Selective Visual Representations Improve Convergence and Generalization for Embodied AI

Embodied AI models often employ off the shelf vision backbones like CLIP to encode their visual observations. Although such general purpose representations encode rich syntactic and semantic information about the scene, much of this information is often irrelevant to the specific task at hand. This introduces noise within the learning process and distracts the agent's focus from task-relevant visual cues. Inspired by selective attention in humans-the process through which people filter their perception based on their experiences, knowledge, and the task at hand-we introduce a parameter-efficient approach to filter visual stimuli for embodied AI. Our approach induces a task-conditioned bottleneck using a small learnable codebook module. This codebook is trained jointly to optimize task reward and acts as a task-conditioned selective filter over the visual observation. Our experiments showcase state-of-the-art performance for object goal navigation and object displacement across 5 benchmarks, ProcTHOR, ArchitecTHOR, RoboTHOR, AI2-iTHOR, and ManipulaTHOR. The filtered representations produced by the codebook are also able generalize better and converge faster when adapted to other simulation environments such as Habitat. Our qualitative analyses show that agents explore their environments more effectively and their representations retain task-relevant information like target object recognition while ignoring superfluous information about other objects. Code and pretrained models are available at our project website: https://embodied-codebook.github.io.

Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scans

This paper introduces a pipeline to parametrically sample and render multi-task vision datasets from comprehensive 3D scans from the real world. Changing the sampling parameters allows one to "steer" the generated datasets to emphasize specific information. In addition to enabling interesting lines of research, we show the tooling and generated data suffice to train robust vision models. Common architectures trained on a generated starter dataset reached state-of-the-art performance on multiple common vision tasks and benchmarks, despite having seen no benchmark or non-pipeline data. The depth estimation network outperforms MiDaS and the surface normal estimation network is the first to achieve human-level performance for in-the-wild surface normal estimation -- at least according to one metric on the OASIS benchmark. The Dockerized pipeline with CLI, the (mostly python) code, PyTorch dataloaders for the generated data, the generated starter dataset, download scripts and other utilities are available through our project website, https://omnidata.vision.

Puzzle-AE: Novelty Detection in Images through Solving Puzzles

Autoencoder (AE) has proved to be an effective framework for novelty detection. However, they do not typically show promising results on other kinds of real-world datasets, which are exhibiting high intra-class variations, such as CIFAR-10. AEs are not generally able to learn a latent space that solely captures common features of the normal class, resulting in both high false positive and false negative rates due to modeling features that are irrelevant to the normal class. Recently, self-supervised learning has shown great promise in representation learning. To this end, we propose a new AE framework that is trained based on solving puzzles on randomly permuted image patches. Based on this framework, we achieve competitive or superior results compared to SOTA anomaly detection methods on various toy and real-world datasets. Unlike many competitors in this field, the proposed framework is stable, has real-time performance, more general and agnostic to choices of the model hyper-parameters, can work effectively under small sample size settings, and does not require unprincipled early stopping.