67 citations · 297 across the 34 of their papers we have counts for
35 papers · 1 filter
ScaleDet: A Scalable Multi-Dataset Object Detector
Yanbei Chen, Manchen Wang, Abhay Mittal +4
Multi-dataset training provides a viable solution for exploiting heterogeneous large-scale datasets without extra annotation cost. In this work, we propose a scalable multi-dataset…
Benchmarking Zero-Shot Recognition with Vision-Language Models: Challenges on Granularity and Specificity
Zhenlin Xu, Yi Zhu, Tiffany Deng +6
This paper presents novel benchmarks for evaluating vision-language models (VLMs) in zero-shot recognition, focusing on granularity and specificity. Although VLMs excel in tasks li…
A Meta-Learning Approach to Predicting Performance and Data Requirements
Achin Jain, Gurumurthy Swaminathan, Paolo Favaro +8
We propose an approach to estimate the number of samples required for a model to reach a target performance. We find that the power law, the de facto principle to estimate model pe…
Spatio-Temporal Crop Aggregation for Video Representation Learning
Sepehr Sameni, Simon Jenni, Paolo Favaro
We propose Spatio-temporal Crop Aggregation for video representation LEarning (SCALE), a novel method that enjoys high scalability at both training and inference time. Our model bu…
Efficient Video Prediction via Sparsely Conditioned Flow Matching
Aram Davtyan, Sepehr Sameni, Paolo Favaro
We introduce a novel generative model for video prediction based on latent flow matching, an efficient alternative to diffusion-based models. In contrast to prior work, we keep the…
MOVE: Unsupervised Movable Object Segmentation and Detection
Adam Bielski, Paolo Favaro
We introduce MOVE, a novel method to segment objects without any form of supervision. MOVE exploits the fact that foreground objects can be shifted locally relative to their initia…
Semi-supervised Vision Transformers at Scale
Zhaowei Cai, Avinash Ravichandran, Paolo Favaro +5
We study semi-supervised learning (SSL) for vision transformers (ViT), an under-explored topic despite the wide adoption of the ViT architectures to different tasks. To tackle this…
Controllable Video Generation through Global and Local Motion Dynamics
Aram Davtyan, Paolo Favaro
We present GLASS, a method for Global and Local Action-driven Sequence Synthesis. GLASS is a generative model that is trained on video sequences in an unsupervised manner and that…
Representation Learning by Detecting Incorrect Location Embeddings
Sepehr Sameni, Simon Jenni, Paolo Favaro
In this paper, we introduce a novel self-supervised learning (SSL) loss for image representation learning. There is a growing belief that generalization in deep neural networks is…
Learning to Deblur and Rotate Motion-Blurred Faces
Givi Meishvili, Attila Szabó, Simon Jenni +1
We propose a solution to the novel task of rendering sharp videos from new viewpoints from a single motion-blurred image of a face. Our method handles the complexity of face blur b…
Optical Flow Dataset Synthesis from Unpaired Images
Adrian Wälchli, Paolo Favaro
The estimation of optical flow is an ambiguous task due to the lack of correspondence at occlusions, shadows, reflections, lack of texture and changes in illumination over time. Th…
ISD: Self-Supervised Learning by Iterative Similarity Distillation
Ajinkya Tejankar, Soroush Abbasi Koohpayegani, Vipin Pillai +2
Recently, contrastive learning has achieved great results in self-supervised learning, where the main idea is to push two augmentations of an image (positive pairs) closer compared…
Self-Supervised Multi-View Synchronization Learning for 3D Pose Estimation
Simon Jenni, Paolo Favaro
Current state-of-the-art methods cast monocular 3D human pose estimation as a learning problem by training neural networks on large data sets of images and corresponding skeleton p…
Video Representation Learning by Recognizing Temporal Transformations
Simon Jenni, Givi Meishvili, Paolo Favaro
We introduce a novel self-supervised learning approach to learn representations of videos that are responsive to changes in the motion dynamics. Our representations can be learned…
Steering Self-Supervised Feature Learning Beyond Local Pixel Statistics
Simon Jenni, Hailin Jin, Paolo Favaro
We introduce a novel principle for self-supervised feature learning based on the discrimination of specific transformations of an image. We argue that the generalization capability…
Unsupervised Generative 3D Shape Learning from Natural Images
Attila Szabó, Givi Meishvili, Paolo Favaro
In this paper we present, to the best of our knowledge, the first method to learn a generative model of 3D shapes from natural images in a fully unsupervised way. For example, we d…
Learning to Have an Ear for Face Super-Resolution
Givi Meishvili, Simon Jenni, Paolo Favaro
We propose a novel method to use both audio and a low-resolution image to perform extreme face super-resolution (a 16x increase of the input size). When the resolution of the input…
On Stabilizing Generative Adversarial Training with Noise
Simon Jenni, Paolo Favaro
We present a novel method and analysis to train generative adversarial networks (GAN) in a stable manner. As shown in recent analysis, training is often undermined by the probabili…
Emergence of Object Segmentation in Perturbed Generative Models
Adam Bielski, Paolo Favaro
We introduce a novel framework to build a model that can learn how to segment objects from a collection of images without any human annotation. Our method builds on the observation…
Unsupervised 3D Shape Learning from Image Collections in the Wild
Attila Szabó, Paolo Favaro
We present a method to learn the 3D surface of objects directly from a collection of images. Previous work achieved this capability by exploiting additional manual annotation, such…
Deep Bilevel Learning
Simon Jenni, Paolo Favaro
We present a novel regularization approach to train neural networks that enjoys better generalization and test error than standard stochastic gradient descent. Our approach is base…
Self-Supervised Feature Learning by Learning to Spot Artifacts
Simon Jenni, Paolo Favaro
We introduce a novel self-supervised learning method based on adversarial training. Our objective is to train a discriminator network to distinguish real images from images with sy…
Boosting Self-Supervised Learning via Knowledge Transfer
Mehdi Noroozi, Ananth Vinjimoor, Paolo Favaro +1
In self-supervised learning, one trains a model to solve a so-called pretext task on a dataset without the need for human annotation. The main objective, however, is to transfer th…
Learning to Extract a Video Sequence from a Single Motion-Blurred Image
Meiguang Jin, Givi Meishvili, Paolo Favaro
We present a method to extract a video sequence from a single motion-blurred image. Motion-blurred images are the result of an averaging process, where instant frames are accumulat…