DNA: Denoised Neighborhood Aggregation for Fine-grained Category Discovery
This addresses the challenge of reducing annotation costs for fine-grained analysis in computer vision, representing a novel method for a known bottleneck rather than a foundational advancement.
The paper tackles the problem of discovering fine-grained categories from coarsely labeled data by proposing Denoised Neighborhood Aggregation (DNA), a self-supervised framework that encodes semantic structures into embeddings, resulting in a 21.31% accuracy improvement in neighbor retrieval and an average 9.96% improvement over state-of-the-art models on three metrics.
Discovering fine-grained categories from coarsely labeled data is a practical and challenging task, which can bridge the gap between the demand for fine-grained analysis and the high annotation cost. Previous works mainly focus on instance-level discrimination to learn low-level features, but ignore semantic similarities between data, which may prevent these models learning compact cluster representations. In this paper, we propose Denoised Neighborhood Aggregation (DNA), a self-supervised framework that encodes semantic structures of data into the embedding space. Specifically, we retrieve k-nearest neighbors of a query as its positive keys to capture semantic similarities between data and then aggregate information from the neighbors to learn compact cluster representations, which can make fine-grained categories more separatable. However, the retrieved neighbors can be noisy and contain many false-positive keys, which can degrade the quality of learned embeddings. To cope with this challenge, we propose three principles to filter out these false neighbors for better representation learning. Furthermore, we theoretically justify that the learning objective of our framework is equivalent to a clustering loss, which can capture semantic similarities between data to form compact fine-grained clusters. Extensive experiments on three benchmark datasets show that our method can retrieve more accurate neighbors (21.31% accuracy improvement) and outperform state-of-the-art models by a large margin (average 9.96% improvement on three metrics). Our code and data are available at https://github.com/Lackel/DNA.