Role: Visual Generative Modeling Manager

  • Build the foundational image generation technologies behind Image Playground, Genmoji, Image Wand and Photos Clean Up experiences that shipped as part of Apple Intelligence with iOS 18.2. Join us as we transform the way billions of users express themselves, create and communicate, on Apple platforms!
  • Leaders that have the ability to translate ideas to action, and the hands-on expertise to train and deploy large-scale, generative ML-based features/workflows. In this role your focus will be on visual generative modeling to power applications related to stylized and identity preserving image/video generation, image and video editing, avatar generation and much more. You will work in a highly cross-functional setting, provide critical technical expertise and leadership, and be responsible for delivering ML solutions that serve the intended experiences while respecting practical constraints such as memory, latency and power.
  • Responsibilities in the role will include training large scale conditional generative models in the visual domain on distributed backends, deployment of compact neural architectures such as transformers efficiently on device, and learning adaptive policies that can be personalized to the user in a privacy preserving manner. Ensuring quality in the field, with an emphasis on fairness and model robustness would constitute an important part of the role.
  • Hands on experience training larges scale visual generative models (e.g. diffusion models) and/or adapting pre-trained models for downstream tasks.
  • Experience in addressing challenges associated with the transition of a prototype into a shipping product.
  • Familiarity with challenges of developing algorithms that run efficiently on resource constrained platforms.

Overview

  • Computer vision is a vast and rapidly evolving field at the intersection of artificial intelligence and image processing. It focuses on enabling machines to interpret and analyze visual data from the world—transforming raw pixels into meaningful information. With applications ranging from autonomous driving and medical imaging to augmented reality and surveillance, computer vision is reshaping how we interact with technology.

  • This primer delves into the core concepts and tasks that form the foundation of computer vision, exploring key areas such as image classification, object detection, segmentation, and more. Through both theoretical insights and practical examples, we aim to provide a comprehensive understanding of the challenges and innovations that drive this exciting domain.

Tasks

Image Generation

Face Recognition

Scene Classification

OCR handwriting

Scene Understanding: A Deep Dive

Scene Understanding goes well beyond the detection of individual objects. It is about grasping the full context of an image or environment, adapting to new variations, and predicting future configurations. This capability is not only essential for informing other systems (including human users) but also for enabling interactive, real-world applications. For instance, while robotics can use scene understanding to modify or interact with their surroundings, multimedia analysis (such as video retrieval) relies on it even when only limited contextual information is available.

Key Components of Scene Understanding

  • Contextual Reasoning:
    This involves inferring the relationships between various objects—like understanding a person riding a bike on a street—and recognizing the broader context of the scene.

  • Scene Graph Generation:
    By creating structured representations that map out objects and their interconnections, scene graphs facilitate tasks such as image retrieval and visual question answering.

  • 3D Scene Reconstruction:
    This process infers depth and spatial structure from 2D images, enabling a more intuitive understanding of spatial relationships within a scene.

Utilizing the Physical Environment as a Canvas

With scene understanding, the physical environment itself becomes a dynamic canvas. Surfaces in the real world—whether flat, like tables and floors, or volumetric, like walls and ceilings—can serve as interactive platforms for digital content. Here’s how these capabilities translate into practical use cases:

  • Virtual Content Placement:
    Identifying physical planes enables the placement of digital objects on specific surfaces. For example, a virtual picture frame can be accurately aligned on a wall or a game object on a table. Semantic labels (such as “floor,” “ceiling,” “desk,” or “couch”) further enhance this interaction, ensuring objects are placed naturally according to the type of surface.

  • Realistic Alignment and Interaction:
    Proper alignment is critical. When a virtual object is correctly placed—like a Mars Rover sitting neatly on a table—users can easily gauge its position and distance. In contrast, misaligned objects can create confusion, as they may seem to float or be improperly anchored.

  • Occlusion:
    By using real-world surfaces to mask or hide portions of virtual objects, occlusion creates a more immersive experience. For example, a virtual character might correctly appear behind a physical table rather than overlapping it, reinforcing the illusion that digital elements coexist naturally with the physical world. For systems like the Meta Quest 3, depth information captured by specialized cameras enhances occlusion, ensuring virtual content is layered correctly according to physical distances.

