Multi-label Classification with Panoptic Context Aggregation Networks

TL;DR

PanCAN tackles the challenge of robust multi-label image classification by modeling cross-scale and multi-order context. It introduces a cascaded Panoptic Context Aggregation Network that combines multi-order neighborhood detection via random-walk guided attention with cross-scale attention-based fusion, all learned end-to-end. Across NUS-WIDE, PASCAL VOC2007, and MS-COCO, PanCAN consistently outperforms state-of-the-art methods and shows strong backbone robustness, indicating its effectiveness for complex scene understanding. The work advances context modeling by unifying micro- to macro-scale information and higher-order relations into a single differentiable framework with practical impact for real-world recognition tasks.

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.

Figures (6)

• Figure 1: Multi-order neighborhood system. The left side shows the first-order (blue cells) and second-order neighborhoods (orange cells). On the right, the third-order neighborhood (green cells) is built from the second-order neighborhood based on the transition probabilities.
• Figure 2: The proposed PanCAN. (a) The overall architecture: the input image is divided into a grid of cells, where each cell is represented by visual features and rotated positional features. These enhanced features are then processed by PanCAN to capture fine-grained and cross-scale contexts, which are further fused with global features for classification. (b) "Multi-order context-aware mapping network" module ("MOCAMN"), where "RWCA" denotes the Random Walk-based Context Aggregation mechanism. The colored boxes represent different levels of neighborhood relations: first-order neighbors, directly adjacent blocks (i.e., neighbors of first-order blocks), and second-order neighbors. (c) "Cross-scale context-aware mapping network" module ("CSCAMN"), the most salient cell in red within each macro-cell is estimated as the anchor, while the remaining cells in different colors belonging to the same macro-cell are aggregated with the anchor through an attention mechanism to obtain the representation of macro-cells.
• Figure 3: Visualization of learned contexts at the different depths of the network on NUS-WIDE dataset. The images are divided into a grid of $8\times10$ cells. From top to bottom each row respectively corresponds to the context-aware network with a depth of one, two and three layers in one scale. The 1st, 3rd and 5th columns show the learned contexts (the centering cell is marked in red, and its learned 1st-, 2nd-, and 3rd-order domains respectively are in yellow, blue and green), while the 2nd, 4th, and 6th columns visualize the learned weights of neighboring cells on the centering cell, where warmer color indicating higher influence.
• Figure 4: Visualization of learned cross-scale contexts. From top to bottom each row respectively shows one instance in the NUS-WIDE, PASCAL VOC2007, MS-COCO dataset. The 1st, 3rd an 5th columns show the grids at different scales separated by white dashed lines. The 2nd, 4th and 6th columns illustrate the influence of the micro-cell on the features of macro-cell learned by the network where warmer color stands for higher importance.
• Figure 5: Visualization of evolution of the learned contexts on the MS-COCO dataset. From left to right, the images show the learned importance of cells within a macro-cell at different training iterations: start point, 50th epochs, 100th epochs, 150th epochs and 200th epochs. Redder colors stand for higher importance and bluer are lower.