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Found 15 papers, 1 papers with code
Data-induced multiscale losses and efficient multirate gradient descent schemes
This paper investigates the impact of multiscale data on machine learning algorithms, particularly in the context of deep learning.
Point Deformable Network with Enhanced Normal Embedding for Point Cloud Analysis
Additional offsets and modulation scalars are learned on the whole point features, which shift the deformable reference points to the regions of interest.
Automated Measurement of Pericoronary Adipose Tissue Attenuation and Volume in CT Angiography
In this pilot work, we developed a fully automated approach for the measurement of PCAT mean attenuation and volume in the region around both coronary arteries.
Gradient constrained sharpness-aware prompt learning for vision-language models
This paper targets a novel trade-off problem in generalizable prompt learning for vision-language models (VLM), i. e., improving the performance on unseen classes while maintaining the performance on seen classes.
Expert Uncertainty and Severity Aware Chest X-Ray Classification by Multi-Relationship Graph Learning
In this paper, we re-extract disease labels from CXR reports to make them more realistic by considering disease severity and uncertainty in classification.
Expert Knowledge-Aware Image Difference Graph Representation Learning for Difference-Aware Medical Visual Question Answering
Given a pair of main and reference images, this task attempts to answer several questions on both diseases and, more importantly, the differences between them.
Linear Regression on Manifold Structured Data: the Impact of Extrinsic Geometry on Solutions
We assume that the data manifold is smooth and is embedded in a Euclidean space, and our objective is to reveal the impact of the data manifold's extrinsic geometry on the regression.
Unified Multi-View Orthonormal Non-Negative Graph Based Clustering Framework
In this paper, we formulate a novel clustering model, which exploits the non-negative feature property and, more importantly, incorporates the multi-view information into a unified joint learning framework: the unified multi-view orthonormal non-negative graph based clustering framework (Umv-ONGC).
Memory Efficient Temporal & Visual Graph Model for Unsupervised Video Domain Adaptation
Existing video domain adaption (DA) methods need to store all temporal combinations of video frames or pair the source and target videos, which are memory cost expensive and can't scale up to long videos.
Nearest Neighbor Sampling of Point Sets using Rays
We propose a new framework for the sampling, compression, and analysis of distributions of point sets and other geometric objects embedded in Euclidean spaces.
DBRec: Dual-Bridging Recommendation via Discovering Latent Groups
In addition, we propose to jointly learn user-user group (item-item group) hierarchies, so that we can effectively discover latent groups and learn compact user/item representations.
Deep inspection: an electrical distribution pole parts study via deep neural networks
We propose a novel two-stage zoom-in detection method to gradually focus on the object of interest.
What is the Best Way for Extracting Meaningful Attributes from Pictures?
With this metric, automatic quantitative evaluation can be performed on the attribute sets; thus, reducing the enormous effort to perform manual evaluation.
Determining the best attributes for surveillance video keywords generation
In this paper, we propose a novel approach, based on the shared structure exhibited amongst meaningful attributes, that enables us to compare between different automatic attribute discovery approaches. We then validate our approach by comparing various attribute discovery methods such as PiCoDeS on two attribute datasets.
Automatic and Quantitative evaluation of attribute discovery methods
In our evaluation, we gleaned some insights that could be beneficial in developing automatic attribute discovery methods to generate meaningful attributes.
