Learning task-agnostic representation via toddler-inspired learning

• URL: http://arxiv.org/abs/2101.11221v1
• Date: Wed, 27 Jan 2021 06:26:56 GMT
• Title: Learning task-agnostic representation via toddler-inspired learning
• Abstract summary: We derive inspiration from a highly intentional learning system via action: the toddler. Inspired by the toddler's learning procedure, we design an interactive agent that can learn and store task-agnostic visual representation. Experimental results show that such obtained representation was expandable to various vision tasks.
• Score: 19.478820330574628
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: One of the inherent limitations of current AI systems, stemming from the passive learning mechanisms (e.g., supervised learning), is that they perform well on labeled datasets but cannot deduce knowledge on their own. To tackle this problem, we derive inspiration from a highly intentional learning system via action: the toddler. Inspired by the toddler's learning procedure, we design an interactive agent that can learn and store task-agnostic visual representation while exploring and interacting with objects in the virtual environment. Experimental results show that such obtained representation was expandable to various vision tasks such as image classification, object localization, and distance estimation tasks. In specific, the proposed model achieved 100%, 75.1% accuracy and 1.62% relative error, respectively, which is noticeably better than autoencoder-based model (99.7%, 66.1%, 1.95%), and also comparable with those of supervised models (100%, 87.3%, 0.71%).

