Incremental Object Learning from Contiguous Views

Stefan Stojanov1, Samarth Mishra*1, Ngoc Anh Thai*1, Nikhil Dhanda1, Ahmad Humayun1, Chen Yu2, Linda B. Smith2, James M. Rehg1
1Georgia Institute of Technology
2Indiana University
*Equal contribution


In this work, we present CRIB (Continual Recognition Inspired by Babies), a synthetic incremental object learning environment that can produce data that models visual imagery produced by object exploration in early infancy. CRIB is coupled with a new 3D object dataset, Toys-200, that contains 200 unique toy-like object instances, and is also compatible with existing 3D datasets. Through extensive empirical evaluation of state-of-the-art incremental learning algorithms, we find the novel empirical result that repetition can significantly ameliorate the effects of catastrophic forgetting. Furthermore, we find that in certain cases repetition allows for performance approaching that of batch learning algorithms. Finally, we propose an unsupervised incremental learning task with intriguing baseline results.

Repeated Exposure Ameliorates Catastrophic Forgetting

In general, incremental learning algorithms only see one concept at a time and never again, usually leading to catastrophic forgetting. Using CRIB to repeatedly sample the concept class of object instances, we find that repeated exposure to object instances leads to a gradual improvement in accuracy that eventually reaches batch performance.

CRIB Learning Environment Samples

Instance Learning Exposures

Plane Cup Truck Bee Spaceship

Instance Testing Image Data

CRIB is a data generator accessible using a simple Python API that given a query for instance id and sequence length, renders a video clip of a smoothly rotating object overlaid on a dynamic background. Data is returned as 3xHxW array and can be saved to disk or directly used as input data for training. Each generated clip has randomized lighting, object scale and object rotation trajectory. This allows for virtually unlimited data.

Incremental Learning Without Supervision is not Solved