grafting Module to graft features extracted from Transformer branch into CNN branch, after which the features are aggregated in the Feature Fusion Module.
Under-Parameterized Double Descent for Ridge Regularized Least Squares Denoising of Data on a Line
For larger values of $\mu$, we observe that the curve for the norm of the estimator has a peak but that this no longer translates to a peak in the generalization error.
Robust Calibrate Proxy Loss for Deep Metric Learning
The mainstream researche in deep metric learning can be divided into two genres: proxy-based and pair-based methods.
PLOT: Prompt Learning with Optimal Transport for Vision-Language Models
To solve this problem, we propose to apply optimal transport to match the vision and text modalities.