Related papers

• Personalized AI Practice Replicates Learning Rate Regularity at Scale [0.0]
  Students exhibit consistent learning rates across diverse educational contexts.<n>We test these findings using a dataset of 1.8 million (366k post-filtering) student interactions from the digital platform Campus AI.
  arXiv  Detail & Related papers  (2026-03-09T13:33:28Z)
• Active Zero: Self-Evolving Vision-Language Models through Active Environment Exploration [72.84714132070404]
  We propose a framework that shifts from passive interaction to active exploration of visual environments.<n>Active-Zero employs three co-evolving agents: a Searcher that retrieves images from open-world repositories based on the model's capability frontier.<n>On Qwen2.5-VL-7B-Instruct across 12 benchmarks, Active-Zero 53.97 average accuracy on reasoning tasks (5.7% improvement) and 59.77 on general understanding (3.9% improvement)
  arXiv  Detail & Related papers  (2026-02-11T17:29:17Z)
• Enhancing Spatio-Temporal Zero-shot Action Recognition with Language-driven Description Attributes [54.50887214639301]
  We propose an innovative approach that harnesses web-crawled descriptions, leveraging a large-language model to extract relevant keywords.<n>This method reduces the need for human annotators and eliminates the laborious manual process of attribute data creation.<n>In our zero-shot experiments, our model achieves accuracies of 81.0%, 53.1%, and 68.9% on UCF-101, HMDB-51, and Kinetics-600, respectively.
  arXiv  Detail & Related papers  (2025-10-31T07:45:44Z)
• Agentic Jigsaw Interaction Learning for Enhancing Visual Perception and Reasoning in Vision-Language Models [63.69856480318313]
  AGILE formulates jigsaw solving as an interactive process, enabling the model to progressively engage with the environment.<n>We show that AGILE substantially boosts performance on jigsaw tasks of varying complexity.<n>We also demonstrate strong generalization across 9 general vision tasks, achieving an average improvement of 3.1%.
  arXiv  Detail & Related papers  (2025-10-01T17:58:05Z)
• Improvement of Human-Object Interaction Action Recognition Using Scene Information and Multi-Task Learning Approach [0.0]
  We propose a methodology to utilize human action recognition performance by considering fixed object information in the environment.<n>The multi-task learning approach, along with interaction area information, succeeds in recognizing the studied interaction and non-interaction actions with an accuracy of 99.25%.
  arXiv  Detail & Related papers  (2025-09-11T00:14:56Z)
• Zero-Shot Object-Centric Representation Learning [72.43369950684057]
  We study current object-centric methods through the lens of zero-shot generalization. We introduce a benchmark comprising eight different synthetic and real-world datasets. We find that training on diverse real-world images improves transferability to unseen scenarios.
  arXiv  Detail & Related papers  (2024-08-17T10:37:07Z)
• Heuristic Vision Pre-Training with Self-Supervised and Supervised Multi-Task Learning [0.0]
  We propose a novel pre-training framework by adopting both self-supervised and supervised visual pre-text tasks in a multi-task manner. Results show that our pre-trained models can deliver results on par with or better than state-of-the-art (SOTA) results on multiple visual tasks.
  arXiv  Detail & Related papers  (2023-10-11T14:06:04Z)
• Reinforcement Learning from Passive Data via Latent Intentions [86.4969514480008]
  We show that passive data can still be used to learn features that accelerate downstream RL. Our approach learns from passive data by modeling intentions. Our experiments demonstrate the ability to learn from many forms of passive data, including cross-embodiment video data and YouTube videos.
  arXiv  Detail & Related papers  (2023-04-10T17:59:05Z)
• Visuomotor Control in Multi-Object Scenes Using Object-Aware Representations [25.33452947179541]
  We show the effectiveness of object-aware representation learning techniques for robotic tasks. Our model learns control policies in a sample-efficient manner and outperforms state-of-the-art object techniques.
  arXiv  Detail & Related papers  (2022-05-12T19:48:11Z)
• Task-Induced Representation Learning [14.095897879222672]
  We evaluate the effectiveness of representation learning approaches for decision making in visually complex environments. We find that representation learning generally improves sample efficiency on unseen tasks even in visually complex scenes.
  arXiv  Detail & Related papers  (2022-04-25T17:57:10Z)
• Learning Representations with Contrastive Self-Supervised Learning for Histopathology Applications [8.69535649683089]
  We show how contrastive self-supervised learning can reduce the annotation effort within digital pathology. Our results pave the way for realizing the full potential of self-supervised learning for histopathology applications.
  arXiv  Detail & Related papers  (2021-12-10T16:08:57Z)
• To be Critical: Self-Calibrated Weakly Supervised Learning for Salient Object Detection [95.21700830273221]
  Weakly-supervised salient object detection (WSOD) aims to develop saliency models using image-level annotations. We propose a self-calibrated training strategy by explicitly establishing a mutual calibration loop between pseudo labels and network predictions. We prove that even a much smaller dataset with well-matched annotations can facilitate models to achieve better performance as well as generalizability.
  arXiv  Detail & Related papers  (2021-09-04T02:45:22Z)
• Curious Representation Learning for Embodied Intelligence [81.21764276106924]
  Self-supervised representation learning has achieved remarkable success in recent years. Yet to build truly intelligent agents, we must construct representation learning algorithms that can learn from environments. We propose a framework, curious representation learning, which jointly learns a reinforcement learning policy and a visual representation model.
  arXiv  Detail & Related papers  (2021-05-03T17:59:20Z)
• Model-Based Visual Planning with Self-Supervised Functional Distances [104.83979811803466]
  We present a self-supervised method for model-based visual goal reaching. Our approach learns entirely using offline, unlabeled data. We find that this approach substantially outperforms both model-free and model-based prior methods.
  arXiv  Detail & Related papers  (2020-12-30T23:59:09Z)
• Scene-Graph Augmented Data-Driven Risk Assessment of Autonomous Vehicle Decisions [1.4086978333609153]
  We propose a novel data-driven approach that uses scene-graphs as intermediate representations. Our approach includes a Multi-Relation Graph Convolution Network, a Long-Short Term Memory Network, and attention layers for modeling the subjective risk of driving maneuvers. We show that our approach achieves a higher classification accuracy than the state-of-the-art approach on both large (96.4% vs. 91.2%) and small (91.8% vs. 71.2%) We also show that our model trained on a synthesized dataset achieves an average accuracy of 87.8% when tested on a real-world dataset.
  arXiv  Detail & Related papers  (2020-08-31T07:41:27Z)
• A Simple Framework for Contrastive Learning of Visual Representations [116.37752766922407]
  This paper presents SimCLR: a simple framework for contrastive learning of visual representations. We show that composition of data augmentations plays a critical role in defining effective predictive tasks. We are able to considerably outperform previous methods for self-supervised and semi-supervised learning on ImageNet.
  arXiv  Detail & Related papers  (2020-02-13T18:50:45Z)

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.