2k citations · 4.7k across the 40 of their papers we have counts for
12 papers · 2 filters
Mask2Former for Video Instance Segmentation
Bowen Cheng, Anwesa Choudhuri, Ishan Misra +3
We find Mask2Former also achieves state-of-the-art performance on video instance segmentation without modifying the architecture, the loss or even the training pipeline. In this re…
Masked-attention Mask Transformer for Universal Image Segmentation
Bowen Cheng, Ishan Misra, Alexander G. Schwing +2
Image segmentation is about grouping pixels with different semantics, e.g., category or instance membership, where each choice of semantics defines a task. While only the semantics…
An End-to-End Transformer Model for 3D Object Detection
Ishan Misra, Rohit Girdhar, Armand Joulin
We propose 3DETR, an end-to-end Transformer based object detection model for 3D point clouds. Compared to existing detection methods that employ a number of 3D-specific inductive b…
Emerging Properties in Self-Supervised Vision Transformers
Mathilde Caron, Hugo Touvron, Ishan Misra +4
In this paper, we question if self-supervised learning provides new properties to Vision Transformer (ViT) that stand out compared to convolutional networks (convnets). Beyond the…
3D Spatial Recognition without Spatially Labeled 3D
Zhongzheng Ren, Ishan Misra, Alexander G. Schwing +1
We introduce WyPR, a Weakly-supervised framework for Point cloud Recognition, requiring only scene-level class tags as supervision. WyPR jointly addresses three core 3D recognition…
Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples
Mahmoud Assran, Mathilde Caron, Ishan Misra +4
This paper proposes a novel method of learning by predicting view assignments with support samples (PAWS). The method trains a model to minimize a consistency loss, which ensures t…
MDETR -- Modulated Detection for End-to-End Multi-Modal Understanding
Aishwarya Kamath, Mannat Singh, Yann LeCun +3
Multi-modal reasoning systems rely on a pre-trained object detector to extract regions of interest from the image. However, this crucial module is typically used as a black box, tr…
Robust Audio-Visual Instance Discrimination
Pedro Morgado, Ishan Misra, Nuno Vasconcelos
We present a self-supervised learning method to learn audio and video representations. Prior work uses the natural correspondence between audio and video to define a standard cross…
Self-supervised Pretraining of Visual Features in the Wild
Priya Goyal, Mathilde Caron, Benjamin Lefaudeux +8
Recently, self-supervised learning methods like MoCo, SimCLR, BYOL and SwAV have reduced the gap with supervised methods. These results have been achieved in a control environment,…
Space-Time Crop & Attend: Improving Cross-modal Video Representation Learning
Mandela Patrick, Yuki M. Asano, Bernie Huang +4
The quality of the image representations obtained from self-supervised learning depends strongly on the type of data augmentations used in the learning formulation. Recent papers h…
Barlow Twins: Self-Supervised Learning via Redundancy Reduction
Jure Zbontar, Li Jing, Ishan Misra +2
Self-supervised learning (SSL) is rapidly closing the gap with supervised methods on large computer vision benchmarks. A successful approach to SSL is to learn embeddings which are…
Self-Supervised Pretraining of 3D Features on any Point-Cloud
Zaiwei Zhang, Rohit Girdhar, Armand Joulin +1
Pretraining on large labeled datasets is a prerequisite to achieve good performance in many computer vision tasks like 2D object recognition, video classification etc. However, pre…