665 citations · 4.3k across the 60 of their papers we have counts for
71 papers
Prompt, Condition, and Generate: Classification of Unsupported Claims with In-Context Learning
Peter Ebert Christensen, Srishti Yadav, Serge Belongie
Unsupported and unfalsifiable claims we encounter in our daily lives can influence our view of the world. Characterizing, summarizing, and -- more generally -- making sense of such…
LUAI Challenge 2021 on Learning to Understand Aerial Images
Gui-Song Xia, Jian Ding, Ming Qian +33
This report summarizes the results of Learning to Understand Aerial Images (LUAI) 2021 challenge held on ICCV 2021, which focuses on object detection and semantic segmentation in a…
The Herbarium 2021 Half-Earth Challenge Dataset
Riccardo de Lutio, Damon Little, Barbara Ambrose +1
Herbarium sheets present a unique view of the world's botanical history, evolution, and diversity. This makes them an all-important data source for botanical research. With the inc…
Benchmarking Representation Learning for Natural World Image Collections
Grant Van Horn, Elijah Cole, Sara Beery +3
Recent progress in self-supervised learning has resulted in models that are capable of extracting rich representations from image collections without requiring any explicit label s…
Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges
Jian Ding, Nan Xue, Gui-Song Xia +8
In the past decade, object detection has achieved significant progress in natural images but not in aerial images, due to the massive variations in the scale and orientation of obj…
Intentonomy: a Dataset and Study towards Human Intent Understanding
Menglin Jia, Zuxuan Wu, Austin Reiter +3
An image is worth a thousand words, conveying information that goes beyond the physical visual content therein. In this paper, we study the intent behind social media images with a…
PyTorch Metric Learning
Kevin Musgrave, Serge Belongie, Ser-Nam Lim
Deep metric learning algorithms have a wide variety of applications, but implementing these algorithms can be tedious and time consuming. PyTorch Metric Learning is an open source…
Learning Gradient Fields for Shape Generation
Ruojin Cai, Guandao Yang, Hadar Averbuch-Elor +4
In this work, we propose a novel technique to generate shapes from point cloud data. A point cloud can be viewed as samples from a distribution of 3D points whose density is concen…
Spatiotemporal Contrastive Video Representation Learning
Rui Qian, Tianjian Meng, Boqing Gong +4
We present a self-supervised Contrastive Video Representation Learning (CVRL) method to learn spatiotemporal visual representations from unlabeled videos. Our representations are l…
End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection
Rui Qian, Divyansh Garg, Yan Wang +6
Reliable and accurate 3D object detection is a necessity for safe autonomous driving. Although LiDAR sensors can provide accurate 3D point cloud estimates of the environment, they…
The Plant Pathology 2020 challenge dataset to classify foliar disease of apples
Ranjita Thapa, Noah Snavely, Serge Belongie +1
Apple orchards in the U.S. are under constant threat from a large number of pathogens and insects. Appropriate and timely deployment of disease management depends on early disease…
DualSDF: Semantic Shape Manipulation using a Two-Level Representation
Zekun Hao, Hadar Averbuch-Elor, Noah Snavely +1
We are seeing a Cambrian explosion of 3D shape representations for use in machine learning. Some representations seek high expressive power in capturing high-resolution detail. Oth…
Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset
Menglin Jia, Mengyun Shi, Mikhail Sirotenko +5
In this work we explore the task of instance segmentation with attribute localization, which unifies instance segmentation (detect and segment each object instance) and fine-graine…
A Metric Learning Reality Check
Kevin Musgrave, Serge Belongie, Ser-Nam Lim
Deep metric learning papers from the past four years have consistently claimed great advances in accuracy, often more than doubling the performance of decade-old methods. In this p…
Differentiating through the Fréchet Mean
Aaron Lou, Isay Katsman, Qingxuan Jiang +3
Recent advances in deep representation learning on Riemannian manifolds extend classical deep learning operations to better capture the geometry of the manifold. One possible exten…
On Feature Normalization and Data Augmentation
Boyi Li, Felix Wu, Ser-Nam Lim +2
The moments (a.k.a., mean and standard deviation) of latent features are often removed as noise when training image recognition models, to increase stability and reduce training ti…
Measuring Dataset Granularity
Yin Cui, Zeqi Gu, Dhruv Mahajan +3
Despite the increasing visibility of fine-grained recognition in our field, "fine-grained'' has thus far lacked a precise definition. In this work, building upon clustering theory,…
Fine-grained Synthesis of Unrestricted Adversarial Examples
Omid Poursaeed, Tianxing Jiang, Yordanos Goshu +3
We propose a novel approach for generating unrestricted adversarial examples by manipulating fine-grained aspects of image generation. Unlike existing unrestricted attacks that typ…
Neural Puppet: Generative Layered Cartoon Characters
Omid Poursaeed, Vladimir G. Kim, Eli Shechtman +2
We propose a learning based method for generating new animations of a cartoon character given a few example images. Our method is designed to learn from a traditionally animated se…
Neural Naturalist: Generating Fine-Grained Image Comparisons
Maxwell Forbes, Christine Kaeser-Chen, Piyush Sharma +1
We introduce the new Birds-to-Words dataset of 41k sentences describing fine-grained differences between photographs of birds. The language collected is highly detailed, while rema…
FoodX-251: A Dataset for Fine-grained Food Classification
Parneet Kaur, Karan Sikka, Weijun Wang +2
Food classification is a challenging problem due to the large number of categories, high visual similarity between different foods, as well as the lack of datasets for training sta…
Enhancing Adversarial Example Transferability with an Intermediate Level Attack
Qian Huang, Isay Katsman, Horace He +3
Neural networks are vulnerable to adversarial examples, malicious inputs crafted to fool trained models. Adversarial examples often exhibit black-box transfer, meaning that adversa…
Positional Normalization
Boyi Li, Felix Wu, Kilian Q. Weinberger +1
A popular method to reduce the training time of deep neural networks is to normalize activations at each layer. Although various normalization schemes have been proposed, they all…
The Herbarium Challenge 2019 Dataset
Kiat Chuan Tan, Yulong Liu, Barbara Ambrose +2
Herbarium sheets are invaluable for botanical research, and considerable time and effort is spent by experts to label and identify specimens on them. In view of recent advances in…