improving visual representation learning through perceptual understanding

View a PDF of the paper titled Improving Visual Representation Learning through Perceptual Understanding, by Samyakh Tukra and 2 other authors. We present an extension to masked autoencoders (MAE) which improves on the representations learnt by the model by explicitly encouraging the learning of higher scene-level features.

recently similar techniques have been applied for learning visual representations from images [1,4,17,30]. Such meth-ods effectively use image reconstruction as a pretext task on the basis that by learning to predict missing patches useful representations can be learnt for downstream tasks. One challenge when applying such techniques to images

CVPR 2023 Open Access Repository. These CVPR 2023 papers are the Open Access versions, provided by the. Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore. This material is presented to ensure timely dissemination of scholarly and technical work.

Approaches based on denoising autoencoders , where the input is masked and the missing parts reconstructed, have shown to be effective for pre-training in NLP with BERT , and more recently similar techniques have been applied for learning visual representations from images [4, 29, 16, 1].

Improving Visual Representation Learning through Perceptual Understanding. CVPR 2023 · Samyakh Tukra , Frederick Hoffman , Ken Chatfield ·. Edit social preview. We present an extension to masked autoencoders (MAE) which improves on the representations learnt by the model by explicitly encouraging the learning of higher scene-level features.

Improving Visual Representation Learning through Perceptual Understanding. December 2022. DOI: 10.48550/arXiv.2212.14504. Authors: Samyakh Tukra. Imperial College London. Frederick Hoffman. Ken ...

Improving Visual Representation Learning through Perceptual Understanding. 12/30/2022. ∙. by Samyakh Tukra, et al. ∙. ∙. We present an extension to masked autoencoders (MAE) which improves on the representations learnt by the model by explicitly encouraging the learning of higher scene-level features. We do this by: (i) the introduction ...

West Building Exhibit Halls ABC 204. We present an extension to masked autoencoders (MAE) which improves on the representations learnt by the model by explicitly encouraging the learning of higher scene-level features. We do this by: (i) the introduction of a perceptual similarity term between generated and real images (ii) incorporating ...

Download Citation | On Jun 1, 2023, Samyakh Tukra and others published Improving Visual Representation Learning Through Perceptual Understanding | Find, read and cite all the research you need on ...

Supplementary Material for Improving Visual Representation Learning through Perceptual Understanding Samyakh Tukra Frederick Hoffman Ken Chatfield Tractable AI {samyakh.tukra,frederick.hoffman,ken}@tractable.ai (a) Architecture for object detection / segmentation on MS-COCO (b) Architecture for semantic segmentation on ADE20K

This research paper introduces a method called Perceptual MAE to improve the learning of high-level features from images. By adding a perceptual loss and adversarial training to a masked autoencoder, the model learns better representations. These improved representations result in better performance on tasks like image classification, object detection, and semantic segmentation. The method is ...

But also boosts both fine-tuned and few-shot settings for classification. ViT-B. MAE PercMAE. All whilst being much more data and compute efficient than alternate methods.

Improving Visual Representation Learning Through Perceptual Understanding. Best source View on content provider's site ...

recently similar techniques have been applied for learning visual representations from images [1,4,16,29]. Such meth-ods effectively use image reconstruction as a pretext task on the basis that by learning to predict patches in a masked image useful representations can be learnt for downstream tasks, demonstrated by improved data label ...

An obvious answer is visual representation - general-izing to diverse visual environments should require pow-erful representation learning. Prior work in computer vi-sion [16,7,8,5,4] have shown that better representations significantly improve downstream performance for tasks such as image classification. However, in the case of robotics,

Humans have been shown to use a ''straightened'' encoding to represent the natural visual world as it evolves in time (Henaff et al. 2019). In the context of discrete video sequences, ''straightened'' means that changes between frames follow a more linear path in representation space at progressively deeper levels of processing. While deep convolutional networks are often proposed as models of ...

In order to investigate the representation learning capabilities of prominent high-performing computer vision models, we investigated how well they capture various indices of perceptual similarity from large-scale behavioral datasets. We find that higher image classification accuracy rates are not associated with a better performance on these ...

Improvising Visual Representation Learning through Perceptual Understanding. Authors: Samyakh Tukra, Frederick Hoffman, Ken Chatfield. Presenting an extension to masked autoencoders (MAE), the paper seeks to improve the learned representations by explicitly promoting the learning of higher-level scene features.

Deep representation learning is a subfield of machine learning that focuses on learning meaningful and useful representations of data through deep neural networks. However, existing methods for semantic classification typically employ pre-defined target codes such as the one-hot and the Hadamard codes, which can either fail or be less flexible ...

The success of deep learning in identifying complex patterns exceeding human intuition comes at the cost of interpretability. Non-linear entanglement of image features makes deep learning a ...

The disconnect between the findings for mental representation structure and motor skill performance in this study suggests perceptual-cognitive scaffolding occurs prior to motor learning through ...

Here, inspired by a classic proposal relating perceptual processing to memory durability, the level-of-processing theory, we present a sparse coding model for compressing feature embeddings of images, and show that the reconstruction residuals from this model predict how well images are encoded into memory.

Rhythm shapes are visual representations of different 16th note patterns. By categorizing these patterns into 15 shapes, students can quickly recognize, analyze, and practice each one systematically. Benefits of Using Rhythm Shapes: Simplified Learning: Breaking down rhythms into shapes helps students understand and memorize patterns more easily.

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