105 citations · 186 across the 11 of their papers we have counts for
12 papers
Agile Deliberation: Concept Deliberation for Subjective Visual Classification
Leijie Wang, Otilia Stretcu, Wei Qiao +7
From content moderation to content curation, applications requiring vision classifiers for visual concepts are rapidly expanding. Existing human-in-the-loop approaches typically as…
Zero-Shot Image Moderation in Google Ads with LLM-Assisted Textual Descriptions and Cross-modal Co-embeddings
Enming Luo, Wei Qiao, Katie Warren +7
We present a scalable and agile approach for ads image content moderation at Google, addressing the challenges of moderating massive volumes of ads with diverse content and evolvin…
A Implies B: Circuit Analysis in LLMs for Propositional Logical Reasoning
Guan Zhe Hong, Nishanth Dikkala, Enming Luo +3
Due to the size and complexity of modern large language models (LLMs), it has proven challenging to uncover the underlying mechanisms that models use to solve reasoning problems. F…
Why Fine-grained Labels in Pretraining Benefit Generalization?
Guan Zhe Hong, Yin Cui, Ariel Fuxman +2
Recent studies show that pretraining a deep neural network with fine-grained labeled data, followed by fine-tuning on coarse-labeled data for downstream tasks, often yields better…
Modeling Collaborator: Enabling Subjective Vision Classification With Minimal Human Effort via LLM Tool-Use
Imad Eddine Toubal, Aditya Avinash, Neil Gordon Alldrin +10
From content moderation to wildlife conservation, the number of applications that require models to recognize nuanced or subjective visual concepts is growing. Traditionally, devel…
Scaling Up LLM Reviews for Google Ads Content Moderation
Wei Qiao, Tushar Dogra, Otilia Stretcu +11
Large language models (LLMs) are powerful tools for content moderation, but their inference costs and latency make them prohibitive for casual use on large datasets, such as the Go…
Visual Program Distillation: Distilling Tools and Programmatic Reasoning into Vision-Language Models
Yushi Hu, Otilia Stretcu, Chun-Ta Lu +5
Solving complex visual tasks such as "Who invented the musical instrument on the right?" involves a composition of skills: understanding space, recognizing instruments, and also re…
Towards Understanding the Effect of Pretraining Label Granularity
Guan Zhe Hong, Yin Cui, Ariel Fuxman +2
In this paper, we study how the granularity of pretraining labels affects the generalization of deep neural networks in image classification tasks. We focus on the "fine-to-coarse"…
Agile Modeling: From Concept to Classifier in Minutes
Otilia Stretcu, Edward Vendrow, Kenji Hata +15
The application of computer vision to nuanced subjective use cases is growing. While crowdsourcing has served the vision community well for most objective tasks (such as labeling a…
NoiseRank: Unsupervised Label Noise Reduction with Dependence Models
Karishma Sharma, Pinar Donmez, Enming Luo +2
Label noise is increasingly prevalent in datasets acquired from noisy channels. Existing approaches that detect and remove label noise generally rely on some form of supervision, w…
Adaptive Image Denoising by Mixture Adaptation
Enming Luo, Stanley H. Chan, Truong Q. Nguyen
We propose an adaptive learning procedure to learn patch-based image priors for image denoising. The new algorithm, called the Expectation-Maximization (EM) adaptation, takes a gen…
Adaptive Image Denoising by Targeted Databases
Enming Luo, Stanley H. Chan, Truong Q. Nguyen
We propose a data-dependent denoising procedure to restore noisy images. Different from existing denoising algorithms which search for patches from either the noisy image or a gene…