Search Results for author:
Found 22 papers, 11 papers with code
Bigger Data or Fairer Data? Augmenting BERT via Active Sampling for Educational Text Classification
Pretrained Language Models (PLMs), though popular, have been diagnosed to encode bias against protected groups in the representations they learn, which may harm the prediction fairness of downstream models.
Mammo-CLIP: Leveraging Contrastive Language-Image Pre-training (CLIP) for Enhanced Breast Cancer Diagnosis with Multi-view Mammography
For framework evaluation, we assembled two datasets retrospectively.
Edit One for All: Interactive Batch Image Editing
With increased human control, it is now possible to edit an image in a plethora of ways; from specifying in text what we want to change, to straight up dragging the contents of the image in an interactive point-based manner.
Improved Baselines with Visual Instruction Tuning
Large multimodal models (LMM) have recently shown encouraging progress with visual instruction tuning.
Ranked #3 on
visual instruction following
on LLaVA-Bench
Factual Inconsistency Detection in Chart Captioning
visual instruction following
+1
Visual Instruction Inversion: Image Editing via Visual Prompting
Given pairs of example that represent the "before" and "after" images of an edit, our goal is to learn a text-based editing direction that can be used to perform the same edit on new images.
Towards Automatic Boundary Detection for Human-AI Collaborative Hybrid Essay in Education
Then we proposed a two-step approach where we (1) separated AI-generated content from human-written content during the encoder training process; and (2) calculated the distances between every two adjacent prototypes and assumed that the boundaries exist between the two adjacent prototypes that have the furthest distance from each other.
Generate Anything Anywhere in Any Scene
Text-to-image diffusion models have attracted considerable interest due to their wide applicability across diverse fields.
Leveraging Large Language Models for Scalable Vector Graphics-Driven Image Understanding
By leveraging the XML-based textual descriptions of SVG representations instead of raster images, we aim to bridge the gap between the visual and textual modalities, allowing LLMs to directly understand and manipulate images without the need for parameterized visual components.
Synthetic CT Generation from MRI using 3D Transformer-based Denoising Diffusion Model
The proposed model consists of two processes: a forward process which adds Gaussian noise to real CT scans, and a reverse process in which a shifted-window transformer V-net (Swin-Vnet) denoises the noisy CT scans conditioned on the MRI from the same patient to produce noise-free CT scans.
BreastSAM: A Study of Segment Anything Model for Breast Tumor Detection in Ultrasound Images
We conducted a thorough investigation of the Segment Anything Model (SAM) for the task of interactive segmentation of breast tumors in ultrasound images.
Cross-Shaped Windows Transformer with Self-supervised Pretraining for Clinically Significant Prostate Cancer Detection in Bi-parametric MRI
We introduce a novel end-to-end Cross-Shaped windows (CSwin) transformer UNet model, CSwin UNet, to detect clinically significant prostate cancer (csPCa) in prostate bi-parametric MR imaging (bpMRI) and demonstrate the effectiveness of our proposed self-supervised pre-training framework.
Polyp-SAM: Transfer SAM for Polyp Segmentation
In this study, we propose Poly-SAM, a finetuned SAM model for polyp segmentation, and compare its performance to several state-of-the-art polyp segmentation models.
SkinSAM: Empowering Skin Cancer Segmentation with Segment Anything Model
Skin cancer is a prevalent and potentially fatal disease that requires accurate and efficient diagnosis and treatment.
Advancing Medical Imaging with Language Models: A Journey from N-grams to ChatGPT
In this paper, we aimed to provide a review and tutorial for researchers in the field of medical imaging using language models to improve their tasks at hand.
Practical and Ethical Challenges of Large Language Models in Education: A Systematic Scoping Review
Educational technology innovations leveraging large language models (LLMs) have shown the potential to automate the laborious process of generating and analysing textual content.
Towards Universal Fake Image Detectors that Generalize Across Generative Models
In this work, we first show that the existing paradigm, which consists of training a deep network for real-vs-fake classification, fails to detect fake images from newer breeds of generative models when trained to detect GAN fake images.
GLIGEN: Open-Set Grounded Text-to-Image Generation
Large-scale text-to-image diffusion models have made amazing advances.
Ranked #4 on
Conditional Text-to-Image Synthesis
on COCO-MIG
Contrastive Learning for Diverse Disentangled Foreground Generation
We introduce a new method for diverse foreground generation with explicit control over various factors.
GIRAFFE HD: A High-Resolution 3D-aware Generative Model
3D-aware generative models have shown that the introduction of 3D information can lead to more controllable image generation.
Collaging Class-specific GANs for Semantic Image Synthesis
We propose a new approach for high resolution semantic image synthesis.
CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakes
Then, we design a two-level perturbation fusion strategy to alleviate the conflict between the adversarial watermarks generated by different facial images and models.
MixNMatch: Multifactor Disentanglement and Encoding for Conditional Image Generation
We present MixNMatch, a conditional generative model that learns to disentangle and encode background, object pose, shape, and texture from real images with minimal supervision, for mix-and-match image generation.
