1.6k citations · 4.5k across the 21 of their papers we have counts for
22 papers
ALBench: A Framework for Evaluating Active Learning in Object Detection
Zhanpeng Feng, Shiliang Zhang, Rinyoichi Takezoe +5
Active learning is an important technology for automated machine learning systems. In contrast to Neural Architecture Search (NAS) which aims at automating neural network architect…
YMIR: A Rapid Data-centric Development Platform for Vision Applications
Phoenix X. Huang, Wenze Hu, William Brendel +3
This paper introduces an open source platform to support the rapid development of computer vision applications at scale. The platform puts the efficient data development at the cen…
Generative Modeling for Small-Data Object Detection
Lanlan Liu, Michael Muelly, Jia Deng +2
This paper explores object detection in the small data regime, where only a limited number of annotated bounding boxes are available due to data rarity and annotation expense. This…
Feature Partitioning for Efficient Multi-Task Architectures
Alejandro Newell, Lu Jiang, Chong Wang +2
Multi-task learning holds the promise of less data, parameters, and time than training of separate models. We propose a method to automatically search over multi-task architectures…
Composing Text and Image for Image Retrieval - An Empirical Odyssey
Nam Vo, Lu Jiang, Chen Sun +4
In this paper, we study the task of image retrieval, where the input query is specified in the form of an image plus some text that describes desired modifications to the input ima…
Vision-Based Gait Analysis for Senior Care
David Xue, Anin Sayana, Evan Darke +7
As the senior population rapidly increases, it is challenging yet crucial to provide effective long-term care for seniors who live at home or in senior care facilities. Smart senio…
NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-Supervised Object Detection
JIyang Gao, Jiang Wang, Shengyang Dai +2
The labeling cost of large number of bounding boxes is one of the main challenges for training modern object detectors. To reduce the dependence on expensive bounding box annotatio…
Focal Visual-Text Attention for Visual Question Answering
Junwei Liang, Lu Jiang, Liangliang Cao +2
Recent insights on language and vision with neural networks have been successfully applied to simple single-image visual question answering. However, to tackle real-life question a…
Iterative Visual Reasoning Beyond Convolutions
Xinlei Chen, Li-Jia Li, Li Fei-Fei +1
We present a novel framework for iterative visual reasoning. Our framework goes beyond current recognition systems that lack the capability to reason beyond stack of convolutions.…
Attention-based Graph Neural Network for Semi-supervised Learning
Kiran K. Thekumparampil, Chong Wang, Sewoong Oh +1
Recently popularized graph neural networks achieve the state-of-the-art accuracy on a number of standard benchmark datasets for graph-based semi-supervised learning, improving sign…
AMC: AutoML for Model Compression and Acceleration on Mobile Devices
Yihui He, Ji Lin, Zhijian Liu +3
Model compression is a critical technique to efficiently deploy neural network models on mobile devices which have limited computation resources and tight power budgets. Convention…
MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels
Lu Jiang, Zhengyuan Zhou, Thomas Leung +2
Recent deep networks are capable of memorizing the entire data even when the labels are completely random. To overcome the overfitting on corrupted labels, we propose a novel techn…
Progressive Neural Architecture Search
Chenxi Liu, Barret Zoph, Maxim Neumann +7
We propose a new method for learning the structure of convolutional neural networks (CNNs) that is more efficient than recent state-of-the-art methods based on reinforcement learni…
Thoracic Disease Identification and Localization with Limited Supervision
Zhe Li, Chong Wang, Mei Han +4
Accurate identification and localization of abnormalities from radiology images play an integral part in clinical diagnosis and treatment planning. Building a highly accurate predi…
Deep Reinforcement Learning-based Image Captioning with Embedding Reward
Zhou Ren, Xiaoyu Wang, Ning Zhang +2
Image captioning is a challenging problem owing to the complexity in understanding the image content and diverse ways of describing it in natural language. Recent advances in deep…
Learning from Noisy Labels with Distillation
Yuncheng Li, Jianchao Yang, Yale Song +3
The ability of learning from noisy labels is very useful in many visual recognition tasks, as a vast amount of data with noisy labels are relatively easy to obtain. Traditionally,…
Dense Captioning with Joint Inference and Visual Context
Linjie Yang, Kevin Tang, Jianchao Yang +1
Dense captioning is a newly emerging computer vision topic for understanding images with dense language descriptions. The goal is to densely detect visual concepts (e.g., objects,…
Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations
Ranjay Krishna, Yuke Zhu, Oliver Groth +9
Despite progress in perceptual tasks such as image classification, computers still perform poorly on cognitive tasks such as image description and question answering. Cognition is…
YFCC100M: The New Data in Multimedia Research
Bart Thomee, David A. Shamma, Gerald Friedland +5
We present the Yahoo Flickr Creative Commons 100 Million Dataset (YFCC100M), the largest public multimedia collection that has ever been released. The dataset contains a total of 1…
Multi-view Face Detection Using Deep Convolutional Neural Networks
Sachin Sudhakar Farfade, Mohammad Saberian, Li-Jia Li
In this paper we consider the problem of multi-view face detection. While there has been significant research on this problem, current state-of-the-art approaches for this task req…
Visual Sentiment Prediction with Deep Convolutional Neural Networks
Can Xu, Suleyman Cetintas, Kuang-Chih Lee +1
Images have become one of the most popular types of media through which users convey their emotions within online social networks. Although vast amount of research is devoted to se…
Large-Scale Multi-Label Learning with Incomplete Label Assignments
Xiangnan Kong, Zhaoming Wu, Li-Jia Li +4
Multi-label learning deals with the classification problems where each instance can be assigned with multiple labels simultaneously. Conventional multi-label learning approaches ma…