Analyzing Visual Representations in Embodied Navigation Tasks

• URL: http://arxiv.org/abs/2003.05993v1
• Date: Thu, 12 Mar 2020 19:43:59 GMT
• Title: Analyzing Visual Representations in Embodied Navigation Tasks
• Authors: Erik Wijmans, Julian Straub, Dhruv Batra, Irfan Essa, Judy Hoffman, Ari Morcos
• Abstract summary: We use the recently proposed projection weighted Canonical Correlation Analysis (PWCCA) to measure the similarity of visual representations learned in the same environment by performing different tasks. We then empirically demonstrate that visual representations learned on one task can be effectively transferred to a different task.
• Score: 45.35107294831313
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in deep reinforcement learning require a large amount of training data and generally result in representations that are often over specialized to the target task. In this work, we present a methodology to study the underlying potential causes for this specialization. We use the recently proposed projection weighted Canonical Correlation Analysis (PWCCA) to measure the similarity of visual representations learned in the same environment by performing different tasks. We then leverage our proposed methodology to examine the task dependence of visual representations learned on related but distinct embodied navigation tasks. Surprisingly, we find that slight differences in task have no measurable effect on the visual representation for both SqueezeNet and ResNet architectures. We then empirically demonstrate that visual representations learned on one task can be effectively transferred to a different task.

Related papers

• Semantic-Based Active Perception for Humanoid Visual Tasks with Foveal Sensors [49.99728312519117]
  The aim of this work is to establish how accurately a recent semantic-based active perception model is able to complete visual tasks that are regularly performed by humans. This model exploits the ability of current object detectors to localize and classify a large number of object classes and to update a semantic description of a scene across multiple fixations. In the task of scene exploration, the semantic-based method demonstrates superior performance compared to the traditional saliency-based model.
  arXiv  Detail & Related papers  (2024-04-16T18:15:57Z)
• Distribution Matching for Multi-Task Learning of Classification Tasks: a Large-Scale Study on Faces & Beyond [62.406687088097605]
  Multi-Task Learning (MTL) is a framework, where multiple related tasks are learned jointly and benefit from a shared representation space. We show that MTL can be successful with classification tasks with little, or non-overlapping annotations. We propose a novel approach, where knowledge exchange is enabled between the tasks via distribution matching.
  arXiv  Detail & Related papers  (2024-01-02T14:18:11Z)
• SeMAIL: Eliminating Distractors in Visual Imitation via Separated Models [22.472167814814448]
  We propose a new model-based imitation learning algorithm named Separated Model-based Adversarial Imitation Learning (SeMAIL) Our method achieves near-expert performance on various visual control tasks with complex observations and the more challenging tasks with different backgrounds from expert observations.
  arXiv  Detail & Related papers  (2023-06-19T04:33:44Z)
• A Dynamic Feature Interaction Framework for Multi-task Visual Perception [100.98434079696268]
  We devise an efficient unified framework to solve multiple common perception tasks. These tasks include instance segmentation, semantic segmentation, monocular 3D detection, and depth estimation. Our proposed framework, termed D2BNet, demonstrates a unique approach to parameter-efficient predictions for multi-task perception.
  arXiv  Detail & Related papers  (2023-06-08T09:24:46Z)
• Task Formulation Matters When Learning Continually: A Case Study in Visual Question Answering [58.82325933356066]
  Continual learning aims to train a model incrementally on a sequence of tasks without forgetting previous knowledge. We present a detailed study of how different settings affect performance for Visual Question Answering.
  arXiv  Detail & Related papers  (2022-09-30T19:12:58Z)
• Self-Supervised Visual Representation Learning Using Lightweight Architectures [0.0]
  In self-supervised learning, a model is trained to solve a pretext task, using a data set whose annotations are created by a machine. We critically examine the most notable pretext tasks to extract features from image data. We study the performance of various self-supervised techniques keeping all other parameters uniform.
  arXiv  Detail & Related papers  (2021-10-21T14:13:10Z)
• Glimpse-Attend-and-Explore: Self-Attention for Active Visual Exploration [47.01485765231528]
  Active visual exploration aims to assist an agent with a limited field of view to understand its environment based on partial observations. We propose the Glimpse-Attend-and-Explore model which employs self-attention to guide the visual exploration instead of task-specific uncertainty maps. Our model provides encouraging results while being less dependent on dataset bias in driving the exploration.
  arXiv  Detail & Related papers  (2021-08-26T11:41:03Z)
• Learning Task Informed Abstractions [10.920599910769276]
  We propose learning Task Informed Abstractions (TIA) that explicitly separates reward-correlated visual features from distractors. TIA leads to significant performance gains over state-of-the-art methods on many visual control tasks.
  arXiv  Detail & Related papers  (2021-06-29T17:56:11Z)
• Multi-Task Learning for Dense Prediction Tasks: A Survey [87.66280582034838]
  Multi-task learning (MTL) techniques have shown promising results w.r.t. performance, computations and/or memory footprint. We provide a well-rounded view on state-of-the-art deep learning approaches for MTL in computer vision.
  arXiv  Detail & Related papers  (2020-04-28T09:15:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.