Graduation Year

2024

Document Type

Dissertation

Degree

Ph.D.

Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Computer Science and Engineering

Major Professor

Sudeep Sarkar, Ph.D.

Committee Member

Yu Sun, Ph.D.

Committee Member

Shaun Canavan, Ph.D.

Committee Member

Anuj Srivastava, Ph.D.

Committee Member

Thomas Sanocki, Ph.D.

Committee Member

Xiaopeng Li, Ph.D.

Keywords

Compositional Perceptual Processing, Online Learning, Predictive Learning

Abstract

In today's world, AI systems need to make sense of large amounts of data as it unfolds in real-time, whether it's a video from surveillance and monitoring cameras, streams of egocentric footage, or sequences in other domains such as text or audio. The ability to break these continuous data streams into meaningful events, discover nested structures, and predict what might happen next at different levels of abstraction is crucial for applications ranging from passive surveillance systems to sensory-motor autonomous learning. However, most existing models rely heavily on large, annotated datasets with fixed data distributions and offline epoch-based training, which makes them impractical for handling the unpredictability and scale of dynamic real-world environments. This dissertation tackles these challenges by introducing a set of predictive models designed to process streaming data efficiently, segment events, and build sequential memory models without supervision or data storage.

First, we present a single-layer predictive model that segments long, unstructured video streams by detecting temporal events and spatially localizing objects in each frame. The model is applied to wildlife monitoring footage, where it processes continuous, high-frame-rate video and successfully detects and tracks events without supervision. It operates in an online streaming manner to perform simultaneous training and inference without storing or revisiting the processed data. This approach alleviates the need for manual labeling, making it ideal for handling long-duration, real-world video footage. Building on this, we introduce STREAMER, a multi-layered architecture that extends the single-layer model into a hierarchical predictive framework. STREAMER segments events at different levels of abstraction, capturing the compositional structure of activities in egocentric videos. By dynamically adapting to various timescales, it creates a hierarchy of nested events and forms more complex and abstract representations of the input data. Finally, we propose the Predictive Attractor Model (PAM), which builds biologically plausible memory models of sequential data. Inspired by neuroscience, PAM uses sparse distributed representations and local learning rules to avoid catastrophic forgetting, allowing it to continually learn and make predictions without overwriting previous knowledge. Unlike many traditional models, PAM can generate multiple potential future outcomes conditioned on the same context, which allows for handling uncertainty in generative tasks.

Together, these models form a unified framework of predictive learning that addresses multiple challenges in event understanding and temporal data analyses. By using prediction as the core mechanism, they segment continuous data streams into events, discover hierarchical structures across multiple levels of abstraction, learn semantic event representations, and model sequences without catastrophic forgetting.

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