Search Results for author: Soravit Changpinyo

Found 15 papers, 6 papers with code

On Model Calibration for Long-Tailed Object Detection and Instance Segmentation

no code implementations5 Jul 2021 Tai-Yu Pan, Cheng Zhang, Yandong Li, Hexiang Hu, Dong Xuan, Soravit Changpinyo, Boqing Gong, Wei-Lun Chao

We propose NorCal, Normalized Calibration for long-tailed object detection and instance segmentation, a simple and straightforward recipe that reweighs the predicted scores of each class by its training sample size.

Instance Segmentation Object Detection +1

2.5D Visual Relationship Detection

no code implementations26 Apr 2021 Yu-Chuan Su, Soravit Changpinyo, Xiangning Chen, Sathish Thoppay, Cho-Jui Hsieh, Lior Shapira, Radu Soricut, Hartwig Adam, Matthew Brown, Ming-Hsuan Yang, Boqing Gong

To enable progress on this task, we create a new dataset consisting of 220k human-annotated 2. 5D relationships among 512K objects from 11K images.

Depth Estimation Visual Relationship Detection

Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts

1 code implementation CVPR 2021 Soravit Changpinyo, Piyush Sharma, Nan Ding, Radu Soricut

The availability of large-scale image captioning and visual question answering datasets has contributed significantly to recent successes in vision-and-language pre-training.

Image Captioning Question Answering +1

MosaicOS: A Simple and Effective Use of Object-Centric Images for Long-Tailed Object Detection

1 code implementation ICCV 2021 Cheng Zhang, Tai-Yu Pan, Yandong Li, Hexiang Hu, Dong Xuan, Soravit Changpinyo, Boqing Gong, Wei-Lun Chao

Many objects do not appear frequently enough in complex scenes (e. g., certain handbags in living rooms) for training an accurate object detector, but are often found frequently by themselves (e. g., in product images).

Imputation Instance Segmentation +2

Denoising Large-Scale Image Captioning from Alt-text Data using Content Selection Models

no code implementations10 Sep 2020 Khyathi Raghavi Chandu, Piyush Sharma, Soravit Changpinyo, Ashish Thapliyal, Radu Soricut

Training large-scale image captioning (IC) models demands access to a rich and diverse set of training examples, gathered from the wild, often from noisy alt-text data.

Denoising Image Captioning

Classifier and Exemplar Synthesis for Zero-Shot Learning

1 code implementation16 Dec 2018 Soravit Changpinyo, Wei-Lun Chao, Boqing Gong, Fei Sha

Zero-shot learning (ZSL) enables solving a task without the need to see its examples.

Denoising Zero-Shot Learning

The Power of Sparsity in Convolutional Neural Networks

no code implementations21 Feb 2017 Soravit Changpinyo, Mark Sandler, Andrey Zhmoginov

Deep convolutional networks are well-known for their high computational and memory demands.

Predicting Visual Exemplars of Unseen Classes for Zero-Shot Learning

no code implementations ICCV 2017 Soravit Changpinyo, Wei-Lun Chao, Fei Sha

Leveraging class semantic descriptions and examples of known objects, zero-shot learning makes it possible to train a recognition model for an object class whose examples are not available.

Zero-Shot Learning

An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wild

1 code implementation13 May 2016 Wei-Lun Chao, Soravit Changpinyo, Boqing Gong, Fei Sha

Zero-shot learning (ZSL) methods have been studied in the unrealistic setting where test data are assumed to come from unseen classes only.

Few-Shot Learning Generalized Zero-Shot Learning +1

Synthesized Classifiers for Zero-Shot Learning

2 code implementations CVPR 2016 Soravit Changpinyo, Wei-Lun Chao, Boqing Gong, Fei Sha

Given semantic descriptions of object classes, zero-shot learning aims to accurately recognize objects of the unseen classes, from which no examples are available at the training stage, by associating them to the seen classes, from which labeled examples are provided.

Zero-Shot Learning

Similarity Component Analysis

no code implementations NeurIPS 2013 Soravit Changpinyo, Kuan Liu, Fei Sha

Moreover, we show how SCA can be instrumental in exploratory analysis of data, where we gain insights about the data by examining patterns hidden in its latent components' local similarity values.

Link Prediction Metric Learning

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