5 citations · 15 across the 14 of their papers we have counts for
14 papers
D3: Data Diversity Design for Systematic Generalization in Visual Question Answering
Amir Rahimi, Vanessa D'Amario, Moyuru Yamada +3
Systematic generalization is a crucial aspect of intelligence, which refers to the ability to generalize to novel tasks by combining known subtasks and concepts. One critical facto…
Modularity Trumps Invariance for Compositional Robustness
Ian Mason, Anirban Sarkar, Tomotake Sasaki +1
By default neural networks are not robust to changes in data distribution. This has been demonstrated with simple image corruptions, such as blurring or adding noise, degrading ima…
HICO-DET-SG and V-COCO-SG: New Data Splits for Evaluating the Systematic Generalization Performance of Human-Object Interaction Detection Models
Kentaro Takemoto, Moyuru Yamada, Tomotake Sasaki +1
Human-Object Interaction (HOI) detection is a task to localize humans and objects in an image and predict the interactions in human-object pairs. In real-world scenarios, HOI detec…
Deephys: Deep Electrophysiology, Debugging Neural Networks under Distribution Shifts
Anirban Sarkar, Matthew Groth, Ian Mason +2
Deep Neural Networks (DNNs) often fail in out-of-distribution scenarios. In this paper, we introduce a tool to visualize and understand such failures. We draw inspiration from conc…
Do Neural Networks for Segmentation Understand Insideness?
Kimberly Villalobos, Vilim Štih, Amineh Ahmadinejad +6
The insideness problem is an aspect of image segmentation that consists of determining which pixels are inside and outside a region. Deep Neural Networks (DNNs) excel in segmentati…
Symmetry Perception by Deep Networks: Inadequacy of Feed-Forward Architectures and Improvements with Recurrent Connections
Shobhita Sundaram, Darius Sinha, Matthew Groth +2
Symmetry is omnipresent in nature and perceived by the visual system of many species, as it facilitates detecting ecologically important classes of objects in our environment. Symm…
Transformer Module Networks for Systematic Generalization in Visual Question Answering
Moyuru Yamada, Vanessa D'Amario, Kentaro Takemoto +2
Transformers achieve great performance on Visual Question Answering (VQA). However, their systematic generalization capabilities, i.e., handling novel combinations of known concept…
Three approaches to facilitate DNN generalization to objects in out-of-distribution orientations and illuminations
Akira Sakai, Taro Sunagawa, Spandan Madan +7
The training data distribution is often biased towards objects in certain orientations and illumination conditions. While humans have a remarkable capability of recognizing objects…
Annotation Cost Reduction of Stream-based Active Learning by Automated Weak Labeling using a Robot Arm
Kanata Suzuki, Taro Sunagawa, Tomotake Sasaki +1
Stream-based active learning (AL) is an efficient training data collection method, and it is used to reduce human annotation cost required in machine learning. However, it is diffi…
Emergent Neural Network Mechanisms for Generalization to Objects in Novel Orientations
Avi Cooper, Xavier Boix, Daniel Harari +4
The capability of Deep Neural Networks (DNNs) to recognize objects in orientations outside the distribution of the training data is not well understood. We present evidence that DN…
The Foes of Neural Network's Data Efficiency Among Unnecessary Input Dimensions
Vanessa D'Amario, Sanjana Srivastava, Tomotake Sasaki +1
Datasets often contain input dimensions that are unnecessary to predict the output label, e.g. background in object recognition, which lead to more trainable parameters. Deep Neura…
In-distribution adversarial attacks on object recognition models using gradient-free search
Spandan Madan, Tomotake Sasaki, Hanspeter Pfister +2
Neural networks are susceptible to small perturbations in the form of 2D rotations and shifts, image crops, and even changes in object colors. Past works attribute these errors to…
How Modular Should Neural Module Networks Be for Systematic Generalization?
Vanessa D'Amario, Tomotake Sasaki, Xavier Boix
Neural Module Networks (NMNs) aim at Visual Question Answering (VQA) via composition of modules that tackle a sub-task. NMNs are a promising strategy to achieve systematic generali…
When and how CNNs generalize to out-of-distribution category-viewpoint combinations
Spandan Madan, Timothy Henry, Jamell Dozier +6
Object recognition and viewpoint estimation lie at the heart of visual understanding. Recent works suggest that convolutional neural networks (CNNs) fail to generalize to out-of-di…