168 citations · 212 across the 12 of their papers we have counts for
12 papers
Benchmarking Out-of-Distribution Generalization Capabilities of DNN-based Encoding Models for the Ventral Visual Cortex
Spandan Madan, Will Xiao, Mingran Cao +3
We characterized the generalization capabilities of DNN-based encoding models when predicting neuronal responses from the visual cortex. We collected \textit{MacaqueITBench}, a lar…
The Indoor-Training Effect: unexpected gains from distribution shifts in the transition function
Serena Bono, Spandan Madan, Ishaan Grover +4
Is it better to perform tennis training in a pristine indoor environment or a noisy outdoor one? To model this problem, here we investigate whether shifts in the transition probabi…
Can Machines Imitate Humans? Integrative Turing-like tests for Language and Vision Demonstrate a Narrowing Gap
Mengmi Zhang, Elisa Pavarino, Xiao Liu +20
As AI becomes increasingly embedded in daily life, ascertaining whether an agent is human is critical. We systematically benchmark AI's ability to imitate humans in three language…
Improving generalization by mimicking the human visual diet
Spandan Madan, You Li, Mengmi Zhang +2
We present a new perspective on bridging the generalization gap between biological and computer vision -- mimicking the human visual diet. While computer vision models rely on inte…
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…
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…
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…
When Pigs Fly: Contextual Reasoning in Synthetic and Natural Scenes
Philipp Bomatter, Mengmi Zhang, Dimitar Karev +3
Context is of fundamental importance to both human and machine vision; e.g., an object in the air is more likely to be an airplane than a pig. The rich notion of context incorporat…
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…
Synthetically Trained Icon Proposals for Parsing and Summarizing Infographics
Spandan Madan, Zoya Bylinskii, Matthew Tancik +6
Widely used in news, business, and educational media, infographics are handcrafted to effectively communicate messages about complex and often abstract topics including `ways to co…
Understanding Infographics through Textual and Visual Tag Prediction
Zoya Bylinskii, Sami Alsheikh, Spandan Madan +5
We introduce the problem of visual hashtag discovery for infographics: extracting visual elements from an infographic that are diagnostic of its topic. Given an infographic as inpu…
Learning Visual Importance for Graphic Designs and Data Visualizations
Zoya Bylinskii, Nam Wook Kim, Peter O'Donovan +6
Knowing where people look and click on visual designs can provide clues about how the designs are perceived, and where the most important or relevant content lies. The most importa…