activity
20212026
most citedScaling Up LLM Reviews for Google Ads Content Moderation

13 citations · 20 across the 11 of their papers we have counts for

collaborators

11 papers

cs.AI2026

Automatic Hard Example Synthesis with Multi-Level Agentic Data Curation

Genglin Liu, Muye Zhang, Krishnamurthy Viswanathan +5

Multimodal Large Language Models (MLLMs) are increasingly deployed for nuanced content safety and moderation tasks, yet they remain vulnerable to adversarial attacks and out-of-dis…

cs.AI2025

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…

cs.CV2024

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…

cs.LG2024★ 1 cited

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…

cs.CV2024★ 1 cited

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…

cs.IR2024★ 13 cited

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…

cs.CV2023★ 1 cited

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…

cs.CV2023★ 1 cited

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"…

cs.LG2023★ 1 cited

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…

cs.CV2023★ 2 cited

Benchmarking Robustness to Adversarial Image Obfuscations

Florian Stimberg, Ayan Chakrabarti, Chun-Ta Lu +9

Automated content filtering and moderation is an important tool that allows online platforms to build striving user communities that facilitate cooperation and prevent abuse. Unfor…

cs.LG2021

CARLS: Cross-platform Asynchronous Representation Learning System

Chun-Ta Lu, Yun Zeng, Da-Cheng Juan +13

In this work, we propose CARLS, a novel framework for augmenting the capacity of existing deep learning frameworks by enabling multiple components -- model trainers, knowledge make…