509 citations · 1.1k across the 18 of their papers we have counts for
20 papers
Towards Autonomous Mathematics Research
Tony Feng, Trieu H. Trinh, Garrett Bingham +25
Recent advances in foundational models have yielded reasoning systems capable of achieving a gold-medal standard at the International Mathematical Olympiad. The transition from com…
Towards Robust Mathematical Reasoning
Thang Luong, Dawsen Hwang, Hoang H. Nguyen +17
Finding the right north-star metrics is highly critical for advancing the mathematical reasoning capabilities of foundation models, especially given that existing evaluations are e…
HaloQuest: A Visual Hallucination Dataset for Advancing Multimodal Reasoning
Zhecan Wang, Garrett Bingham, Adams Yu +3
Hallucination has been a major problem for large language models and remains a critical challenge when it comes to multimodality in which vision-language models (VLMs) have to deal…
DaTaSeg: Taming a Universal Multi-Dataset Multi-Task Segmentation Model
Xiuye Gu, Yin Cui, Jonathan Huang +8
Observing the close relationship among panoptic, semantic and instance segmentation tasks, we propose to train a universal multi-dataset multi-task segmentation model: DaTaSeg.We u…
Revisiting Multi-Scale Feature Fusion for Semantic Segmentation
Tianjian Meng, Golnaz Ghiasi, Reza Mahjourian +2
It is commonly believed that high internal resolution combined with expensive operations (e.g. atrous convolutions) are necessary for accurate semantic segmentation, resulting in s…
Scaling Open-Vocabulary Image Segmentation with Image-Level Labels
Golnaz Ghiasi, Xiuye Gu, Yin Cui +1
We design an open-vocabulary image segmentation model to organize an image into meaningful regions indicated by arbitrary texts. Recent works (CLIP and ALIGN), despite attaining im…
Combined Scaling for Zero-shot Transfer Learning
Hieu Pham, Zihang Dai, Golnaz Ghiasi +9
We present a combined scaling method - named BASIC - that achieves 85.7% top-1 accuracy on the ImageNet ILSVRC-2012 validation set without learning from any labeled ImageNet exampl…
Multi-Task Self-Training for Learning General Representations
Golnaz Ghiasi, Barret Zoph, Ekin D. Cubuk +2
Despite the fast progress in training specialized models for various tasks, learning a single general model that works well for many tasks is still challenging for computer vision.…
Gradual Domain Adaptation in the Wild:When Intermediate Distributions are Absent
Samira Abnar, Rianne van den Berg, Golnaz Ghiasi +3
We focus on the problem of domain adaptation when the goal is shifting the model towards the target distribution, rather than learning domain invariant representations. It has been…
Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation
Golnaz Ghiasi, Yin Cui, Aravind Srinivas +5
Building instance segmentation models that are data-efficient and can handle rare object categories is an important challenge in computer vision. Leveraging data augmentations is a…
Rethinking Pre-training and Self-training
Barret Zoph, Golnaz Ghiasi, Tsung-Yi Lin +4
Pre-training is a dominant paradigm in computer vision. For example, supervised ImageNet pre-training is commonly used to initialize the backbones of object detection and segmentat…
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
Xianzhi Du, Tsung-Yi Lin, Pengchong Jin +5
Convolutional neural networks typically encode an input image into a series of intermediate features with decreasing resolutions. While this structure is suited to classification t…
MnasFPN: Learning Latency-aware Pyramid Architecture for Object Detection on Mobile Devices
Bo Chen, Golnaz Ghiasi, Hanxiao Liu +4
Despite the blooming success of architecture search for vision tasks in resource-constrained environments, the design of on-device object detection architectures have mostly been m…
Learning Data Augmentation Strategies for Object Detection
Barret Zoph, Ekin D. Cubuk, Golnaz Ghiasi +3
Data augmentation is a critical component of training deep learning models. Although data augmentation has been shown to significantly improve image classification, its potential h…
NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection
Golnaz Ghiasi, Tsung-Yi Lin, Ruoming Pang +1
Current state-of-the-art convolutional architectures for object detection are manually designed. Here we aim to learn a better architecture of feature pyramid network for object de…
Adjustable Real-time Style Transfer
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. Although recent feed-forward neural networks can…
DropBlock: A regularization method for convolutional networks
Golnaz Ghiasi, Tsung-Yi Lin, Quoc V. Le
Deep neural networks often work well when they are over-parameterized and trained with a massive amount of noise and regularization, such as weight decay and dropout. Although drop…
Exploring the structure of a real-time, arbitrary neural artistic stylization network
Golnaz Ghiasi, Honglak Lee, Manjunath Kudlur +2
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 styliz…
Laplacian Pyramid Reconstruction and Refinement for Semantic Segmentation
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. Thi…
Occlusion Coherence: Detecting and Localizing Occluded Faces
Golnaz Ghiasi, Charless C. Fowlkes
The presence of occluders significantly impacts object recognition accuracy. However, occlusion is typically treated as an unstructured source of noise and explicit models for occl…