Multi Modal Intent Detection
A leading manufacturing firm specializing in high-volume production of automotive components. The company operates multiple production lines and faces frequent scheduling bottlenecks due to fluctuating customer demands.
Understanding user intent in multi-modal interactions is a complex challenge, requiring accurate classification of known intents and detection of novel intents in evolving datasets. Existing research explores various techniques, but gaps remain in handling incremental classification effectively.
Conducted comprehensive research analysis across academic and industry sources to ground the development of innovative solutions.
Reproduced foundational studies, critically evaluating experimental outcomes to ensure methodological rigor.
Pioneered a new technique for incremental intent detection using adaptive clustering, enabling dynamic recognition of unseen categories.
The clustering approach proved effective in detecting novel intents while maintaining classification accuracy for known intents. Insights gained from literature analysis and paper reproductions provided a strong foundation for further advancements in multi-modal intent detection.
