Hybrid Knowledge Routed Modules for Large-scale Object Detection (1810.12681v1)

Abstract: The dominant object detection approaches treat the recognition of each region separately and overlook crucial semantic correlations between objects in one scene. This paradigm leads to substantial performance drop when facing heavy long-tail problems, where very few samples are available for rare classes and plenty of confusing categories exists. We exploit diverse human commonsense knowledge for reasoning over large-scale object categories and reaching semantic coherency within one image. Particularly, we present Hybrid Knowledge Routed Modules (HKRM) that incorporates the reasoning routed by two kinds of knowledge forms: an explicit knowledge module for structured constraints that are summarized with linguistic knowledge (e.g. shared attributes, relationships) about concepts; and an implicit knowledge module that depicts some implicit constraints (e.g. common spatial layouts). By functioning over a region-to-region graph, both modules can be individualized and adapted to coordinate with visual patterns in each image, guided by specific knowledge forms. HKRM are light-weight, general-purpose and extensible by easily incorporating multiple knowledge to endow any detection networks the ability of global semantic reasoning. Experiments on large-scale object detection benchmarks show HKRM obtains around 34.5% improvement on VisualGenome (1000 categories) and 30.4% on ADE in terms of mAP. Codes and trained model can be found in https://github.com/chanyn/HKRM.

• The paper introduces HKRM, integrating explicit class-graph reasoning with implicit spatial constraints to boost semantic detection.
• The explicit module uses graph-based MLPs with linguistic attributes, refining region features through structured relationships.
• Empirical results show mAP improvements of 34.5% on Visual Genome and 30.4% on ADE, highlighting its robustness in large-scale detection.

Hybrid Knowledge Routed Modules for Large-scale Object Detection

The paper "Hybrid Knowledge Routed Modules for Large-scale Object Detection" by Jiang et al. introduces a novel framework to address performance challenges in large-scale object detection, particularly under long-tail distributions common in real-world scenarios. The devised framework, Hybrid Knowledge Routed Modules (HKRM), incorporates two distinct knowledge forms—explicit and implicit—into the object detection pipeline, enhancing the semantic reasoning within images.

The conventional region-based object detection paradigm isolates region proposals, neglecting the semantic correlations among objects. This oversight is detrimental in scenarios where certain object classes are underrepresented. The HKRM framework addresses this limitation by leveraging two modules: an explicit knowledge module and an implicit knowledge module.

The explicit module integrates structured linguistic constraints, such as shared attributes and relational knowledge, obtained from class-to-class graphs. For instance, the module employs a graph-theoretic approach utilizing Multi-layer Perceptrons supervised by prior class-to-class attribute and relationship graphs. This enables the model to refine region features with improved semantic context, akin to human-like reasoning by understanding descriptive and interactional attributes of objects within an image.

The implicit module captures unarticulated spatial constraints by applying multiple region-to-region graphs devoid of explicit human-labeled spatial definitions. It aggregates spatial-geometry features of regions to learn common patterns—which are challenging to articulate explicitly—but are vital for accurate detection and localization.

The empirical results on large datasets, including Visual Genome and ADE, underscore the efficacy of HKRM. Specifically, a mean Average Precision (mAP) improvement of 34.5% on Visual Genome with 1000 classes, and 30.4% on ADE, indicates the robustness of integrating diverse knowledge forms. Notably, the modules encapsulate general-purpose applicability, enhancing various detection networks with minimal computational overhead.

In light of these advancements, HKRM presents tangible implications for advancing the field of AI-based object detection. It suggests a promising direction for future research to explore incorporating multifaceted knowledge forms beyond the explicit-implicit dichotomy, potentially involving dynamic and context-sensitive reasoning frameworks. Additionally, extending these methodologies to incorporate emerging datasets and evolving object categories in dynamic environments could further enhance real-world applicability and generalization.

Overall, the paper systematically extends the semantic reasoning capabilities of traditional object detection frameworks by embedding commonsense knowledge, thereby paving the path for more robust and context-aware AI models.