Synthetic Data for CV

Event: Synthetic Data for Computer Vision Workshop - CVPR 2024 · Duration: 296 min · ▶ Watch on YouTube

Abstract

This video segment begins with a Q&A session following Ming C. Lin’s talk, where she discusses the critical role of synthetic data in data-scarce domains, techniques like diffusion models to mitigate artifacts, and the necessity of real-world testing for models trained on synthetic data. Her subsequent presentation, “Simulated Data for Visual Learning,” delves into the data-driven revolution, outlining a vision for an ultimate metaverse built upon capturing and reconstructing the real world. She showcases the application of simulated data in diverse areas such as autonomous driving, virtual try-on for garments, and enhancing model robustness and generalization by addressing data and technical challenges. The segment concludes with the introduction of Yannis Kalantidis’s talk, “Improving the Generalization of Visual Representations using Generative Models,” which aims to explore how generative models can be leveraged to create visual encoders that generalize more effectively across unseen classes, domains, and tasks. This segment features two talks on synthetic data and multimodal datasets. The first talk explores using synthetic data generated by Stable Diffusion to improve model generalization, demonstrating that while synthetic data may not close the performance gap on training tasks compared to real data, it can lead to superior transfer learning performance. It also introduces a method for weatherproofing visual localization models using generative AI and geometric consistency. The second talk delves into the creation and impact of large-scale public image-text datasets like LAION-5B and DataComp, highlighting their role in advancing multimodal learning. It discusses the challenges of dataset creation, filtering techniques, and the performance of CLIP models trained on these datasets, ultimately showcasing how data-centric approaches can lead to state-of-the-art results and significant compute efficiency. This segment explores the growing importance of synthetic data in AI model development, highlighting its role in addressing challenges related to data variability, distribution, and labeling. The speaker introduces various synthetic data generation engines, including photo-realistic rendering techniques like NeRF and 3D Gaussian Splatting, as well as simulators and large language models. A key focus is on Neural-Sim, a method for learning to generate training data on demand using NeRF, which optimizes rendering parameters to improve model performance on target domains. The discussion also covers the benefits of synthetic data in few-shot learning scenarios and its potential for real-world applications like plant health monitoring. This segment begins with a discussion on the use of synthetic data for object detection, activity understanding, and causality research, highlighting the challenges of obtaining extensive and high-quality real-world annotations. It then transitions to a presentation by Jia Deng from Princeton University, focusing on achieving an ‘ImageNet moment’ for synthetic data. The talk introduces Infinigen, a 100% procedural 3D scene generator that creates unlimited, randomized, automatic, and controllable variations of natural and indoor scenes. Infinigen aims to address the limitations of existing synthetic datasets and the difficulty of acquiring 3D ground truth from real-world data, offering a free, open-source solution for generating diverse and high-quality labeled data for various computer vision applications.

Speakers

  • Ming C. Lin — University of Maryland at College Park, Amazon, University of North Carolina at Chapel Hill
  • Yannis Kalantidis — NAVER LABS Europe
  • Shobhita S Sundaram — Massachusetts Institute of Technology
  • Ludwig Schmidt — University of Washington
  • Yale Song — FAIR at Meta
  • Jia Deng — Princeton University

Talks (6)

  • 00:00:00 — Ming C. Lin: (Implicit) Q&A on Simulated Data for Visual Learning
    • A Q&A session discussing the value of synthetic data in data-scarce domains, handling artifacts with diffusion models, and the importance of testing synthetic data-trained models on real data, especially for dynamic benchmark generation.
  • 01:01:54 — Yannis Kalantidis: Improving the Generalization of Visual Representations using Generative Models
    • This talk introduces how generative models can be leveraged to learn visual encoders that generalize better, specifically focusing on improving robustness to unseen classes, domains, and test-time distribution shifts.
  • 01:13:59 — Yannis Kalantidis: Synthetic data for improving generalization
    • This talk explores how synthetic data generated by Stable Diffusion can be used to improve model generalization, particularly for transfer learning, and introduces methods for weatherproofing visual localization models using generative AI and geometric consistency.
  • 01:44:59 — Ludwig Schmidt: LAION-5B & DataComp: In search of the next generation of multimodal datasets
    • This talk discusses the creation and impact of large-scale public image-text datasets like LAION-5B and DataComp, highlighting their role in advancing multimodal learning, and showcasing how data-centric approaches can lead to state-of-the-art results and significant compute efficiency.
  • 02:27:59 — Yale Song: Synthetic Data for Efficient Model Development
    • This talk introduces the concept and benefits of synthetic data for AI model development, highlighting its role in addressing data challenges and showcasing Neural-Sim for on-demand data generation using NeRF.
  • 04:16:03 — Jia Deng: Toward an ImageNet Moment for Synthetic Data
    • This talk introduces Infinigen, a procedural 3D scene generator, aiming to provide unlimited, high-quality synthetic data for computer vision tasks, especially 3D vision, by leveraging old-fashioned computer graphics.

