Seeing the Unseen: Visual Common Sense for Semantic Placement (2401.07770v1)

Abstract: Computer vision tasks typically involve describing what is present in an image (e.g. classification, detection, segmentation, and captioning). We study a visual common sense task that requires understanding what is not present. Specifically, given an image (e.g. of a living room) and name of an object ("cushion"), a vision system is asked to predict semantically-meaningful regions (masks or bounding boxes) in the image where that object could be placed or is likely be placed by humans (e.g. on the sofa). We call this task: Semantic Placement (SP) and believe that such common-sense visual understanding is critical for assitive robots (tidying a house), and AR devices (automatically rendering an object in the user's space). Studying the invisible is hard. Datasets for image description are typically constructed by curating relevant images and asking humans to annotate the contents of the image; neither of those two steps are straightforward for objects not present in the image. We overcome this challenge by operating in the opposite direction: we start with an image of an object in context from web, and then remove that object from the image via inpainting. This automated pipeline converts unstructured web data into a dataset comprising pairs of images with/without the object. Using this, we collect a novel dataset, with ${\sim}1.3$M images across $9$ object categories, and train a SP prediction model called CLIP-UNet. CLIP-UNet outperforms existing VLMs and baselines that combine semantic priors with object detectors on real-world and simulated images. In our user studies, we find that the SP masks predicted by CLIP-UNet are favored $43.7\%$ and $31.3\%$ times when comparing against the $4$ SP baselines on real and simulated images. In addition, we demonstrate leveraging SP mask predictions from CLIP-UNet enables downstream applications like building tidying robots in indoor environments.

• The paper presents a novel semantic placement task that uses inpainting to create a 1.3M image dataset for training robust models.
• The methodology employs a CLIP-UNet architecture that significantly improves human preference ratings and placement precision over existing vision-language models.
• The work demonstrates practical benefits by enabling effective object placement in simulated robotics, paving the way for future integration of spatial awareness in real-world applications.

An Overview of "Seeing the Unseen: Visual Common Sense for Semantic Placement"

"Seeing the Unseen: Visual Common Sense for Semantic Placement" explores a novel area in computer vision focused on understanding visually common-sense tasks, specifically tackling Semantic Placement (SP). This problem involves predicting plausible locations for an object within an image where it is not currently present. This task departs from traditional computer vision objectives, which typically classify or describe visible elements in an image. Instead, SP requires reasoning about visual elements that could be part of the scene under different contexts. The potential applications for such a task are significant; it can enhance assistive robotics, improve augmented reality (AR) rendering, and empower visually-grounded dialogue systems with a more nuanced understanding of everyday visual contexts.

Methodology

The central challenge for Semantic Placement is the absence of datasets since traditional image collection methods are ineffective for objects not visible in scenes. To address this, the authors propose a novel data collection strategy that inverts the typical dataset generation process: they start with images that do include the object and then use inpainting techniques to remove it. This synthesis results in a dataset of approximately 1.3 million images across nine object categories, collected using automated techniques involving open-vocab object detectors and inpainting models. The resulting dataset supports the training of an SP model named CLIP-UNet.

The CLIP-UNet model uses a frozen CLIP visual encoder combined with a U-Net-style decoder to predict SP masks. The model's architecture facilitates capturing the semantic context and making informed predictions about where an object should be placed. Training is performed in two stages: initial pretraining on the comprehensive SP dataset followed by finetuning on a high-quality synthetically generated dataset, designed to overcome potential bias from the inpainting process.

Evaluation and Results

The evaluation of the model highlights several metrics: human preference for predicted placements, precision in predicting accurate target placements, and alignment with receptacle surface priors. The CLIP-UNet model showed a notable improvement in performance, with specific gains found in user preference tests and predicting high-quality placements over established baselines like LLaVA and GPT4V, both vision-LLMs.

In practical terms, the work demonstrated the utility of SP in enabling a mobile manipulator robot to perform the task of identifying and executing object placements in a simulated realistic environment. Here, the integration of SP predictions delivered a 12.5% success rate in correctly placing objects across various indoor scenes.

Implications and Future Work

This work's implications are extensive. By allowing systems to reason about the unseen, assistive robots and AR devices could offer sophisticated, context-aware interactions. However, challenges remain, particularly as the current model does not account for embodiment-specific limitations, potentially degrading performance in situational contexts requiring fine-grained interaction capabilities.

Future work could focus on extending this model to more general settings and integrating spatial and geometric awareness into the prediction framework. Additionally, refining methods to better account for realtime and physical interaction scenarios would enhance the system's applicability in practical assistive technologies.

Overall, "Seeing the Unseen" explores an innovative fusion of vision and LLMs to tackle a challenging, underexplored domain in computational understanding. These advances not only make strides in artificial agents' capabilities but also prompt further research into modeling unseen or potential configurations in complex environments.