visual representation for learning

Chemists routinely use visual representations to investigate relationships and move between the observable, physical level and the invisible particulate level (Kozma, Chin, Russell, & Marx, 2002). Generating explanations in a visual format may be a particularly useful learning tool for this domain.

The technique of drawing to learn has received increasing attention in recent years. In this article, we will present distinct purposes for using drawing that are based on active, constructive, and interactive forms of engagement.

The use of visual representations (i.e., photographs, diagrams, models) has been part of science, and their use makes it possible for scientists to interact with and represent complex phenomena, not observable in other ways. Despite a wealth of research in science education on visual representations, the emphasis of such research has mainly been on the conceptual understanding when using ...

Following the literature, the use of drawings as a visual representation has the following benefits: (a) Drawing allows us to represent science, reason in science, communicate and link ideas, and improve participation by molding students' ideas toward organizing their knowledge (Ainsworth et al., 2011 ). (b)

This visual representation can make the complex process easier to understand and remember, reinforcing the learning outcome. 17. Comparative Charts. Comparative charts are a fantastic visual learning strategy that can be effectively used by teachers and parents to boost a visual learner's understanding.

Therefore, visual representation has great potential to enhance learning and teaching, an issue that has been extensively explored and well-documented in extant literature (Eilam, 2012), (Buckley ...

Email. A large body of research indicates that visual cues help us to better retrieve and remember information. The research outcomes on visual learning make complete sense when you consider that ...

Page 5: Visual Representations. Yet another evidence-based strategy to help students learn abstract mathematics concepts and solve problems is the use of visual representations. More than simply a picture or detailed illustration, a visual representation—often referred to as a schematic representation or schematic diagram— is an accurate ...

Visu al information plays a fundamental role in our understanding, more than any other form of information (Colin, 2012). Colin (2012: 2) defines. visualisation as "a graphica l representation ...

vised visual representation learning. From a perspective on contrastive learning [29] as dictionary look-up, we build a dynamic dictionary with a queue and a moving-averaged encoder. This enables building a large and consistent dic-tionary on-the-fly that facilitates contrastive unsupervised learning. MoCo provides competitive results under the

View a PDF of the paper titled Big Transfer (BiT): General Visual Representation Learning, by Alexander Kolesnikov and 6 other authors. Transfer of pre-trained representations improves sample efficiency and simplifies hyperparameter tuning when training deep neural networks for vision. We revisit the paradigm of pre-training on large supervised ...

We present Momentum Contrast (MoCo) for unsupervised visual representation learning. From a perspective on contrastive learning as dictionary look-up, we build a dynamic dictionary with a queue and a moving-averaged encoder. This enables building a large and consistent dictionary on-the-fly that facilitates contrastive unsupervised learning. MoCo provides competitive results under the common ...

Jyothish Pari, Nur Muhammad Shafiullah, Sridhar Pandian Arunachalam, Lerrel Pinto. View a PDF of the paper titled The Surprising Effectiveness of Representation Learning for Visual Imitation, by Jyothish Pari and 3 other authors. While visual imitation learning offers one of the most effective ways of learning from visual demonstrations ...

(iv) contrastive learning lags far behind supervised learn-ing on fine-grained visual classification tasks. 1. Introduction Self-supervised learning (SSL) techniques can now pro-duce visual representations which are competitive with rep-resentations generated by fully supervised networks for many downstream tasks [18]. This is an important mile-

Quentin Garrido, Mahmoud Assran, Nicolas Ballas, Adrien Bardes, Laurent Najman, Yann LeCun. View a PDF of the paper titled Learning and Leveraging World Models in Visual Representation Learning, by Quentin Garrido and 5 other authors. Joint-Embedding Predictive Architecture (JEPA) has emerged as a promising self-supervised approach that learns ...

In this repository we release multiple models from the Big Transfer (BiT): General Visual Representation Learning paper that were pre-trained on the ILSVRC-2012 and ImageNet-21k datasets. We provide the code to fine-tuning the released models in the major deep learning frameworks TensorFlow 2, PyTorch and Jax/Flax.

