# ALFA: Agglomerative Late Fusion Algorithm for Object Detection

## TL;DR

ALFA is a new late fusion algorithm for object detection that uses agglomerative clustering to combine predictions from multiple detectors, improving accuracy on standard datasets.

## Contribution

ALFA introduces a novel agglomerative clustering approach for late fusion in object detection, outperforming existing methods on PASCAL VOC datasets.

## Key findings

- Achieves up to 32% lower error than best individual detectors.
- Outperforms baseline fusion strategies and the DBF algorithm.
- Effective with pairs and triplets of state-of-the-art detectors.

## Abstract

We propose ALFA - a novel late fusion algorithm for object detection. ALFA is based on agglomerative clustering of object detector predictions taking into consideration both the bounding box locations and the class scores. Each cluster represents a single object hypothesis whose location is a weighted combination of the clustered bounding boxes. ALFA was evaluated using combinations of a pair (SSD and DeNet) and a triplet (SSD, DeNet and Faster R-CNN) of recent object detectors that are close to the state-of-the-art. ALFA achieves state of the art results on PASCAL VOC 2007 and PASCAL VOC 2012, outperforming the individual detectors as well as baseline combination strategies, achieving up to 32% lower error than the best individual detectors and up to 6% lower error than the reference fusion algorithm DBF - Dynamic Belief Fusion.