Discovering Novel Actions in Egocentric Videos through Object-Grounded Visual Commonsense Reasoning

Concepts de base

A neuro-symbolic framework that leverages object-centric vision-language models and commonsense knowledge to discover novel actions and infer activities in egocentric videos without explicit supervision.

Résumé

The paper proposes a neuro-symbolic framework called ALGO (Action Learning with Grounded Object recognition) to tackle the problem of open-world egocentric activity recognition. The key ideas are:

1. Evidence-based Object Grounding: ALGO uses a novel neuro-symbolic prompting approach to ground objects in the video by leveraging object-centric vision-language foundation models (like CLIP) as a noisy oracle and reasoning over compositional properties from a commonsense knowledge base (ConceptNet).

2. Object-driven Activity Discovery: Driven by prior commonsense knowledge, ALGO discovers plausible activities (verb-noun combinations) through an energy-based symbolic pattern theory framework. It then learns to ground the inferred action (verb) concepts in the video.

3. Visual-Semantic Action Grounding: ALGO learns to ground the inferred actions from the activity interpretations by bootstrapping a simple mapping function from video features to the semantic embedding space of the commonsense knowledge base.

The proposed approach is evaluated on two egocentric video datasets, GTEA Gaze and GTEA Gaze Plus, demonstrating its performance on open-world activity inference and generalization to unseen actions. ALGO also shows competitive performance compared to state-of-the-art vision-language models in zero-shot settings.

Stats

The GTEA Gaze dataset consists of 14 subjects performing activities composed of 10 verbs and 38 nouns across 17 videos.
The GTEA Gaze Plus dataset has 27 nouns and 15 verbs from 6 subjects performing 7 meal preparation activities across 37 videos.
The Charades-Ego dataset contains 7,860 videos with 157 activities in the test set.

Citations

"Learning to infer labels in an open world, i.e., in an environment where the target 'labels' are unknown, is an important characteristic for achieving autonomy."
"Foundation models pre-trained on enormous amounts of data have shown remarkable generalization skills through prompting, particularly in zero-shot inference. However, their performance is restricted to the correctness of the target label's search space."

Idées clés tirées de

Discovering Novel Actions from Open World Egocentric Videos with Object-Grounded Visual Commonsense Reasoning

by Sanjoy Kundu... à arxiv.org 05-06-2024

https://arxiv.org/pdf/2305.16602.pdf

Questions plus approfondies

How can the proposed neuro-symbolic framework be extended to handle third-person egocentric videos, where the gaze information is not available

To extend the proposed neuro-symbolic framework to handle third-person egocentric videos without gaze information, we can incorporate alternative mechanisms for attention and object grounding. Instead of relying on human gaze data, we can utilize object detection algorithms to identify regions of interest in the video frames. These regions can then be used as input for object grounding using a vision-language model like CLIP. By adapting the object grounding process to work without gaze information, we can still ground objects in the video frames and proceed with the neuro-symbolic reasoning for activity recognition. Additionally, incorporating attention mechanisms that focus on relevant objects and actions in the video frames can help simulate the egocentric perspective even without explicit gaze data.

What are the potential limitations of the energy-based pattern theory formalism in representing and reasoning over more complex commonsense knowledge structures

While the energy-based pattern theory formalism provides a flexible framework for representing and reasoning over complex knowledge structures, it may have limitations when dealing with highly intricate commonsense knowledge. One potential limitation is the scalability of the approach to handle a large number of interconnected concepts. As the knowledge base grows, the complexity of the energy calculations and inference process may increase significantly, leading to computational challenges. Additionally, the formalism may struggle with capturing nuanced relationships between concepts that require more sophisticated reasoning mechanisms beyond simple energy calculations. Complex semantic relationships that involve multiple layers of abstraction or context may not be effectively captured by the energy-based approach, limiting the model's ability to reason over such structures.

Can the visual-semantic action grounding mechanism be further improved by incorporating more sophisticated contrastive learning-based approaches, similar to those used in vision-language pretraining

The visual-semantic action grounding mechanism can indeed be further improved by incorporating more sophisticated contrastive learning-based approaches similar to those used in vision-language pretraining. By leveraging contrastive learning techniques, the model can learn more robust and discriminative representations for grounding actions in the visual data. Contrastive learning can help in capturing fine-grained similarities and differences between visual features and semantic embeddings, leading to more accurate and semantically meaningful action grounding. Additionally, incorporating self-supervised learning objectives within the contrastive framework can enhance the model's ability to learn meaningful representations from unlabeled data, further improving the quality of visual-semantic action grounding. By integrating advanced contrastive learning methods, the visual-semantic action grounding mechanism can achieve higher performance and generalization capabilities in egocentric activity recognition tasks.