## Flowchart: Multi-Modal Feature Integration Architecture ### Overview The diagram illustrates a multi-modal neural network architecture for processing human and object features to generate relational features for classification. The system integrates spatial features through concatenation, employs transformer-based encoding/decoding, and uses a projection layer to combine features with past actions for classification. ### Components/Axes 1. **Input Features**: - Human Feature: [x_h, y_h] (pink blocks) - Object Feature: [x_o, y_o] (green blocks) - Union Feature: x_u (orange blocks) 2. **Core Components**: - Encoder: Blue block with Q (query), K (key), V (value) connections - Decoder: Blue block with K (key), V (value), Q (query) connections - Projection Layer: Gray block receiving Union Feature - Classifier MLP: Gray block receiving Relational Features and Past Actions 3. **Output**: - Relational Features: x_r (yellow/blue/orange blocks) - Final Output: Classifier MLP prediction ### Detailed Analysis 1. **Feature Integration**: - Human and Object Features are concatenated (pink + green → green) - Union Feature (x_u) is generated from concatenated features - Encoder processes Q/K/V to transform features into latent space - Decoder reconstructs features using K/V/Q interactions 2. **Projection and Classification**: - Projection Layer combines Union Feature with temporal context - Relational Features (x_r) are generated through decoder output - Classifier MLP fuses x_r with Past Actions for final prediction ### Key Observations 1. Bidirectional information flow between encoder and decoder 2. Spatial features (x, y coordinates) are preserved through concatenation 3. Temporal context (Past Actions) is integrated at classification stage 4. Transformer architecture (Q/K/V) used for feature transformation 5. Color-coded blocks indicate feature types and flow direction ### Interpretation This architecture demonstrates a hybrid approach combining: 1. **Spatial Attention**: Through Q/K/V mechanisms in encoder/decoder 2. **Temporal Integration**: By incorporating past actions in final classification 3. **Multi-Modal Fusion**: Via concatenation of human/object features 4. **Hierarchical Processing**: From raw features to relational representations The design suggests an intention to capture both spatial relationships (through transformer architecture) and temporal dynamics (through action history) for improved classification performance. The separation of feature generation (encoder/decoder) from classification (MLP) allows for modular optimization of different components.