Search Results for author:
Found 17 papers, 11 papers with code
Scalable Pre-training of Large Autoregressive Image Models
Specifically, we highlight two key findings: (1) the performance of the visual features scale with both the model capacity and the quantity of data, (2) the value of the objective function correlates with the performance of the model on downstream tasks.
Ranked #333 on
Image Classification
on ImageNet
(using extra training data)
ImageBind: One Embedding Space To Bind Them All
We show that all combinations of paired data are not necessary to train such a joint embedding, and only image-paired data is sufficient to bind the modalities together.
Ranked #2 on
Zero-shot Classification (unified classes)
on LLVIP
DINOv2: Learning Robust Visual Features without Supervision
The recent breakthroughs in natural language processing for model pretraining on large quantities of data have opened the way for similar foundation models in computer vision.
Ranked #1 on
Image Classification
on CIFAR-10
Are Visual Recognition Models Robust to Image Compression?
For example, for segmentation mIoU is reduced from 44. 5 to 30. 5 mIoU when compressing to 0. 1 bpp using the best compression model we evaluated.
Improving Statistical Fidelity for Neural Image Compression with Implicit Local Likelihood Models
Lossy image compression aims to represent images in as few bits as possible while maintaining fidelity to the original.
Image Compression with Product Quantized Masked Image Modeling
In this work, we attempt to bring these lines of research closer by revisiting vector quantization for image compression.
OmniMAE: Single Model Masked Pretraining on Images and Videos
Furthermore, this model can be learned by dropping 90% of the image and 95% of the video patches, enabling extremely fast training of huge model architectures.
Three things everyone should know about Vision Transformers
(2) Fine-tuning the weights of the attention layers is sufficient to adapt vision transformers to a higher resolution and to other classification tasks.
Ranked #8 on
Image Classification
on CIFAR-10
(using extra training data)
Augmenting Convolutional networks with attention-based aggregation
We show how to augment any convolutional network with an attention-based global map to achieve non-local reasoning.
Ranked #38 on
Semantic Segmentation
on ADE20K val
Are Large-scale Datasets Necessary for Self-Supervised Pre-training?
Our study shows that denoising autoencoders, such as BEiT or a variant that we introduce in this paper, are more robust to the type and size of the pre-training data than popular self-supervised methods trained by comparing image embeddings. We obtain competitive performance compared to ImageNet pre-training on a variety of classification datasets, from different domains.
XCiT: Cross-Covariance Image Transformers
We propose a "transposed" version of self-attention that operates across feature channels rather than tokens, where the interactions are based on the cross-covariance matrix between keys and queries.
Ranked #55 on
Instance Segmentation
on COCO minival
ResMLP: Feedforward networks for image classification with data-efficient training
We present ResMLP, an architecture built entirely upon multi-layer perceptrons for image classification.
Ranked #1 on
Image Classification
on Certificate Verification
LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference
We design a family of image classification architectures that optimize the trade-off between accuracy and efficiency in a high-speed regime.
Ranked #11 on
Image Classification
on iNaturalist 2019
Training Vision Transformers for Image Retrieval
Transformers have shown outstanding results for natural language understanding and, more recently, for image classification.
Skip-Clip: Self-Supervised Spatiotemporal Representation Learning by Future Clip Order Ranking
Deep neural networks require collecting and annotating large amounts of data to train successfully.
Ranked #44 on
Self-Supervised Action Recognition
on UCF101
Tell, Draw, and Repeat: Generating and Modifying Images Based on Continual Linguistic Instruction
Conditional text-to-image generation is an active area of research, with many possible applications.
Ranked #2 on
Text-to-Image Generation
on GeNeVA (i-CLEVR)
Real-Time End-to-End Action Detection with Two-Stream Networks
Finally, for better network initialization, we transfer from the task of action recognition to action detection by pre-training our framework using the recently released large-scale Kinetics dataset.
