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1. Hierarchical Grouping of Simple Visual Scenes

   Rice, P. J.; Mao, L.; Zhu, A.; Wu, E.' & (January 2023, Proceedings of the Forty-Fifth Annual Meeting of the Cognitive Science Society)

   Human visual grouping processes consolidate independent visual objects into grouped visual features on the basis of shared characteristics; these visual features can themselves be grouped, resulting in a hierarchical representation of visual grouping information. This “grouping hierarchy“ promotes ef- ficient attention in the support of goal-directed behavior, but improper grouping of elements of a visual scene can also re- sult in critical behavioral errors. Understanding of how visual object/features characteristics such as size and form influences perception of hierarchical visual groups can further theory of human visual grouping behavior and contribute to effective in- terface design. In the present study, participants provided free- response groupings of a set of stimuli that contained consistent structural relationships between a limited set of visual features. These grouping patterns were evaluated for relationships be- tween specific characteristics of the constituent visual features and the distribution of features across levels of the indicated grouping hierarchy. We observed that while the relative size of the visual features differentiated groupings across levels of the grouping hierarchy, the form of visual objects and features was more likely to distinguish separate groups within a partic- ular level of hierarchy. These consistent relationships between visual feature characteristics and placement within a grouping hierarchy can be leveraged to advance computational theories of human visual grouping behavior, which can in turn be ap- plied to effective design for interfaces such as voter ballots. 

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2. GARField: Group Anything with Radiance Fields

   Kim, Chung_Min; Wu, Mingxuan; Kerr, Justin; Goldberg, Ken; Tancik, Matthew; Kanazawa, Angjoo (June 2024, CVPR)

   Grouping is inherently ambiguous due to the multiple levels of granularity in which one can decompose a scene -- should the wheels of an excavator be considered separate or part of the whole? We present Group Anything with Radiance Fields (GARField), an approach for decomposing 3D scenes into a hierarchy of semantically meaningful groups from posed image inputs. To do this we embrace group ambiguity through physical scale: by optimizing a scale-conditioned 3D affinity feature field, a point in the world can belong to different groups of different sizes. We optimize this field from a set of 2D masks provided by Segment Anything (SAM) in a way that respects coarse-to-fine hierarchy, using scale to consistently fuse conflicting masks from different viewpoints. From this field we can derive a hierarchy of possible groupings via automatic tree construction or user interaction. We evaluate GARField on a variety of in-the-wild scenes and find it effectively extracts groups at many levels: clusters of objects, objects, and various subparts. GARField inherently represents multi-view consistent groupings and produces higher fidelity groups than the input SAM masks. GARField's hierarchical grouping could have exciting downstream applications such as 3D asset extraction or dynamic scene understanding. See the project website at https://www.garfield.studio/ 

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3. Supervised learning of large perceptual organization: graph spectral partitioning and learning automata

   https://doi.org/10.1109/34.857006  

   Sarkar, S.; Soundararajan, P. (May 2000, IEEE Transactions on Pattern Analysis and Machine Intelligence)

   Perceptual organization offers an elegant framework to group low-level features that are likely to come from a single object. We offer a novel strategy to adapt this grouping process to objects in a domain. Given a set of training images of objects in context, the associated learning process decides on the relative importance of the basic salient relationships such as proximity, parallelness, continuity, junctions, and common region toward segregating the objects from the background. The parameters of the grouping process are cast as probabilistic specifications of Bayesian networks that need to be learned. This learning is accomplished using a team of stochastic automata in an N-player cooperative game framework. The grouping process, which is based on graph partitioning is able to form large groups from relationships defined over a small set of primitives and is fast. We statistically demonstrate the robust performance of the grouping and the learning frameworks on a variety of real images. Among the interesting conclusions is the significant role of photometric attributes in grouping and the ability to form large salient groups from a set of local relations, each defined over a small number of primitives. 

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4. Clustering Social Touch Gestures for Human-Robot Interaction

   Abou_Chahine, Ramzi; Vasquez, Steven; Fazli, Pooyan; Seifi, Hasti (December 2023, Springer-Verlag)

   Social touch provides a rich non-verbal communication channel between humans and robots. Prior work has identified a set of touch gestures for human-robot interaction and described them with natural language labels (e.g., stroking, patting). Yet, no data exists on the semantic relationships between the touch gestures in users’ minds. To endow robots with touch intelligence, we investigated how people perceive the similarities of social touch labels from the literature. In an online study, 45 participants grouped 36 social touch labels based on their perceived similarities and annotated their groupings with descriptive names. We derived quantitative similarities of the gestures from these groupings and analyzed the similarities using hierarchical clustering. The analysis resulted in 9 clusters of touch gestures formed around the social, emotional, and contact characteristics of the gestures. We discuss the implications of our results for designing and evaluating touch sensing and interactions with social robots. 

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5. Hierarchical and nonhierarchical features of the mouse visual cortical network

   https://doi.org/10.1038/s41467-022-28035-y  

   D’Souza, Rinaldo D.; Wang, Quanxin; Ji, Weiqing; Meier, Andrew M.; Kennedy, Henry; Knoblauch, Kenneth; Burkhalter, Andreas (December 2022, Nature Communications)

   Abstract Neocortical computations underlying vision are performed by a distributed network of functionally specialized areas. Mouse visual cortex, a dense interareal network that exhibits hierarchical properties, comprises subnetworks interconnecting distinct processing streams. To determine the layout of the mouse visual hierarchy, we have evaluated the laminar patterns formed by interareal axonal projections originating in each of ten areas. Reciprocally connected pairs of areas exhibit feedforward/feedback relationships consistent with a hierarchical organization. Beta regression analyses, which estimate a continuous hierarchical distance measure, indicate that the network comprises multiple nonhierarchical circuits embedded in a hierarchical organization of overlapping levels. Single-unit recordings in anaesthetized mice show that receptive field sizes are generally consistent with the hierarchy, with the ventral stream exhibiting a stricter hierarchy than the dorsal stream. Together, the results provide an anatomical metric for hierarchical distance, and reveal both hierarchical and nonhierarchical motifs in mouse visual cortex. 

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