Search Results for author:
Found 6 papers, 2 papers with code
Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Data
We curate a benchmark comprising 22 multimodal datasets from diverse real-world applications, encompassing all 4 combinations of the 3 modalities.
AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models
AutoGluon-Multimodal (AutoMM) is introduced as an open-source AutoML library designed specifically for multimodal learning.
Convolution Meets LoRA: Parameter Efficient Finetuning for Segment Anything Model
The Segment Anything Model (SAM) stands as a foundational framework for image segmentation.
UniBoost: Unsupervised Unimodal Pre-training for Boosting Zero-shot Vision-Language Tasks
Our thorough studies validate that models pre-trained as such can learn rich representations of both modalities, improving their ability to understand how images and text relate to each other.
Tailoring Instructions to Student's Learning Levels Boosts Knowledge Distillation
It has been commonly observed that a teacher model with superior performance does not necessarily result in a stronger student, highlighting a discrepancy between current teacher training practices and effective knowledge transfer.
Towards Arbitrary Text-driven Image Manipulation via Space Alignment
Then, the text input can be directly accessed into the StyleGAN space and be used to find the semantic shift according to the text description.
