# ALFA: Agglomerative Late Fusion Algorithm for Object Detection

Source: [ALFA: Agglomerative Late Fusion Algorithm for Object Detection](<https://www.sota2.com/research/paper/68fa66131e209131cea0e1ba>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:42:29.976Z

Authors: Evgenii Razinkov, Iuliia Saveleva, Ji\\v{r}i Matas

Published: 2019-07-13T12:30:37.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1907.06067>)

[PDF](<https://arxiv.org/pdf/1907.06067.pdf>)