532 citations · 2.6k across the 41 of their papers we have counts for
9 papers · 1 filter
STAIR: Learning Sparse Text and Image Representation in Grounded Tokens
Chen Chen, Bowen Zhang, Liangliang Cao +7
Image and text retrieval is one of the foundational tasks in the vision and language domain with multiple real-world applications. State-of-the-art approaches, e.g. CLIP, ALIGN, re…
Co-training Transformer with Videos and Images Improves Action Recognition
Bowen Zhang, Jiahui Yu, Christopher Fifty +4
In learning action recognition, models are typically pre-trained on object recognition with images, such as ImageNet, and later fine-tuned on target action recognition with videos.…
Vector-quantized Image Modeling with Improved VQGAN
Jiahui Yu, Xin Li, Jing Yu Koh +7
Pretraining language models with next-token prediction on massive text corpora has delivered phenomenal zero-shot, few-shot, transfer learning and multi-tasking capabilities on bot…
Searching for Fast Model Families on Datacenter Accelerators
Sheng Li, Mingxing Tan, Ruoming Pang +4
Neural Architecture Search (NAS), together with model scaling, has shown remarkable progress in designing high accuracy and fast convolutional architecture families. However, as ne…
EfficientDet: Scalable and Efficient Object Detection
Mingxing Tan, Ruoming Pang, Quoc V. Le
Model efficiency has become increasingly important in computer vision. In this paper, we systematically study neural network architecture design choices for object detection and pr…
Searching for MobileNetV3
Andrew Howard, Mark Sandler, Grace Chu +9
We present the next generation of MobileNets based on a combination of complementary search techniques as well as a novel architecture design. MobileNetV3 is tuned to mobile phone…
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…
Domain Adaptive Transfer Learning with Specialist Models
Jiquan Ngiam, Daiyi Peng, Vijay Vasudevan +3
Transfer learning is a widely used method to build high performing computer vision models. In this paper, we study the efficacy of transfer learning by examining how the choice of…
MnasNet: Platform-Aware Neural Architecture Search for Mobile
Mingxing Tan, Bo Chen, Ruoming Pang +4
Designing convolutional neural networks (CNN) for mobile devices is challenging because mobile models need to be small and fast, yet still accurate. Although significant efforts ha…