Key Takeaways

  • Synthetic data is invaluable in data-scarce domains and can power dynamic benchmark generation engines, but models trained on synthetic data must always be tested on real data to ensure real-world applicability.
  • Simulated data can be used to generate large, labeled datasets with ground-truth parameters, enabling the creation of digital twins and virtual environments for training and testing complex systems like autonomous vehicles and virtual try-on.
  • Advanced simulation techniques, including physics-based simulations and multi-agent systems, allow for the generation of data for hard-to-capture scenarios (e.g., accidents, extreme body shapes, diverse fabric materials) that are crucial for improving model robustness and generalization.
  • Combining simulated data with real-world data and employing techniques like transfer learning and adversarial training can significantly enhance the performance and robustness of models, especially when dealing with domain shifts and unseen scenarios.
  • Synthetic data can significantly improve transfer learning performance, even if it doesn’t fully match real data performance on the original training task.
  • Generative AI models can be leveraged to create diverse synthetic data for specific tasks like visual localization, including simulating various environmental conditions.
  • Geometric consistency checks can be used to automatically verify the quality of generated synthetic data, ensuring it retains crucial characteristics for the task.
  • Large-scale public image-text datasets like LAION-5B and DataComp are crucial for advancing multimodal learning, enabling the training of state-of-the-art models and fostering data-centric research.
  • Synthetic data offers significant advantages in AI model development by providing control over data variability, ensuring balanced distributions, and generating clean, rich labels without privacy concerns.
  • Neural-Sim leverages NeRF and bi-level optimization to generate training data on demand, specifically tailored to optimize model performance on target domains, demonstrating improved accuracy in few-shot learning.
  • The use of synthetic data is particularly beneficial in scenarios with limited real-world data, such as few-shot learning or novel concept detection, and can be combined with classical augmentation techniques for enhanced generalization.
  • The development of advanced synthetic data engines, including photo-realistic renderers and simulators, is crucial for creating diverse and relevant datasets that can match specific target domain distributions.
  • Synthetic data, particularly from procedural 3D scene generators like Infinigen, offers a promising solution to the challenges of acquiring large quantities of high-quality, labeled 3D data for computer vision tasks.
  • Infinigen leverages 100% procedural generation based on mathematical rules, allowing for unlimited, randomized, automatic, and controllable variations of scenes and objects, providing fine-grained ground truth information.
  • The compositional nature of Infinigen’s generation system enables exponential growth in complexity and diversity, potentially surpassing the linear growth of traditional datasets like ImageNet.
  • The project addresses the domain gap challenge by aiming for high photo-realism and offering customizable distributions, while also exploring applications in structured indoor environments using a constraint-based system.

Methods / Models / Datasets Mentioned

  • 3D Gaussian Splatting
  • 3D-GPT
  • AI2THOR
  • ARCSim
  • AdamW
  • AlexNet
  • AugMix
  • Auto-encoder
  • Blender
  • Blender Geometry Nodes
  • C4
  • CARLA
  • CC12M
  • CC3M
  • CLIP VIT-B/32
  • CLIPA-v2
  • CMU MoCap
  • CNN-LSTM architecture
  • COCO
  • D-RFCN + SNIP
  • DBO-ft
  • DCLM-Baseline
  • DINO
  • Data Filtering Networks (DFN)
  • DataComp
  • DataComp-1B
  • DataComp-LM
  • Deep Neural Network (DNN)
  • DeepSeek
  • DeiT-III
  • Diffusion models
  • Dolma v1
  • DyHead
  • Fake it till you make it [Wood et al., ICCV 2021]
  • Falcon-7B
  • FallingThings
  • Fast-RCNN
  • Faster R-CNN
  • FineWeb edu
  • FocalNet-H (DINO)
  • GPT-4o
  • GaussianAvatars
  • Gemini
  • Gemma-7B
  • GraphConv
  • Gumbel-Softmax
  • HOW
  • HR-VS
  • Habitat 3.0
  • ImageNet
  • ImageNet-100-SD
  • ImageNet-1K
  • ImageNet-1K-SD
  • ImageNet-SD
  • InMaP
  • InStereo2K
  • InfoNCE
  • InstructPix2Pix
  • Kinect
  • LAION-5B
  • LLM360
  • LaViLa
  • Lidar
  • LightGlue
  • Llama1-7B
  • Llama2-7B
  • Llama3-8B
  • MAP-Neo-7B
  • MS-COCO
  • Mask R-CNN
  • Middlebury Stereo
  • Mistral AI
  • Mistral-7B-v0.3
  • NAS-FPN
  • NeRF
  • Neural Radiance Fields (NeRF)
  • Neural-Sim
  • OLMo-1.7-7B
  • OLMo-1B
  • OLMo-7B
  • Objaverse-XL
  • ObjectNet
  • Phi-3
  • Places 365 dataset
  • RAFT
  • RAFT-Stereo
  • RedCaps
  • RedPajama
  • RefinedWeb
  • Ret4Loc
  • RetinaNet
  • S3D
  • SceneFlow
  • ShapeNet
  • Sintel-Stereo
  • Stable Diffusion
  • Swin-L
  • T-MARS
  • TartanAir
  • Together-RPJ-7B
  • UNIC
  • Unreal Engine
  • VGG-16 network
  • VideoCLIP
  • Visual Genome
  • WIT
  • WordNet
  • YFCC100M
  • ZLaP
  • iBOT

Topics

3D vision · AI Model Development · Autonomous driving · Bi-level Optimization · CLIP Models · Computer graphics · Computer vision · Data Augmentation · Data-centric AI · Data-driven revolution · Dataset Creation · Domain Adaptation · Domain gap · Few-shot Learning · Generalization · Generative AI · Generative models · High-quality labels · ImageNet moment · Infinigen · Metaverse · Multimodal Learning · Neural Radiance Fields (NeRF) · Photo-realistic Rendering · Procedural generation · Robustness · Simulation · Synthetic Data · Synthetic Data Generation · Synthetic data · Synthetic data generation · Transfer Learning · Virtual try-on · Visual Localization

Notes

Open for commentary — connections to other work, critiques, follow-up reading.