SpatialSense: An Adversarially Crowdsourced Benchmark for Spatial Relation Recognition (1908.02660v2)

Abstract: Understanding the spatial relations between objects in images is a surprisingly challenging task. A chair may be "behind" a person even if it appears to the left of the person in the image (depending on which way the person is facing). Two students that appear close to each other in the image may not in fact be "next to" each other if there is a third student between them. We introduce SpatialSense, a dataset specializing in spatial relation recognition which captures a broad spectrum of such challenges, allowing for proper benchmarking of computer vision techniques. SpatialSense is constructed through adversarial crowdsourcing, in which human annotators are tasked with finding spatial relations that are difficult to predict using simple cues such as 2D spatial configuration or language priors. Adversarial crowdsourcing significantly reduces dataset bias and samples more interesting relations in the long tail compared to existing datasets. On SpatialSense, state-of-the-art recognition models perform comparably to simple baselines, suggesting that they rely on straightforward cues instead of fully reasoning about this complex task. The SpatialSense benchmark provides a path forward to advancing the spatial reasoning capabilities of computer vision systems. The dataset and code are available at https://github.com/princeton-vl/SpatialSense.

SpatialSense: An Adversarially Crowdsourced Benchmark for Spatial Relation Recognition

The paper "SpatialSense: An Adversarially Crowdsourced Benchmark for Spatial Relation Recognition" presents an innovative approach to developing a dataset for assessing spatial relation recognition in computer vision systems. Spatial relation recognition is critical for scene description, object referencing, and tasks such as navigation and manipulation, yet current benchmarks lack the depth necessary to gauge advanced spatial reasoning beyond simple visual cues.

The SpatialSense Dataset

SpatialSense, introduced by Yang, Russakovsky, and Deng from Princeton University, offers a specialized dataset designed to capture a broad spectrum of spatial relations that are difficult to predict using simple 2D spatial configuration or language priors. Created through an adversarial crowdsourcing process, human annotators were tasked with identifying spatial relations that would challenge predictive models by being non-trivial or counterintuitive. This systematic approach to annotator guidance results in a dataset that reduces biases and samples more nuanced relations than those typically found in existing datasets such as Visual Genome or Open Images.

SpatialSense comprises 17,498 relations across 11,569 images, with balanced representations of positive and negative relations for each spatial predicate. The dataset holds promise for rigorous testing of spatial relation recognition models, focusing on challenging examples often occurring in the long tail of spatial semantics.

Implications and Observations

The authors contend that SpatialSense highlights the limitations of current state-of-the-art models in recognizing spatial relations, which often rely heavily on dataset biases rather than developing true spatial reasoning skills. When tested, even advanced models performed comparably to simple baselines reliant only on 2D cues, underscoring the need for SpatialSense as a more challenging benchmark to drive progress in the spatial reasoning capabilities of AI systems.

These findings beg further exploration into how models can be fine-tuned or redesigned to genuinely comprehend spatial semantics of different entities beyond mere surface cues, which is critical for applications across human-robot interaction, autonomous navigation, and complex scene understanding.

Future Directions

SpatialSense's construction through adversarial crowdsourcing is particularly compelling as a methodology that may be beneficial in crafting other benchmarks aimed at challenging AI systems on nuanced reasoning tasks. Exploring adversarial techniques further could refine how datasets are assembled and enhance our understanding of AI biases in practical scenarios.

As the community continues to build upon benchmarks like SpatialSense, integrating insights from spatial relations research with advances in machine learning architectures could foster the development of AI models capable of reasoning about spatial semantics in a manner aligned with human understanding.

In conclusion, SpatialSense stands as a crucial asset for those researching visual recognition and spatial reasoning. While the dataset captures inherent complexities of spatial relations, addressing its results could lead to significant strides in AI's ability to interpret the visual world meaningfully.