ViLLa: A Neuro-Symbolic approach for Animal Monitoring
This work addresses the need for interpretable systems in animal monitoring for researchers and conservationists, though it is incremental as it combines existing neuro-symbolic approaches for a specific domain.
The paper tackles the problem of monitoring animal populations by developing ViLLa, a neuro-symbolic framework that integrates visual detection, language parsing, and symbolic reasoning to answer natural language queries about animals in images, such as counting or locating them, with results showing accurate performance on animal imagery tasks.
Monitoring animal populations in natural environments requires systems that can interpret both visual data and human language queries. This work introduces ViLLa (Vision-Language-Logic Approach), a neuro-symbolic framework designed for interpretable animal monitoring. ViLLa integrates three core components: a visual detection module for identifying animals and their spatial locations in images, a language parser for understanding natural language queries, and a symbolic reasoning layer that applies logic-based inference to answer those queries. Given an image and a question such as "How many dogs are in the scene?" or "Where is the buffalo?", the system grounds visual detections into symbolic facts and uses predefined rules to compute accurate answers related to count, presence, and location. Unlike end-to-end black-box models, ViLLa separates perception, understanding, and reasoning, offering modularity and transparency. The system was evaluated on a range of animal imagery tasks and demonstrates the ability to bridge visual content with structured, human-interpretable queries.