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Found 9 papers, 2 papers with code
Modeling Collaborator: Enabling Subjective Vision Classification With Minimal Human Effort via LLM Tool-Use
Our framework leverages recent advances in foundation models, both large language models and vision-language models, to carve out the concept space through conversation and by automatically labeling training data points.
Scaling Up LLM Reviews for Google Ads Content Moderation
This study proposes a method for scaling up LLM reviews for content moderation in Google Ads.
Visual Program Distillation: Distilling Tools and Programmatic Reasoning into Vision-Language Models
We propose Visual Program Distillation (VPD), an instruction tuning framework that produces a vision-language model (VLM) capable of solving complex visual tasks with a single forward pass.
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Towards Understanding the Effect of Pretraining Label Granularity
Furthermore, we perform comprehensive experiments using the label hierarchies of iNaturalist 2021 and observe that the following conditions, in addition to proper choice of label granularity, enable the transfer to work well in practice: 1) the pretraining dataset needs to have a meaningful label hierarchy, and 2) the pretraining and target label functions need to align well.
Agile Modeling: From Concept to Classifier in Minutes
In reaction, we introduce the problem of Agile Modeling: the process of turning any subjective visual concept into a computer vision model through a real-time user-in-the-loop interactions.
Benchmarking Robustness to Adversarial Image Obfuscations
We evaluate 33 pretrained models on the benchmark and train models with different augmentations, architectures and training methods on subsets of the obfuscations to measure generalization.
CARLS: Cross-platform Asynchronous Representation Learning System
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 makers and knowledge banks -- to concertedly work together in an asynchronous fashion across hardware platforms.
Guess Me if You Can: Acronym Disambiguation for Enterprises
The framework takes the enterprise corpus as input and produces a high-quality acronym disambiguation system as output.
