Search Results for author:
Found 12 papers, 7 papers with code
Inference-time sparse attention with asymmetric indexing
Self-attention in transformer models is an incremental associative memory that maps key vectors to value vectors.
SimpleFSDP: Simpler Fully Sharded Data Parallel with torch.compile
Distributed training of large models consumes enormous computation resources and requires substantial engineering efforts to compose various training techniques.
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 Retrieval
on AmsterTime
(using extra training data)
Hybrid Transformers for Music Source Separation
While it performs poorly when trained only on MUSDB, we show that it outperforms Hybrid Demucs (trained on the same data) by 0. 45 dB of SDR when using 800 extra training songs.
Ranked #1 on
Music Source Separation
on MUSDB18
(using extra training data)
Training data-efficient image transformers & distillation through attention
In this work, we produce a competitive convolution-free transformer by training on Imagenet only.
Ranked #4 on
Efficient ViTs
on ImageNet-1K (with DeiT-S)
End-to-End Object Detection with Transformers
We present a new method that views object detection as a direct set prediction problem.
Ranked #22 on
Panoptic Segmentation
on COCO minival
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Deep learning frameworks have often focused on either usability or speed, but not both.
Frame Interpolation with Multi-Scale Deep Loss Functions and Generative Adversarial Networks
To improve the quality of synthesised intermediate video frames, our network is jointly supervised at different levels with a perceptual loss function that consists of an adversarial and two content losses.
Crafting a multi-task CNN for viewpoint estimation
Convolutional Neural Networks (CNNs) were recently shown to provide state-of-the-art results for object category viewpoint estimation.
Deep Exemplar 2D-3D Detection by Adapting from Real to Rendered Views
This paper presents an end-to-end convolutional neural network (CNN) for 2D-3D exemplar detection.
Convolutional Neural Networks for joint object detection and pose estimation: A comparative study
In this paper we study the application of convolutional neural networks for jointly detecting objects depicted in still images and estimating their 3D pose.
