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Found 14 papers, 3 papers with code
Multiple-Crop Human Mesh Recovery with Contrastive Learning and Camera Consistency in A Single Image
We tackle the problem of single-image Human Mesh Recovery (HMR).
Interleaving One-Class and Weakly-Supervised Models with Adaptive Thresholding for Unsupervised Video Anomaly Detection
In previous work, the two models are closely entangled with each other, and it is not known how to upgrade their method without modifying their training framework significantly.
The Radiation Oncology NLP Database
ROND is specifically designed to address this gap in the domain of radiation oncology, a field that offers many opportunities for NLP exploration.
Quaternion tensor left ring decomposition and application for color image inpainting
Therefore, in this paper, based on the left quaternion matrix multiplication, we propose the quaternion tensor left ring (QTLR) decomposition, which inherits the powerful and generalized representation abilities of the TR decomposition while leveraging the advantages of quaternions for color pixel representation.
Exploring Multimodal Approaches for Alzheimer's Disease Detection Using Patient Speech Transcript and Audio Data
As AD impairs the patient's language understanding and expression ability, the speech of AD patients can serve as an indicator of this disease.
Differentiate ChatGPT-generated and Human-written Medical Texts
We focus on analyzing the differences between medical texts written by human experts and generated by ChatGPT, and designing machine learning workflows to effectively detect and differentiate medical texts generated by ChatGPT.
AugGPT: Leveraging ChatGPT for Text Data Augmentation
Text data augmentation is an effective strategy for overcoming the challenge of limited sample sizes in many natural language processing (NLP) tasks.
Mask-guided BERT for Few Shot Text Classification
The main challenge of FSL is the difficulty of training robust models on small amounts of samples, which frequently leads to overfitting.
Uniform tensor clustering by jointly exploring sample affinities of various orders
Conventional clustering methods based on pairwise affinity usually suffer from the concentration effect while processing huge dimensional features yet low sample sizes data, resulting in inaccuracy to encode the sample proximity and suboptimal performance in clustering.
Coarse-to-fine Knowledge Graph Domain Adaptation based on Distantly-supervised Iterative Training
In this framework, we apply distant-supervision on cross-domain knowledge graph adaptation.
Fine-grained Domain Adaptive Crowd Counting via Point-derived Segmentation
To this end, we propose to untangle \emph{domain-invariant} crowd and \emph{domain-specific} background from crowd images and design a fine-grained domain adaption method for crowd counting.
Spatially-Invariant Style-Codes Controlled Makeup Transfer
In this paper, we take a different perspective to break down the makeup transfer problem into a two-step extraction-assignment process.
Learning Common Harmonic Waves on Stiefel Manifold -- A New Mathematical Approach for Brain Network Analyses
To that end, we propose a novel connectome harmonic analysis framework to provide enhanced mathematical insights by detecting frequency-based alterations relevant to brain disorders.
Integrating Tensor Similarity to Enhance Clustering Performance
The performance of most the clustering methods hinges on the used pairwise affinity, which is usually denoted by a similarity matrix.
