Search Results for author:
Found 12 papers, 5 papers with code
Win-Win: Training High-Resolution Vision Transformers from Two Windows
In this paper, we propose a novel strategy for efficient training and inference of high-resolution vision transformers: the key principle is to mask out most of the high-resolution inputs during training, keeping only N random windows.
CroCo v2: Improved Cross-view Completion Pre-training for Stereo Matching and Optical Flow
Despite impressive performance for high-level downstream tasks, self-supervised pre-training methods have not yet fully delivered on dense geometric vision tasks such as stereo matching or optical flow.
Ranked #1 on
Optical Flow Estimation
on KITTI 2012
PoseScript: 3D Human Poses from Natural Language
This process extracts low-level pose information -- the posecodes -- using a set of simple but generic rules on the 3D keypoints.
CroCo: Self-Supervised Pre-training for 3D Vision Tasks by Cross-View Completion
More precisely, we propose the pretext task of cross-view completion where the first input image is partially masked, and this masked content has to be reconstructed from the visible content and the second image.
PoseGPT: Quantization-based 3D Human Motion Generation and Forecasting
The discrete and compressed nature of the latent space allows the GPT-like model to focus on long-range signal, as it removes low-level redundancy in the input signal.
Learning Super-Features for Image Retrieval
Second, they are typically trained with a global loss that only acts on top of an aggregation of local features; by contrast, testing is based on local feature matching, which creates a discrepancy between training and testing.
Ranked #3 on
Image Retrieval
on ROxford (Medium)
Barely-Supervised Learning: Semi-Supervised Learning with very few labeled images
We propose a method to leverage self-supervised methods that provides training signal in the absence of confident pseudo-labels.
Adaptive Density Estimation for Generative Models
We show that our model significantly improves over existing hybrid models: offering GAN-like samples, IS and FID scores that are competitive with fully adversarial models, and improved likelihood scores.
Coverage and Quality Driven Training of Generative Image Models
First, we propose a model that extends variational autoencoders by using deterministic invertible transformation layers to map samples from the decoder to the image space.
Mixed batches and symmetric discriminators for GAN training
We propose to feed the discriminator with mixed batches of true and fake samples, and train it to predict the ratio of true samples in the batch.
Auxiliary Guided Autoregressive Variational Autoencoders
Our contribution is a training procedure relying on an auxiliary loss function that controls which information is captured by the latent variables and what is left to the autoregressive decoder.
Areas of Attention for Image Captioning
We propose "Areas of Attention", a novel attention-based model for automatic image captioning.
