242 citations · 1.5k across the 60 of their papers we have counts for
9 papers · 1 filter
Natural Language Descriptions of Deep Visual Features
Evan Hernandez, Sarah Schwettmann, David Bau +3
Some neurons in deep networks specialize in recognizing highly specific perceptual, structural, or semantic features of inputs. In computer vision, techniques exist for identifying…
Toward a Visual Concept Vocabulary for GAN Latent Space
Sarah Schwettmann, Evan Hernandez, David Bau +3
A large body of recent work has identified transformations in the latent spaces of generative adversarial networks (GANs) that consistently and interpretably transform generated im…
Subspace Regularizers for Few-Shot Class Incremental Learning
Afra Feyza Akyürek, Ekin Akyürek, Derry Tanti Wijaya +1
Few-shot class incremental learning -- the problem of updating a trained classifier to discriminate among an expanded set of classes with limited labeled data -- is a key challenge…
Joint Modeling of Chest Radiographs and Radiology Reports for Pulmonary Edema Assessment
Geeticka Chauhan, Ruizhi Liao, William Wells +6
We propose and demonstrate a novel machine learning algorithm that assesses pulmonary edema severity from chest radiographs. While large publicly available datasets of chest radiog…
Explainable Neural Computation via Stack Neural Module Networks
Ronghang Hu, Jacob Andreas, Trevor Darrell +1
In complex inferential tasks like question answering, machine learning models must confront two challenges: the need to implement a compositional reasoning process, and, in many ap…
Speaker-Follower Models for Vision-and-Language Navigation
Daniel Fried, Ronghang Hu, Volkan Cirik +7
Navigation guided by natural language instructions presents a challenging reasoning problem for instruction followers. Natural language instructions typically identify only a few h…
Learning to Reason: End-to-End Module Networks for Visual Question Answering
Ronghang Hu, Jacob Andreas, Marcus Rohrbach +2
Natural language questions are inherently compositional, and many are most easily answered by reasoning about their decomposition into modular sub-problems. For example, to answer…
Modeling Relationships in Referential Expressions with Compositional Modular Networks
Ronghang Hu, Marcus Rohrbach, Jacob Andreas +2
People often refer to entities in an image in terms of their relationships with other entities. For example, "the black cat sitting under the table" refers to both a "black cat" en…
Neural Module Networks
Jacob Andreas, Marcus Rohrbach, Trevor Darrell +1
Visual question answering is fundamentally compositional in nature---a question like "where is the dog?" shares substructure with questions like "what color is the dog?" and "where…