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1. Scene layout priming relies primarily on low-level features rather than scene layout

   https://doi.org/10.1167/19.1.14  

   Shafer-Skelton, Anna ; Brady, Timothy F. ( January 2019 , Journal of Vision)
2. Scene-selective brain regions respond to embedded objects of a scene

   https://doi.org/10.1093/cercor/bhac399  

   Aminoff, Elissa M. ; Durham, Tess ( October 2022 , Cerebral Cortex)

   Objects are fundamental to scene understanding. Scenes are defined by embedded objects and how we interact with them. Paradoxically, scene processing in the brain is typically discussed in contrast to object processing. Using the BOLD5000 dataset (Chang et al., 2019), we examined whether objects within a scene predicted the neural representation of scenes, as measured by functional magnetic resonance imaging in humans. Stimuli included 1,179 unique scenes across 18 semantic categories. Object composition of scenes were compared across scene exemplars in different semantic scene categories, and separately, in exemplars of the same scene category. Neural representations in scene- and object-preferring brain regions were significantly related to which objects were in a scene, with the effect at times stronger in the scene-preferring regions. The object model accounted for more variance when comparing scenes within the same semantic category to scenes from different categories. Here, we demonstrate the function of scene-preferring regions includes the processing of objects. This suggests visual processing regions may be better characterized by the processes, which are engaged when interacting with the stimulus kind, such as processing groups of objects in scenes, or processing a single object in our foreground, rather than the stimulus kind itself.

    

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3. Reasoning With Scene Graphs for Robot Planning Under Partial Observability

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   Amiri, Saeid ; Chandan, Kishan ; Zhang, Shiqi ( April 2022 , IEEE Robotics and Automation Letters)
4. SceneCraft: Automating Interactive Narrative Scene Generation in Digital Games with Large Language Models

   https://doi.org/10.1609/aiide.v19i1.27504  

   Kumaran, Vikram ; Rowe, Jonathan ; Mott, Bradford ; Lester, James ( October 2023 , Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment)

   Creating engaging interactive story-based experiences dynamically responding to individual player choices poses significant challenges for narrative-centered games. Recent advances in pre-trained large language models (LLMs) have the potential to revolutionize procedural content generation for narrative-centered games. Historically, interactive narrative generation has specified pivotal events in the storyline, often utilizing planning-based approaches toward achieving narrative coherence and maintaining the story arc. However, manual authorship is typically used to create detail and variety in non-player character (NPC) interaction to specify and instantiate plot events. This paper proposes SCENECRAFT, a narrative scene generation framework that automates NPC interaction crucial to unfolding plot events. SCENECRAFT interprets natural language instructions about scene objectives, NPC traits, location, and narrative variations. It then employs large language models to generate game scenes aligned with authorial intent. It generates branching conversation paths that adapt to player choices while adhering to the author’s interaction goals. LLMs generate interaction scripts, semantically extract character emotions and gestures to align with the script, and convert dialogues into a game scripting language. The generated script can then be played utilizing an existing narrative-centered game framework. Through empirical evaluation using automated and human assessments, we demonstrate SCENECRAFT’s effectiveness in creating narrative experiences based on creativity, adaptability, and alignment with intended author instructions.

    

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5. A 34-FPS 698-GOP/s/W Binarized Deep Neural Network-based Natural Scene Text Interpretation Accelerator for Mobile Edge Computing

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   Li, Yixing ; Liu, Zichuan ; Liu, Wenye ; Jiang, Yu ; Wang, Yongliang ; Goh, Wang Ling ; Yu, Hao ; Ren, Fengbo ( October 2018 , IEEE Transactions on Industrial Electronics)