Search Results for author: Li-Jia Li

Found 30 papers, 14 papers with code

YMIR: A Rapid Data-centric Development Platform for Vision Applications

1 code implementation19 Nov 2021 Phoenix X. Huang, Wenze Hu, William Brendel, Manmohan Chandraker, Li-Jia Li, Xiaoyu Wang

This paper introduces an open source platform to support the rapid development of computer vision applications at scale.

Active Learning

Generative Modeling for Small-Data Object Detection

1 code implementation ICCV 2019 Lanlan Liu, Michael Muelly, Jia Deng, Tomas Pfister, Li-Jia Li

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.

Object Detection Pedestrian Detection +1

Feature Partitioning for Efficient Multi-Task Architectures

no code implementations ICLR 2020 Alejandro Newell, Lu Jiang, Chong Wang, Li-Jia Li, Jia Deng

Multi-task learning holds the promise of less data, parameters, and time than training of separate models.

Multi-Task Learning

Eidetic 3D LSTM: A Model for Video Prediction and Beyond

2 code implementations ICLR 2019 Yunbo Wang, Lu Jiang, Ming-Hsuan Yang, Li-Jia Li, Mingsheng Long, Li Fei-Fei

We first evaluate the E3D-LSTM network on widely-used future video prediction datasets and achieve the state-of-the-art performance.

Activity Recognition Video Prediction

Composing Text and Image for Image Retrieval - An Empirical Odyssey

3 code implementations CVPR 2019 Nam Vo, Lu Jiang, Chen Sun, Kevin Murphy, Li-Jia Li, Li Fei-Fei, James Hays

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 image.

Image Retrieval Image Retrieval with Multi-Modal Query

NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-Supervised Object Detection

no code implementations ICCV 2019 JIyang Gao, Jiang Wang, Shengyang Dai, Li-Jia Li, Ram Nevatia

Comparing to standard Faster RCNN, it contains three highlights: an ensemble of two classification heads and a distillation head to avoid overfitting on noisy labels and improve the mining precision, masking the negative sample loss in box predictor to avoid the harm of false negative labels, and training box regression head only on seed annotations to eliminate the harm from inaccurate boundaries of mined bounding boxes.

Semi-Supervised Object Detection Weakly Supervised Object Detection

Vision-Based Gait Analysis for Senior Care

no code implementations1 Dec 2018 David Xue, Anin Sayana, Evan Darke, Kelly Shen, Jun-Ting Hsieh, Zelun Luo, Li-Jia Li, N. Lance Downing, Arnold Milstein, Li Fei-Fei

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.

Distributed Asynchronous Optimization with Unbounded Delays: How Slow Can You Go?

no code implementations ICML 2018 Zhengyuan Zhou, Panayotis Mertikopoulos, Nicholas Bambos, Peter Glynn, Yinyu Ye, Li-Jia Li, Li Fei-Fei

One of the most widely used optimization methods for large-scale machine learning problems is distributed asynchronous stochastic gradient descent (DASGD).

Focal Visual-Text Attention for Visual Question Answering

2 code implementations CVPR 2018 Junwei Liang, Lu Jiang, Liangliang Cao, Li-Jia Li, Alexander Hauptmann

Recent insights on language and vision with neural networks have been successfully applied to simple single-image visual question answering.

Memex Question Answering Question Answering +1

Iterative Visual Reasoning Beyond Convolutions

no code implementations CVPR 2018 Xinlei Chen, Li-Jia Li, Li Fei-Fei, Abhinav Gupta

The framework consists of two core modules: a local module that uses spatial memory to store previous beliefs with parallel updates; and a global graph-reasoning module.

Visual Reasoning

Attention-based Graph Neural Network for Semi-supervised Learning

1 code implementation ICLR 2018 Kiran K. Thekumparampil, Chong Wang, Sewoong Oh, Li-Jia Li

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 significantly over existing approaches.

Graph Regression

AMC: AutoML for Model Compression and Acceleration on Mobile Devices

13 code implementations ECCV 2018 Yihui He, Ji Lin, Zhijian Liu, Hanrui Wang, Li-Jia Li, Song Han

Model compression is a critical technique to efficiently deploy neural network models on mobile devices which have limited computation resources and tight power budgets.

Model Compression Neural Architecture Search

A Goal-oriented Neural Conversation Model by Self-Play

no code implementations ICLR 2018 Wei Wei, Quoc V. Le, Andrew M. Dai, Li-Jia Li

One challenge in applying such techniques to building goal-oriented conversation models is that maximum likelihood-based models are not optimized toward accomplishing goals.

