@proceedings {4261, title = {Draping an Elephant: Uncovering Children{\textquoteright}s Reasoning About Cloth-Covered Objects}, year = {2019}, month = {07/2019}, address = {Montreal, Canada}, abstract = {

Humans have an intuitive understanding of physics. They can predict how a physical scene will unfold, and reason about how it came to be. Adults may rely on such a physical representation for visual reasoning and recognition, going beyond visual features and capturing objects in terms of their physical properties. Recently, the use of draped objects in recognition was used to examine adult object representations in the absence of many common visual features. In this paper we examine young children{\textquoteright}s reasoning about draped objects in order to examine the develop of physical object representation. In addition, we argue that a better understanding of the development of the concept of cloth as a physical entity is worthwhile in and of itself, as it may form a basic ontological category in intuitive physical reasoning akin to liquids and solids. We use two experiments to investigate young children{\textquoteright}s (ages 3{\textendash}5) reasoning about cloth-covered objects, and find that they perform significantly above chance (though far from perfectly) indicating a representation of physical objects that can interact dynamically with the world. Children{\textquoteright}s success and failure pattern is similar across the two experiments, and we compare it to adult behavior. We find a small effect, which suggests the specific features that make reasoning about certain objects more difficult may carry into adulthood.

}, keywords = {analysis-by-synthesis, cloth, cognitive development, imagination, intuitive physics, object recognition, occlusion, perception, vision}, url = {https://mindmodeling.org/cogsci2019/papers/0506/index.html}, author = {Tomer D Ullman and Eliza Kosoy and Ilker Yildirim and Amir Arsalan Soltani and Max Siegel and Joshua B. Tenenbaum and Elizabeth S Spelke} } @article {4294, title = {Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?}, journal = {bioRxiv preprint}, year = {2018}, abstract = {

The internal representations of early deep artificial neural networks (ANNs) were found to be remarkably similar to the internal neural representations measured experimentally in the primate brain. Here we ask, as deep ANNs have continued to evolve, are they becoming more or less brain-like? ANNs that are most functionally similar to the brain will contain mechanisms that are most like those used by the brain. We therefore developed Brain-Score {\textendash} a composite of multiple neural and behavioral benchmarks that score any ANN on how similar it is to the brain{\textquoteright}s mechanisms for core object recognition {\textendash} and we deployed it to evaluate a wide range of state-of-the-art deep ANNs. Using this scoring system, we here report that: (1) DenseNet-169, CORnet-S and ResNet-101 are the most brain-like ANNs. There remains considerable variability in neural and behavioral responses that is not predicted by any ANN, suggesting that no ANN model has yet captured all the relevant mechanisms. (3) Extending prior work, we found that gains in ANN ImageNet performance led to gains on Brain-Score. However, correlation weakened at >= 70\% top-1 ImageNet performance, suggesting that additional guidance from neuroscience is needed to make further advances in capturing brain mechanisms. (4) We uncovered smaller (i.e. less complex) ANNs that are more brain-like than many of the best-performing ImageNet models, which suggests the opportunity to simplify ANNs to better understand the ventral stream. The scoring system used here is far from complete. However, we propose that evaluating and tracking model-benchmark correspondences through a Brain-Score that is regularly updated with new brain data is an exciting opportunity: experimental benchmarks can be used to guide machine network evolution, and machine networks are mechanistic hypotheses of the brain{\textquoteright}s network and thus drive next experiments. To facilitate both of these, we release Brain-Score.org: a platform that hosts the neural and behavioral benchmarks, where ANNs for visual processing can be submitted to receive a Brain-Score and their rank relative to other models, and where new experimental data can be naturally incorporated.

}, keywords = {computational neuroscience, deep learning, Neural Networks, object recognition, ventral stream}, doi = {10.1101/407007}, url = {https://www.biorxiv.org/content/10.1101/407007v1}, author = {Martin Schrimpf and Jonas Kubilius}, editor = {Ha Hong and Najib J. Majaj and Rishi Rajalingham and Elias B. Issa and Kohitij Kar and Pouya Bashivan and Jonathan Prescott-Roy and Kailyn Schmidt and Daniel L K Yamins and James J. DiCarlo} } @article {3517, title = {What is changing when: decoding visual information in movies from human intracranial recordings}, journal = {NeuroImage}, volume = {180, Part A}, year = {2018}, month = {10/2018}, pages = {147-159}, abstract = {