  • Physics-Based Interactions:
    Scene understanding can treat real surfaces as collision boundaries. This means that digital objects—such as a virtual ball—can bounce off walls or floors, adding a layer of realism to interactions.

  • Navigation:
    Virtual characters or objects can be programmed to navigate physical spaces intelligently. Whether they’re restricted to walking on floors or allowed to traverse walls and tables, the underlying physical context guides their movement in a believable manner.

  • Visualization of Physical Surfaces:
    While the magic of immersive experiences often comes from digital objects interacting seamlessly with the real world, sometimes visualizing these surfaces helps provide context. For example, in certain applications the environment might be revealed gradually using dimmed, grayscale passthrough visuals combined with animated outlines of surfaces, enhancing user confidence in the system’s spatial awareness.

  • Shadow Rendering:
    Shadows play a crucial role in grounding virtual objects. By adding drop shadows or halos that mimic natural light, digital objects appear more realistic and their spatial relationships—such as distance and alignment—are communicated more effectively. This not only improves usability but also contributes to safety in interactive applications.

  • Leveraging Semantic Labels for Rich Experiences:
    Semantic labels allow developers to tailor the digital overlay on physical surfaces creatively. Imagine transforming a room by filling the floor with lush green grass or replacing the ceiling with a dramatic, star-filled night sky. These labels enable rich, contextually aware experiences that can change dynamically based on the environment.

Updated Perspective (Dec 17, 2024)

Scene understanding is evolving continuously. Its capabilities—from contextual reasoning and scene graph generation to 3D scene reconstruction—are instrumental in creating immersive, interactive experiences. By using the physical world as a canvas, developers can design applications where digital and real-world elements coexist naturally, enhancing both the usability and the magic of mixed reality environments.

  • Label Noise Representation Learning

Models

  • The image below (source), showcases the architectures of the models we will talk about below

  • Convolutional Neural Networks (CNNs):
    The backbone for many computer vision tasks, CNNs are designed to automatically and adaptively learn spatial hierarchies of features from input images.

  • Vision Transformers (ViTs):
    A more recent innovation, transformers adapted for vision tasks have shown competitive performance by capturing long-range dependencies in image data.

  • CLIP

  • CLIP2Scene

  • MaskCLIP

  • Segment Anything

Diffusion models

  • Diffusion models are a class of generative models that create data (like images) by learning to reverse a process that gradually adds noise to the data until it becomes nearly random. Here’s a breakdown of how they work:
  1. Forward Process (Diffusion):
    • The model starts with a real image and adds small amounts of noise over many steps.
    • As the steps progress, the image becomes increasingly noisy until it eventually resembles pure noise.
  2. Reverse Process (Denoising):
    • The model is trained to reverse this noising process.
    • Starting from random noise, it gradually “denoises” the image step by step, reconstructing the original image in the process.
  3. Learning the Process:
    • During training, the model learns how to predict the noise added at each step, which helps it understand how to remove noise and recover the original structure of the image.
    • This training is typically done using a loss function that measures the difference between the predicted noise and the actual noise added.
  4. Applications and Advantages:
    • Diffusion models have been shown to generate high-quality images and have been used in tasks like image synthesis and text-to-image generation.
    • Their iterative denoising process often results in finer details and better diversity in the generated samples compared to some other generative models.
  • In essence, diffusion models leverage the idea of “reversing” a controlled corruption process to generate realistic data from noise.

Generative Adversarial Networks (GANS)

Variational Autoencoders (VAEs)

Autoregressive Models

  • PixelCNN

Transformer based Models

  • DALL-E

VLM as a Judge

Datasets and Benchmarks

Evaluation

  • Developing strategies and algorithms for mining large amounts of data numbering in the billions for the purposes of targeted model training.

  • Addressing challenges in automatic evaluation of generative results, identification and classification of failure cases, and strategies for assessing their prevalence and severity.

  • Streamlining human-in-the-loop processes for dataset construction, and creating and implementing systems that execute on strategies to account for subjectivity and human error.

  • Synthesis of training data as well as synthesis of augmentations to real-world data.

References