Search Results for author: Golnaz Ghiasi

Found 19 papers, 12 papers with code

Revisiting Multi-Scale Feature Fusion for Semantic Segmentation

no code implementations23 Mar 2022 Tianjian Meng, Golnaz Ghiasi, Reza Mahjorian, Quoc V. Le, Mingxing Tan

It is commonly believed that high internal resolution combined with expensive operations (e. g. atrous convolutions) are necessary for accurate semantic segmentation, resulting in slow speed and large memory usage.

Semantic Segmentation

Open-Vocabulary Image Segmentation

no code implementations22 Dec 2021 Golnaz Ghiasi, Xiuye Gu, Yin Cui, Tsung-Yi Lin

First, it learns to propose segmentation masks for possible organizations.

Semantic Segmentation

Combined Scaling for Open-Vocabulary Image Classification

no code implementations19 Nov 2021 Hieu Pham, Zihang Dai, Golnaz Ghiasi, Kenji Kawaguchi, Hanxiao Liu, Adams Wei Yu, Jiahui Yu, Yi-Ting Chen, Minh-Thang Luong, Yonghui Wu, Mingxing Tan, Quoc V. Le

Second, while increasing the dataset size and the model size has been the defacto method to improve the performance of deep learning models like BASIC, the effect of a large contrastive batch size on such contrastive-trained image-text models is not well-understood.

Ranked #2 on Zero-Shot Transfer Image Classification on ImageNet (using extra training data)

Classification Contrastive Learning +3

Gradual Domain Adaptation in the Wild: When Intermediate Distributions are Absent

no code implementations29 Sep 2021 Samira Abnar, Rianne van den Berg, Golnaz Ghiasi, Mostafa Dehghani, Nal Kalchbrenner, Hanie Sedghi

It is shown that under the following two assumptions: (a) access to samples from intermediate distributions, and (b) samples being annotated with the amount of change from the source distribution; self-training can be successfully applied on gradually shifted samples to adapt the model toward the target distribution.

Domain Adaptation

Multi-Task Self-Training for Learning General Representations

no code implementations ICCV 2021 Golnaz Ghiasi, Barret Zoph, Ekin D. Cubuk, Quoc V. Le, Tsung-Yi Lin

The results suggest self-training is a promising direction to aggregate labeled and unlabeled training data for learning general feature representations.

Multi-Task Learning

Gradual Domain Adaptation in the Wild:When Intermediate Distributions are Absent

1 code implementation10 Jun 2021 Samira Abnar, Rianne van den Berg, Golnaz Ghiasi, Mostafa Dehghani, Nal Kalchbrenner, Hanie Sedghi

It has been shown that under the following two assumptions: (a) access to samples from intermediate distributions, and (b) samples being annotated with the amount of change from the source distribution, self-training can be successfully applied on gradually shifted samples to adapt the model toward the target distribution.

Domain Adaptation

Rethinking Pre-training and Self-training

2 code implementations NeurIPS 2020 Barret Zoph, Golnaz Ghiasi, Tsung-Yi Lin, Yin Cui, Hanxiao Liu, Ekin D. Cubuk, Quoc V. Le

For example, on the COCO object detection dataset, pre-training benefits when we use one fifth of the labeled data, and hurts accuracy when we use all labeled data.

 Ranked #1 on Semantic Segmentation on PASCAL VOC 2012 test (using extra training data)

Data Augmentation Object Detection +1

SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization

5 code implementations CVPR 2020 Xianzhi Du, Tsung-Yi Lin, Pengchong Jin, Golnaz Ghiasi, Mingxing Tan, Yin Cui, Quoc V. Le, Xiaodan Song

We propose SpineNet, a backbone with scale-permuted intermediate features and cross-scale connections that is learned on an object detection task by Neural Architecture Search.

General Classification Image Classification +4

MnasFPN: Learning Latency-aware Pyramid Architecture for Object Detection on Mobile Devices

2 code implementations CVPR 2020 Bo Chen, Golnaz Ghiasi, Hanxiao Liu, Tsung-Yi Lin, Dmitry Kalenichenko, Hartwig Adams, Quoc V. Le

We propose MnasFPN, a mobile-friendly search space for the detection head, and combine it with latency-aware architecture search to produce efficient object detection models.

Object Detection

Learning Data Augmentation Strategies for Object Detection

6 code implementations ECCV 2020 Barret Zoph, Ekin D. Cubuk, Golnaz Ghiasi, Tsung-Yi Lin, Jonathon Shlens, Quoc V. Le

Importantly, the best policy found on COCO may be transferred unchanged to other detection datasets and models to improve predictive accuracy.

Image Augmentation Image Classification +1

Adjustable Real-time Style Transfer

1 code implementation ICLR 2020 Mohammad Babaeizadeh, Golnaz Ghiasi

Artistic style transfer is the problem of synthesizing an image with content similar to a given image and style similar to another.

Style Transfer

DropBlock: A regularization method for convolutional networks

6 code implementations NeurIPS 2018 Golnaz Ghiasi, Tsung-Yi Lin, Quoc V. Le

This lack of success of dropout for convolutional layers is perhaps due to the fact that activation units in convolutional layers are spatially correlated so information can still flow through convolutional networks despite dropout.

Image Classification Object Detection

Exploring the structure of a real-time, arbitrary neural artistic stylization network

13 code implementations18 May 2017 Golnaz Ghiasi, Honglak Lee, Manjunath Kudlur, Vincent Dumoulin, Jonathon Shlens

In this paper, we present a method which combines the flexibility of the neural algorithm of artistic style with the speed of fast style transfer networks to allow real-time stylization using any content/style image pair.

Style Transfer

Laplacian Pyramid Reconstruction and Refinement for Semantic Segmentation

1 code implementation8 May 2016 Golnaz Ghiasi, Charless C. Fowlkes

CNN architectures have terrific recognition performance but rely on spatial pooling which makes it difficult to adapt them to tasks that require dense, pixel-accurate labeling.

Semantic Segmentation

Parsing Occluded People

no code implementations CVPR 2014 Golnaz Ghiasi, Yi Yang, Deva Ramanan, Charless C. Fowlkes

Occlusion poses a significant difficulty for object recognition due to the combinatorial diversity of possible occlusion patterns.

Object Recognition Pose Estimation

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