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Neural Shape Parsers for Constructive Solid Geometry
Gopal Sharma, Rishabh Goyal, Difan Liu +2
Constructive Solid Geometry (CSG) is a geometric modeling technique that defines complex shapes by recursively applying boolean operations on primitives such as spheres and cylinde…
Learning Point Embeddings from Shape Repositories for Few-Shot Segmentation
Gopal Sharma, Evangelos Kalogerakis, Subhransu Maji
User generated 3D shapes in online repositories contain rich information about surfaces, primitives, and their geometric relations, often arranged in a hierarchy. We present a fram…
When Does Self-supervision Improve Few-shot Learning?
Jong-Chyi Su, Subhransu Maji, Bharath Hariharan
We investigate the role of self-supervised learning (SSL) in the context of few-shot learning. Although recent research has shown the benefits of SSL on large unlabeled datasets, i…
Visualizing and Describing Fine-grained Categories as Textures
Tsung-Yu Lin, Mikayla Timm, Chenyun Wu +1
We analyze how categories from recent FGVC challenges can be described by their textural content. The motivation is that subtle differences between species of birds or butterflies…
Boosting Supervision with Self-Supervision for Few-shot Learning
Jong-Chyi Su, Subhransu Maji, Bharath Hariharan
We present a technique to improve the transferability of deep representations learned on small labeled datasets by introducing self-supervised tasks as auxiliary loss functions. Wh…
Inferring 3D Shapes from Image Collections using Adversarial Networks
Matheus Gadelha, Aartika Rai, Subhransu Maji +1
We investigate the problem of learning a probabilistic distribution over three-dimensional shapes given two-dimensional views of multiple objects taken from unknown viewpoints. Our…
Meta-Learning with Differentiable Convex Optimization
Kwonjoon Lee, Subhransu Maji, Avinash Ravichandran +1
Many meta-learning approaches for few-shot learning rely on simple base learners such as nearest-neighbor classifiers. However, even in the few-shot regime, discriminatively traine…
A Bayesian Perspective on the Deep Image Prior
Zezhou Cheng, Matheus Gadelha, Subhransu Maji +1
The deep image prior was recently introduced as a prior for natural images. It represents images as the output of a convolutional network with random inputs. For "inference", gradi…
Active Adversarial Domain Adaptation
Jong-Chyi Su, Yi-Hsuan Tsai, Kihyuk Sohn +3
We propose an active learning approach for transferring representations across domains. Our approach, active adversarial domain adaptation (AADA), explores a duality between two re…
Task2Vec: Task Embedding for Meta-Learning
Alessandro Achille, Michael Lam, Rahul Tewari +5
We introduce a method to provide vectorial representations of visual classification tasks which can be used to reason about the nature of those tasks and their relations. Given a d…