Search Results for author:
Found 17 papers, 8 papers with code
ClickDiffusion: Harnessing LLMs for Interactive Precise Image Editing
We demonstrate that by serializing both an image and a multi-modal instruction into a textual representation it is possible to leverage LLMs to perform precise transformations of the layout and appearance of an image.
LLM Attributor: Interactive Visual Attribution for LLM Generation
Our library offers a new way to quickly attribute an LLM's text generation to training data points to inspect model behaviors, enhance its trustworthiness, and compare model-generated text with user-provided text.
UniTable: Towards a Unified Framework for Table Structure Recognition via Self-Supervised Pretraining
Tables convey factual and quantitative data with implicit conventions created by humans that are often challenging for machines to parse.
Self-Supervised Pre-Training for Table Structure Recognition Transformer
We discover that the performance gap between the linear projection transformer and the hybrid CNN-transformer can be mitigated by SSP of the visual encoder in the TSR model.
How Much is Unseen Depends Chiefly on Information About the Seen
In our experiments, our genetic algorithm discovers estimators that have a substantially smaller MSE than the state-of-the-art Good-Turing estimator.
Point and Instruct: Enabling Precise Image Editing by Unifying Direct Manipulation and Text Instructions
This allows users to benefit from both the visual descriptiveness of natural language and the spatial precision of direct manipulation.
Mobile Fitting Room: On-device Virtual Try-on via Diffusion Models
The growing digital landscape of fashion e-commerce calls for interactive and user-friendly interfaces for virtually trying on clothes.
High-Performance Transformers for Table Structure Recognition Need Early Convolutions
This allows it to "see" an appropriate portion of the table and "store" the complex table structure within sufficient context length for the subsequent transformer.
Ranked #3 on
Table Recognition
on PubTabNet
SuperNOVA: Design Strategies and Opportunities for Interactive Visualization in Computational Notebooks
Through this study, we identify key design implications and trade-offs, such as leveraging multimodal data in notebooks as well as balancing the degree of visualization-notebook integration.
Diffusion Explainer: Visual Explanation for Text-to-image Stable Diffusion
Diffusion Explainer tightly integrates a visual overview of Stable Diffusion's complex components with detailed explanations of their underlying operations, enabling users to fluidly transition between multiple levels of abstraction through animations and interactive elements.
VisCUIT: Visual Auditor for Bias in CNN Image Classifier
CNN image classifiers are widely used, thanks to their efficiency and accuracy.
Concept Evolution in Deep Learning Training: A Unified Interpretation Framework and Discoveries
We present ConceptEvo, a unified interpretation framework for deep neural networks (DNNs) that reveals the inception and evolution of learned concepts during training.
Multi-EPL: Accurate Multi-source Domain Adaptation
Given multiple source datasets with labels, how can we train a target model with no labeled data?
Ensemble Multi-Source Domain Adaptation with Pseudolabels
Multi-source domain adaptation (MSDA) aims to train a model using multiple source datasets different from a target dataset in the absence of target data labels.
Genetic Improvement @ ICSE 2020
Following Prof. Mark Harman of Facebook's keynote and formal presentations (which are recorded in the proceedings) there was a wide ranging discussion at the eighth international Genetic Improvement workshop, GI-2020 @ ICSE (held as part of the 42nd ACM/IEEE International Conference on Software Engineering on Friday 3rd July 2020).
KNU-HYUNDAI's NMT system for Scientific Paper and Patent Tasks onWAT 2019
In this paper, we describe the neural machine translation (NMT) system submitted by the Kangwon National University and HYUNDAI (KNU-HYUNDAI) team to the translation tasks of the 6th workshop on Asian Translation (WAT 2019).
