16 citations · 30 across the 7 of their papers we have counts for
7 papers
Revisiting Image Classifier Training for Improved Certified Robust Defense against Adversarial Patches
Aniruddha Saha, Shuhua Yu, Arash Norouzzadeh +2
Certifiably robust defenses against adversarial patches for image classifiers ensure correct prediction against any changes to a constrained neighborhood of pixels. PatchCleanser a…
Defending Multimodal Fusion Models against Single-Source Adversaries
Karren Yang, Wan-Yi Lin, Manash Barman +2
Beyond achieving high performance across many vision tasks, multimodal models are expected to be robust to single-source faults due to the availability of redundant information bet…
How to augment your ViTs? Consistency loss and StyleAug, a random style transfer augmentation
Akash Umakantha, Joao D. Semedo, S. Alireza Golestaneh +1
The Vision Transformer (ViT) architecture has recently achieved competitive performance across a variety of computer vision tasks. One of the motivations behind ViTs is weaker indu…
Accelerating Road Sign Ground Truth Construction with Knowledge Graph and Machine Learning
Ji Eun Kim, Cory Henson, Kevin Huang +2
Having a comprehensive, high-quality dataset of road sign annotation is critical to the success of AI-based Road Sign Recognition (RSR) systems. In practice, annotators often face…
Crossing You in Style: Cross-modal Style Transfer from Music to Visual Arts
Cheng-Che Lee, Wan-Yi Lin, Yen-Ting Shih +2
Music-to-visual style transfer is a challenging yet important cross-modal learning problem in the practice of creativity. Its major difference from the traditional image style tran…
Noisy Batch Active Learning with Deterministic Annealing
Gaurav Gupta, Anit Kumar Sahu, Wan-Yi Lin
We study the problem of training machine learning models incrementally with batches of samples annotated with noisy oracles. We select each batch of samples that are important and…
DeepBbox: Accelerating Precise Ground Truth Generation for Autonomous Driving Datasets
Govind Rathore, Wan-Yi Lin, Ji Eun Kim
Autonomous driving requires various computer vision algorithms, such as object detection and tracking.Precisely-labeled datasets (i.e., objects are fully contained in bounding boxe…