THE CONCEPT
Bridging the cognitive gap between visual stimuli and auditory description, this research initiative explores the frontiers of Symbol Grounding and Multimodal Learning. By architecting a system that “sees” motion and autonomously maps it to the corresponding spoken language, the model achieves true Compositional Generalization that enables AI to accurately describe novel object-action scenarios it has never witnessed during training. The result is a system that moves beyond simple pattern matching to understand the semantic “essence” of an event, effectively translating pixels into phonemes with near-human logic.
THE ENGINEERING
The core architecture abandons traditional direct feature mapping in favor of a structured, disentangled latent space. I engineered a custom β-Variational Autoencoder (β-VAE) to compress high-dimensional video (CLIP) and audio (Wav2Vec) features into shared abstract semantic vectors. To solve the “black box” problem of standard deep learning, I implemented a Gradual Ramp-Up β- scheduling strategy. This algorithmic approach dynamically tunes the regularization pressure during training, forcing the model to isolate independent factors of variation—specifically distinguishing static “objects” from dynamic “actions”. These structured latents were then fed into a modified Sequence-to-Sequence (Seq2Seq) LSTM network, which translates visual context into auditory sequences. The system outperformed baseline models significantly, reducing the Test Cosine Distance alignment error from 50.12 to just 0.17, proving that structured representation learning is the key to unlocking robust Cross-Modal Translation.
TECH STACK
Core Frameworks: PyTorch (β-VAE), TensorFlow/Keras (Seq2Seq)
Feature Extraction: CLIP (Video) , Wav2Vec (Audio)
Neural Architecture: 1D Convolutional Encoders , LSTM Context Decoders , Dense Output Layers
Data Pipeline: NumPy , Scikit-learn (MinMax Scaling , Temporal Segmentation)
Optimization: Adaptive Capacity Loss, Gradual β-Scheduling, Adam Optimizer