Search Results for author:
Found 20 papers, 6 papers with code
Neuro-symbolic Learning Yielding Logical Constraints
Neuro-symbolic systems combine the abilities of neural perception and logical reasoning.
Revisiting Multi-Modal LLM Evaluation
With the advent of multi-modal large language models (MLLMs), datasets used for visual question answering (VQA) and referring expression comprehension have seen a resurgence.
EMBANet: A Flexible Efffcient Multi-branch Attention Network
The proposed MBC module brings new degrees of freedom (DoF) for the design of attention networks by allowing the type of transformation operators and the number of branches to be flexibly adjusted.
Orthogonal Constrained Minimization with Tensor $\ell_{2,p}$ Regularization for HSI Denoising and Destriping
The model of the proposed NLTL2p approach is built based on a new sparsity-enhanced Nonlocal Low-rank Tensor regularization and a tensor $\ell_{2, p}$ norm with $p\in(0, 1)$.
Capabilities of Gemini Models in Medicine
We evaluate Med-Gemini on 14 medical benchmarks, establishing new state-of-the-art (SoTA) performance on 10 of them, and surpass the GPT-4 model family on every benchmark where a direct comparison is viable, often by a wide margin.
Ranked #1 on
Question Answering
on MedQA
(using extra training data)
LE-SSL-MOS: Self-Supervised Learning MOS Prediction with Listener Enhancement
In this paper, we proposed a novel fusion model for MOS prediction that combines supervised and unsupervised approaches.
OSRE: Object-to-Spot Rotation Estimation for Bike Parking Assessment
However, estimating object rotation with respect to other visual objects in the visual context of an input image still lacks deep studies due to the unavailability of object datasets with rotation annotations.
Learnable Irrelevant Modality Dropout for Multimodal Action Recognition on Modality-Specific Annotated Videos
However, effectively leveraging the audio modality in vision-specific annotated videos for action recognition is of particular challenge.
A three-dimensional dual-domain deep network for high-pitch and sparse helical CT reconstruction
By embedding our implementation into the network, we propose an end-to-end deep network for the high pitch helical CT reconstruction with sparse detectors.
EPSANet: An Efficient Pyramid Squeeze Attention Block on Convolutional Neural Network
Recently, it has been demonstrated that the performance of a deep convolutional neural network can be effectively improved by embedding an attention module into it.
Robust Graph Learning Under Wasserstein Uncertainty
To this end, we propose a graph learning framework using Wasserstein distributionally robust optimization (WDRO) which handles uncertainty in data by defining an uncertainty set on distributions of the observed data.
Expansions in multiple bases over general alphabets
Expansions in non-integer bases have been extensively investigated since a pioneering work of R\'enyi.
Combinatorics Number Theory 11A63 (Primary) 37B10 (Secondary)
Liquefaction-induced Plasticity from Entropy-boosted Amorphous Ceramics
Ceramics are easy to break, and very few generic mechanisms are available for improving their mechanical properties, e. g., the 1975-discovered anti-fracture mechanism is strictly limited to zirconia and hafnia.
Materials Science
A New Weighting Scheme for Fan-beam and Circle Cone-beam CT Reconstructions
In this paper, we first present an arc based algorithm for fan-beam computed tomography (CT) reconstruction via applying Katsevich's helical CT formula to 2D fan-beam CT reconstruction.
Multi-scale Receptive Fields Graph Attention Network for Point Cloud Classification
The proposed MRFGAT architecture is tested on ModelNet10 and ModelNet40 datasets, and results show it achieves state-of-the-art performance in shape classification tasks.
Analysis of Adaptive Synchrosqueezing Transform with a Time-varying Parameter
The WSST with a time-varying parameter, called the adaptive WSST, was introduced very recently in the paper "Adaptive synchrosqueezing transform with a time-varying parameter for non-stationary signal separation".
A model-guided deep network for limited-angle computed tomography
In this paper, we first propose a variational model for the limited-angle computed tomography (CT) image reconstruction and then convert the model into an end-to-end deep network. We use the penalty method to solve the model and divide it into three iterative subproblems, where the first subproblem completes the sinograms by utilizing the prior information of sinograms in the frequency domain and the second refines the CT images by using the prior information of CT images in the spatial domain, and the last merges the outputs of the first two subproblems.
A deep network for sinogram and CT image reconstruction
A CT image can be well reconstructed when the sampling rate of the sinogram satisfies the Nyquist criteria and the sampled signal is noise-free.
HBE: Hand Branch Ensemble Network for Real-time 3D Hand Pose Estimation
The structural design inspiration of the HBE network comes from the understanding of the differences in the functional importance of different fingers.
Want a Good Answer? Ask a Good Question First!
Community Question Answering (CQA) websites have become valuable repositories which host a massive volume of human knowledge.
