Search Results for author:
Found 11 papers, 5 papers with code
Learning Probabilistic Ordinal Embeddings for Uncertainty-Aware Regression
An ordinal distribution constraint is proposed to exploit the ordinal nature of regression.
Ranked #2 on
Age Estimation
on Adience
Aesthetics Quality Assessment
Age And Gender Classification
+3
OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal Regression
In this paper, we propose to learn the rank concepts from the rich semantic CLIP latent space.
Ranked #1 on
Few-shot Age Estimation
on MORPH Album2
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.
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.
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.
Efficient Meshy Neural Fields for Animatable Human Avatars
Moreover, only minutes of optimization is enough for plausible reconstruction results.
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.
Predicting Token Impact Towards Efficient Vision Transformer
Token filtering to reduce irrelevant tokens prior to self-attention is a straightforward way to enable efficient vision Transformer.
Efficient Text-Guided 3D-Aware Portrait Generation with Score Distillation Sampling on Distribution
These elaborated designs enable our model to generate portraits with robust multi-view semantic consistency, eliminating the need for optimization-based methods.
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.
Segment and Caption Anything
We propose a method to efficiently equip the Segment Anything Model (SAM) with the ability to generate regional captions.