Language Modelling Natural Language Understanding

Progressive Neural Architecture Search

11 code implementations ECCV 2018 Chenxi Liu, Barret Zoph, Maxim Neumann, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan Yuille, Jonathan Huang, Kevin Murphy

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 learning and evolutionary algorithms.

General Classification Image Classification +1

Thoracic Disease Identification and Localization with Limited Supervision

1 code implementation CVPR 2018 Zhe Li, Chong Wang, Mei Han, Yuan Xue, Wei Wei, Li-Jia Li, Li Fei-Fei

Accurate identification and localization of abnormalities from radiology images play an integral part in clinical diagnosis and treatment planning.

General Classification

Deep Reinforcement Learning-based Image Captioning with Embedding Reward

no code implementations CVPR 2017 Zhou Ren, Xiaoyu Wang, Ning Zhang, Xutao Lv, Li-Jia Li

The policy network serves as a local guidance by providing the confidence of predicting the next word according to the current state.

Decision Making Image Captioning

Learning from Noisy Labels with Distillation

no code implementations ICCV 2017 Yuncheng Li, Jianchao Yang, Yale Song, Liangliang Cao, Jiebo Luo, Li-Jia Li

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.

Dense Captioning with Joint Inference and Visual Context

1 code implementation CVPR 2017 Linjie Yang, Kevin Tang, Jianchao Yang, Li-Jia Li

The goal is to densely detect visual concepts (e. g., objects, object parts, and interactions between them) from images, labeling each with a short descriptive phrase.

Best of Both Worlds: Human-Machine Collaboration for Object Annotation

no code implementations CVPR 2015 Olga Russakovsky, Li-Jia Li, Li Fei-Fei

This paper brings together the latest advancements in object detection and in crowd engineering into a principled framework for accurately and efficiently localizing objects in images.

Object Detection

Image Retrieval Using Scene Graphs

no code implementations CVPR 2015 Justin Johnson, Ranjay Krishna, Michael Stark, Li-Jia Li, David Shamma, Michael Bernstein, Li Fei-Fei

We introduce a novel dataset of 5, 000 human-generated scene graphs grounded to images and use this dataset to evaluate our method for image retrieval.

Image Retrieval Object Localization

YFCC100M: The New Data in Multimedia Research

2 code implementations5 Mar 2015 Bart Thomee, David A. Shamma, Gerald Friedland, Benjamin Elizalde, Karl Ni, Douglas Poland, Damian Borth, Li-Jia Li

We present the Yahoo Flickr Creative Commons 100 Million Dataset (YFCC100M), the largest public multimedia collection that has ever been released.

Multimedia Computers and Society H.3.7

Multi-view Face Detection Using Deep Convolutional Neural Networks

3 code implementations10 Feb 2015 Sachin Sudhakar Farfade, Mohammad Saberian, Li-Jia Li

In this paper we propose Deep Dense Face Detector (DDFD), a method that does not require pose/landmark annotation and is able to detect faces in a wide range of orientations using a single model based on deep convolutional neural networks.

Data Augmentation Face Detection +1

Visual Sentiment Prediction with Deep Convolutional Neural Networks

no code implementations21 Nov 2014 Can Xu, Suleyman Cetintas, Kuang-Chih Lee, Li-Jia Li

Images have become one of the most popular types of media through which users convey their emotions within online social networks.

Object Recognition Sentiment Analysis +2

Large-Scale Multi-Label Learning with Incomplete Label Assignments

no code implementations6 Jul 2014 Xiangnan Kong, Zhaoming Wu, Li-Jia Li, Ruofei Zhang, Philip S. Yu, Hang Wu, Wei Fan

Unlike prior works, our method can effectively and efficiently consider missing labels and label correlations simultaneously, and is very scalable, that has linear time complexities over the size of the data.

Multi-Label Learning

Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification

no code implementations NeurIPS 2010 Li-Jia Li, Hao Su, Li Fei-Fei, Eric P. Xing

Robust low-level image features have been proven to be effective representations for a variety of visual recognition tasks such as object recognition and scene classification; but pixels, or even local image patches, carry little semantic meanings.

General Classification Object Recognition +1

Large Margin Learning of Upstream Scene Understanding Models

no code implementations NeurIPS 2010 Jun Zhu, Li-Jia Li, Li Fei-Fei, Eric P. Xing

This paper presents a joint max-margin and max-likelihood learning method for upstream scene understanding models, in which latent topic discovery and prediction model estimation are closely coupled and well-balanced.

General Classification Scene Classification +2

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