Salient Objects in Clutter: Bringing Salient Object Detection to the Foreground (1803.06091v2)

Abstract: We provide a comprehensive evaluation of salient object detection (SOD) models. Our analysis identifies a serious design bias of existing SOD datasets which assumes that each image contains at least one clearly outstanding salient object in low clutter. The design bias has led to a saturated high performance for state-of-the-art SOD models when evaluated on existing datasets. The models, however, still perform far from being satisfactory when applied to real-world daily scenes. Based on our analyses, we first identify 7 crucial aspects that a comprehensive and balanced dataset should fulfill. Then, we propose a new high quality dataset and update the previous saliency benchmark. Specifically, our SOC (Salient Objects in Clutter) dataset, includes images with salient and non-salient objects from daily object categories. Beyond object category annotations, each salient image is accompanied by attributes that reflect common challenges in real-world scenes. Finally, we report attribute-based performance assessment on our dataset.

• The paper identifies significant bias in current SOD datasets and introduces the SOC dataset to emulate real-world image complexity.
• It comprehensively benchmarks state-of-the-art CNN models using metrics like region similarity, pixel accuracy, and structure similarity to reveal performance drops in complex settings.
• The study analyzes attributes such as occlusion, motion blur, and complex boundaries, paving the way for more robust and accurate SOD models.

An Analysis of "Salient Objects in Clutter: Bringing Salient Object Detection to the Foreground"

"Salient Objects in Clutter: Bringing Salient Object Detection to the Foreground" addresses key challenges in the field of salient object detection (SOD), primarily focusing on biases associated with existing datasets and proposing novel solutions to reflect real-world complexity. This work presents an in-depth evaluation of SOD models, revealing limitations in prevalent datasets that inadvertently induce biases due to simplified and often contrived image collections.

Key Contributions

1. Bias in Existing Datasets: The paper identifies a significant data selection bias prevalent in current SOD datasets, which typically assume that each image contains at least one easily discernible salient object amidst low clutter. This bias results in inflated performance metrics for state-of-the-art SOD models when validated on existing datasets but yields far from satisfactory outcomes when applied to real-world environments.
2. The SOC Dataset: In response to the identified bias, the authors introduce the "Salient Objects in Clutter" (SOC) dataset, an extensive and diverse collection of 6,000 images, designed to better emulate real-world conditions. This dataset stands out in several ways:
   • It includes a balanced representation of 3,000 images each for salient and non-salient object scenarios.
   • Instance-level annotations provide detailed object category demarcations, facilitating complex analyses such as weakly supervised SOD tasks.
   • Attributes are annotated for each salient image that highlights common challenges in dynamic and cluttered environments, such as occlusion, motion blur, and complex boundaries.
3. Performance Evaluation: A comprehensive benchmarking of multiple state-of-the-art convolutional neural networks (CNN) based SOD models is conducted using the SOC dataset. Three metrics are employed to evaluate model performance: region similarity, pixel-wise accuracy, and structure similarity. The paper indicates that while existing models perform robustly on simplified datasets, they exhibit significant performance drops in more complex settings represented in the SOC dataset.
4. Attributes-Based Analysis: By evaluating model performance on subgroup attributes such as object size, occlusion, and appearance changes, the research underscores strengths and limitations in current methodologies. Such insights are critical for paving the way towards more resilient SOD models capable of real-world applicability.

Implications and Future Directions

The findings from this paper have profound implications for the development and evaluation of SOD models. By highlighting how existing datasets may contribute to misleading performance metrics, the authors advocate for a shift towards datasets that present realistic scenarios, thus driving advancements in model robustness and applicability in diverse environments.

The SOC dataset offers significant potential for future research. It opens avenues for investigations into weakly supervised models and instance-level detection, leveraging its comprehensive annotations and carefully curated challenges. Furthermore, this paper provides an evaluative framework that can be adopted for future benchmarking efforts, encouraging the development of models that meet the nuances of dynamic visual contexts present in natural scenes.

In conclusion, the work emphasizes the necessity for balanced, realistic datasets as a foundation for SOD research. By addressing the biases in existing datasets and offering a more representative evaluation platform, "Salient Objects in Clutter" contributes a significant resource to the field that is poised to facilitate the development of more nuanced and accurate SOD models in the years ahead.