The majority of visual recognition studies have focused on the neural responses to repeated presentations of static stimuli with abrupt and well-defined onset and offset times. In contrast, natural vision involves unique renderings of visual inputs that are continuously changing without explicitly defined temporal transitions. Here we considered commercial movies as a coarse proxy to natural vision. We recorded intracranial field potential signals from 1,284 electrodes implanted in 15 patients with epilepsy while the subjects passively viewed commercial movies. We could rapidly detect large changes in the visual inputs within approximately 100 ms of their occurrence, using exclusively field potential signals from ventral visual cortical areas including the inferior temporal gyrus and inferior occipital gyrus. Furthermore, we could decode the content of those visual changes even in a single movie presentation, generalizing across the wide range of transformations present in a movie. These results present a methodological framework for studying cognition during dynamic and natural vision.

}, keywords = {Electrocorticography (ECoG), Movies, Natural vision, neural decoding, object recognition, Ventral pathway}, doi = {10.1016/j.neuroimage.2017.08.027}, url = {https://www.sciencedirect.com/science/article/pii/S1053811917306742}, author = {Leyla Isik and Jedediah Singer and Nancy Kanwisher and Madsen JR and Anderson WS and Gabriel Kreiman} } @article {1760, title = {Atoms of recognition in human and computer vision}, journal = {PNAS }, volume = {113}, year = {2016}, month = {03/2016}, pages = {2744{\textendash}2749}, abstract = {
Discovering the visual features and representations used by thebrain to recognize objects is a central problem in the study of vision. Recently, neural network models of visual object recognition, including biological and deep network models, have shown remarkableprogress and have begun to rival human performance in some challenging tasks. These models are trained on image examples andlearn to extract features and representations and to use them for categorization. It remains unclear, however, whether the representations and learning processes discovered by current models aresimilar to those used by the human visual system. Here we show,by introducing and using minimal recognizable images, that thehuman visual system uses features and processes that are not usedby current models and that are critical for recognition. We found bypsychophysical studies that at the level of minimal recognizableimages a minute change in the image can have a drastic effect onrecognition, thus identifying features that are critical for the task.Simulations then showed that current models cannot explain thissensitivity to precise feature configurations and, more generally,do not learn to recognize minimal images at a human level. The roleof the features shown here is revealed uniquely at the minimal level, where the contribution of each feature is essential. A full understanding of the learning and use of such features will extend ourunderstanding of visual recognition and its cortical mechanisms andwill enhance the capacity of computational models to learn fromvisual experience and to deal with recognition and detailedimage interpretation.
}, keywords = {Computer vision, minimal images, object recognition, visual perception, visual representations}, issn = {1091-6490}, doi = {10.1073/pnas.1513198113}, url = {http://www.pnas.org/content/113/10/2744.abstract}, author = {Shimon Ullman and Liav Assif and Eitan Fetaya and Daniel Harari} } @article {385, title = {Object recognition data sets (iCub/IIT)}, year = {2013}, month = {05/2013}, abstract = {

Data set for object recognition and categorization. 10 categories, 40 objects for the training phase. The acquisition size is 640{\texttimes}480 and subsequently cropped to the bounding box of the object according to the kinematics or motion cue. The bounding box is 160{\texttimes}160 in human mode and 320{\texttimes}320 in robot mode. For each object we provide 200 training samples. Each category is trained with 3 objects (600 examples per category).

Click HERE to Download Dataset from IIT website \>

Publications

Fanello, S.R.; Ciliberto, C.; Santoro, M.; Natale, L.; Metta, G.; Rosasco, L.; Odone, F.,{\textquotedblright}iCub World: Friendly Robots Help Building Good Vision Data-Sets,{\textquotedblright} In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPR), 2013

Fanello, S. R.; Ciliberto, C.; Natale, L.; Metta, G., {\textquotedblleft}Weakly Supervised Strategies for Natural Object Recognition in Robotics,{\textquotedblright} IEEE International Conference on Robotics and Automation (ICRA). Karlsruhe, Germany, May 6-10, 2013

Fanello, S.R.; Noceti, N.; Metta, G.; Odone, F., {\textquotedblleft}Multi-Class Image Classification: Sparsity Does It Better,{\textquotedblright} International Conference on Computer Vision Theory and Applications (VISAPP), 2013

Ciliberto C.; Smeraldi F.; Natale L.; Metta G., {\textquotedblleft}Online Multiple Instance Learning Applied to Hand Detection in a Humanoid Robot,{\textquotedblright} IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2011). San Francisco, California, USA, September 25-30, 2011

}, keywords = {Computer vision, object recognition, robotics}, author = {Lorenzo Rosasco} }