Multimodal Prediction Union

Overview

In product/image matching, different signals (CNN embeddings, text TF-IDF, perceptual hash) each catch different true matches. Take the set union of all per-signal predictions to maximize recall. This is simpler and often better than learned fusion for retrieval tasks where precision can be traded for recall.

Quick Start

import numpy as np
import pandas as pd

def union_predictions(df, pred_columns):
    """Merge predictions from multiple signals via set union.

    Args:
        df: DataFrame where each pred_column contains arrays of matched IDs
        pred_columns: list of column names with per-signal match arrays
    Returns:
        Series of unique merged match arrays
    """
    def merge_row(row):
        all_ids = np.concatenate([row[col] for col in pred_columns])
        return np.unique(all_ids)
    return df.apply(merge_row, axis=1)

# Usage: each column has arrays of matched item IDs
df['image_matches'] = find_matches(image_embeddings, ids, img_thresh)
df['text_matches'] = find_matches(text_embeddings, ids, txt_thresh)
df['hash_matches'] = phash_group_matches(df)

df['final_matches'] = union_predictions(
    df, ['image_matches', 'text_matches', 'hash_matches']
)

Key Decisions

• Union over intersection: maximizes recall at slight precision cost — appropriate for retrieval
• Per-signal thresholds: tune each modality's threshold independently before merging
• Order doesn't matter: set union is commutative — no need to prioritize signals
• Diminishing returns: typically 3-4 signals saturate; more signals add noise without recall gain

References

• Source: part-2-rapids-tfidfvectorizer-cv-0-700
• Competition: Shopee - Price Match Guarantee