Chun-Ta Lu
Chun-Ta Lu is a Software Engineer at Google Research. Prior to joining Google, he received his PhD from University of Illinois at Chicago. His main research interests span various fields of Computer Vision, Data Mining, Machine Learning.
Authored Publications
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Visual Program Tuning: Training Large Multimodal Models to Reason like Programs
Yushi Hu
Krishna Viswanathan
Kenji Hata
Enming Luo
Ranjay Krishna
Ariel Fuxman
Conference on Computer Vision and Pattern Recognition (2024)
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Solving complex visual tasks (e.g., “Who invented the musical instrument on the right?”) involves back-and-forth between visual processing and reasoning. Visual programming is a recent multimodal framework that has shown promise in conducting visual reasoning in an interpretable and compositional manner. However, this framework is error-prone—it can lead to a wrong answer whenever the program itself is wrong, or when any of the steps of the program are solved incorrectly, thus leading to worse overall performance than end-to-end systems trained with labeled data. Moreover, it is inefficient to involve multiple steps (i.e., generating and then running programs) during inference. Ideally, a single large multimodal model (LMM) should directly conduct similar reasoning and yield the correct answer.
In this work, we propose Visual Program Tuning (VPT), which leverages visual programs for teaching LLMs to reason via instruction tuning. VPT rewrites the execution traces of visual programs as chain-of-thought reasoning steps, and tunes an LMM to output not only the label but its reasoning as well. Extensive experiments on complex vision tasks show that models trained with VPT achieve state-of-the-art accuracy while being able to produce interpretable and faithful reasoning steps. PaLI-X + VPT outperforms all existing LMMs on a wide range of visual tasks, improving performance on counting, spatial relations, and compositional reasoning tasks. VPT is also helpful for quick adaptation on new tasks. Our experiments on content moderation show that fine-tuning LMMs with program-augmented examples is more sample efficient than traditional supervised training.
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Scaling Up LLM Reviews for Google Ads Content Moderation
Ariel Fuxman
Chih-Chun Chia
Dongjin Kwon
Enming Luo
Mehmet Tek
Ranjay Krishna
Tiantian Fang
Tushar Dogra
Yu-Han Lyu
(2024)
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Large language models (LLMs) are powerful tools for content moderation but LLM inference costs and latency on large volumes of data, such as the Google Ads repository, are prohibitive for their casual usage. This study is focused on scaling up LLM reviews for content moderation in Google Ads. First, we use heuristics to select candidates via filtering and duplicate removal, and create clusters of ads for which we select one representative ad per cluster. Then, LLMs are used to review only the representative ads. Finally we propagate the LLM decisions for representative ads back to their clusters. This method reduces the number of reviews by more than 3 orders of magnitude while achieving a 2x recall compared to a non-LLM model as a baseline. Note that, the success of this approach is a strong function of the representations used in clustering and label propagation; we observed that cross-modal similarity representations yield better results than uni-modal representations.
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Benchmarking Robustness to Adversarial Image Obfuscations
Florian Stimberg
Hussein Hazimeh
Yintao Liu
Merve Kaya
Ariel Fuxman
Mehmet Tek
Advances in Neural Information Processing Systems (2023)
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Automated content filtering and moderation is an important tool that allows online platforms to build striving user communities that facilitate cooperation and prevent abuse. Unfortunately, resourceful actors try to bypass automated filters in a bid to post content that violate platform policies and codes of conduct. To reach this goal, these malicious actors obfuscate policy violating content to prevent machine learning models from reaching the correct decision. In this paper, we invite researchers to tackle this specific issue and present a new image benchmark. This benchmark, based on ImageNet, simulates the type of obfuscations created by malicious actors. It goes beyond ImageNet-C and ImageNet-C-Bar by proposing general, drastic, adversarial modifications that preserve the original content intent. It aims to tackle a more common adversarial threat than the one considered by Lp-norm bounded adversaries. Our hope is that this benchmark will encourage researchers to test their models and methods and try to find new approaches that are more robust to these obfuscations.
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Neural Structured Learning in TensorFlow: Hands-On Tutorial at KDD
Chun-Sung Ferng
George Yu
(2020), pp. 3501-3502
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We present Neural Structured Learning (NSL) in TensorFlow, a new learning paradigm to train neural networks by leveraging structured signals in addition to feature inputs. Structure can be explicit as represented by a graph, or implicit, either induced by adversarial perturbation or inferred using techniques like embedding learning. NSL is open-sourced as part of the TensorFlow ecosystem and is widely used in Google across many products and services. In this tutorial, we provide an overview of the NSL framework including various libraries, tools, and APIs as well as demonstrate the practical use of NSL in different applications. The NSL website is hosted at www.tensorflow.org/neural_structured_learning, which includes details about the theoretical foundations of the technology, extensive API documentation, and hands-on tutorials.
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Graph-RISE: Graph-Regularized Image Semantic Embedding
Aleksei Timofeev
Futang Peng
Krishnamurthy Viswanathan
Lucy Gao
Sujith Ravi
Yi-ting Chen
Zhen Li
The 12th International Conference on Web Search and Data Mining (2020) (to appear)
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Learning image representation to capture instance-based semantics has been a challenging and important task for enabling many applications such as image search and clustering. In this paper, we explore the limits of image embedding learning at unprecedented scale and granularity. We present Graph-RISE, an image embedding that captures very fine-grained, instance-level semantics. Graph-RISE is learned via a large-scale, neural graph learning framework that leverages graph structure to regularize the training of deep neural networks. To the best of our knowledge, this is the first work that can capture instance-level image semantics at million—O(40M)—scale. Experimental results show that Graph-RISE outperforms state-of-the-art image embedding algorithms on several evaluation tasks, including image classification and triplet ranking. We also provide case studies to demonstrate that, qualitatively, image retrieval based on Graph-RISE well captures the semantics and differentiates nuances at instance level.
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Inferring Context from Pixels for Multimodal Image Classification
Manan Shah
Krishnamurthy Viswanathan
Ariel Fuxman
Zhen Li
Aleksei Timofeev
Chao Jia
Proceedings of the 28th ACM International Conference on Information and Knowledge Management, ACM (2019) (to appear)
Preview abstract
Image classification models take image pixels as input and predict labels in a predefined taxonomy. While contextual information (e.g. text surrounding an image) can provide valuable orthogonal signals to improve classification, the typical setting in literature assumes the unavailability of text and thus focuses on models that rely purely on pixels. In this work, we also focus on the setting where only pixels are available in the input. However, we demonstrate that if we predict textual information from pixels, we can subsequently use the predicted text to train models that improve overall performance.
We propose a framework that consists of two main components: (1) a phrase generator that maps image pixels to a contextual phrase, and (2) a multimodal model that uses textual features from the phrase generator and visual features from the image pixels to produce labels in the output taxonomy. The phrase generator is trained using web-based query-image pairs to incorporate contextual information associated with each image and has a large output space.
We evaluate our framework on diverse benchmark datasets (specifically, the WebVision dataset for evaluating multi-class classification and OpenImages dataset for evaluating multi-label classification), demonstrating performance improvements over approaches based exclusively on pixels and showcasing benefits in prediction interpretability. We additionally present results to demonstrate that our framework provides improvements in few-shot learning of minimally labeled concepts. We further demonstrate the unique benefits of the multimodal nature of our framework by utilizing intermediate image/text co-embeddings to perform baseline zero-shot learning on the ImageNet dataset.
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