Multi-label Classification with Panoptic Context Aggregation Networks

TL;DR

The paper introduces PanCAN, a novel deep network that hierarchically integrates multi-scale, multi-order geometric contexts for improved multi-label image classification, outperforming existing methods on standard benchmarks.

Contribution

It proposes a new hierarchical framework that combines cross-scale feature aggregation with attention mechanisms to model complex scene contexts in multi-label classification.

Findings

PanCAN outperforms state-of-the-art methods on NUS-WIDE, PASCAL VOC2007, and MS-COCO.

The approach effectively models multi-scale, multi-order geometric relationships.

Experimental results show significant improvements in classification accuracy.

Abstract

Context modeling is crucial for visual recognition, enabling highly discriminative image representations by integrating both intrinsic and extrinsic relationships between objects and labels in images. A limitation in current approaches is their focus on basic geometric relationships or localized features, often neglecting cross-scale contextual interactions between objects. This paper introduces the Deep Panoptic Context Aggregation Network (PanCAN), a novel approach that hierarchically integrates multi-order geometric contexts through cross-scale feature aggregation in a high-dimensional Hilbert space. Specifically, PanCAN learns multi-order neighborhood relationships at each scale by combining random walks with an attention mechanism. Modules from different scales are cascaded, where salient anchors at a finer scale are selected and their neighborhood features are dynamically fused via attention. This enables effective cross-scale modeling that significantly enhances complex scene understanding by combining multi-order and cross-scale context-aware features. Extensive multi-label classification experiments on NUS-WIDE, PASCAL VOC2007, and MS-COCO benchmarks demonstrate that PanCAN consistently achieves competitive results, outperforming state-of-the-art techniques in both quantitative and qualitative evaluations, thereby substantially improving multi-label classification performance.