This work spawned a line of work in patch-based self-supervised visual represen-tation learning methods. These include a model from [31] that predicts the permutation of a "jigsaw puzzle" created from the full image and recent follow-ups [29, 33]. In contrast to patch-based methods, some methods gen-erate cleverly designed image-level ...

Transfer of pre-trained representations improves sample efficiency and simplifies hyperparameter tuning when training deep neural networks for vision. We revisit the paradigm of pre-training on large supervised datasets and fine-tuning the model on a target task. We scale up pre-training, and propose a simple recipe that we call Big Transfer (BiT).

Visual Representation Learning. Early studies predominantly focused on exploring CNN for visual representation learning, leading to a series of CNN backbones, e.g., [21, 26, 27, 46, 50]. Most of them stack low-to-high convolutions by going deeper, targeting for producing low-resolution and high-level representations tailored for image ...

A concept map is a visual representation of contents and core concepts, for example of a course or a chapter. It shows how parts of the contents are related to each other (interrelationships) within a knowledge network. ... but also as a teaching and learning method to let the students practice integrating the contents of different classes ...

This enables each network to learn extra contrastive knowledge from others, leading to better feature representations for visual recognition tasks. We emphasize that the resulting MCL is conceptually simple yet empirically powerful. It is a generic framework that can be applied to both supervised and self-supervised representation learning.

Compositional Zero-Shot Learning (CZSL) has been applied to various scenarios, including scene understanding, visual-language representation, and domain adaptation. Despite numerous endeavours and significant advancements, the crucial issues of fuzzy conceptualization of visual attributes and insufficient inter-class connectivity, have remained insufficiently addressed. To address these issues ...

Recently the state space models (SSMs) with efficient hardware-aware designs, i.e., the Mamba deep learning model, have shown great potential for long sequence modeling. Meanwhile building efficient and generic vision backbones purely upon SSMs is an appealing direction. However, representing visual data is challenging for SSMs due to the position-sensitivity of visual data and the requirement ...

Example of a simplified geometric representation of neuronal activity. Successful inference from context 1 (blue) to context 2 (pink) causes the two planes to become parallel over time. ... "In certain neuronal populations during learning, we saw transitions from disordered representations to these beautiful geometric structures that were ...

Conventional robust learning using WLS may not work well when there is a base matrix involved because instead of the dictionary getting updated, only the representation matrix gets updated while ...

Prompt learning has recently emerged as a compelling alternative to the traditional fine-tuning paradigm for adapting the pre-trained Vision-Language (V-L) models to downstream tasks. Drawing inspiration from the success of prompt learning in Natural Language Processing, pioneering research efforts have been predominantly concentrated on text-based prompting strategies. By contrast, the visual ...

Understanding how the brain represents sensory information and triggers behavioural responses is a fundamental goal in neuroscience. Recent advances in neuronal recording techniques aim to progress towards this milestone, yet the resulting high dimensional responses are challenging to interpret and link to relevant variables. In this work, we introduce SPARKS, a model capable of generating low ...

Unsupervised visual representation learning remains a largely unsolved problem in computer vision research. Among a big body of recently proposed approaches for unsupervised learning of visual representations, a class of self-supervised techniques achieves superior performance on many challenging benchmarks. A large number of the pretext tasks for self-supervised learning have been studied ...

Exercise 1. In the model above, the weight and bias values have been randomly initialized. Perform the following tasks to familiarize yourself with the interface and explore the linear model. You can ignore the Activation Function dropdown for now; we'll discuss this topic later on in the module.. Click the Play ( ️) button above the network to calculate the value of the output node for the ...

Secret values have been redacted from the Visual Studio Code output channel to enhance security. Bug Fixes. Fixed vulnerability issues in TeamsFx SDK. #11973; Fixed an issue with groupchat and groupChat compatibility in Teams app manifest. #12028; Resolved an issue where the link redirection for the lifecycle button Provision was incorrect. #12120