Search Results for author:
Found 16 papers, 8 papers with code
PaLI-3 Vision Language Models: Smaller, Faster, Stronger
This paper presents PaLI-3, a smaller, faster, and stronger vision language model (VLM) that compares favorably to similar models that are 10x larger.
Ranked #2 on
Temporal/Casual QA
on NExT-QA
(using extra training data)
Video OWL-ViT: Temporally-consistent open-world localization in video
Our model is end-to-end trainable on video data and enjoys improved temporal consistency compared to tracking-by-detection baselines, while retaining the open-world capabilities of the backbone detector.
PaLI-X: On Scaling up a Multilingual Vision and Language Model
We present the training recipe and results of scaling up PaLI-X, a multilingual vision and language model, both in terms of size of the components and the breadth of its training task mixture.
Ranked #1 on
Fine-Grained Image Recognition
on OVEN
Scaling Vision Transformers to 22 Billion Parameters
The scaling of Transformers has driven breakthrough capabilities for language models.
Ranked #1 on
Zero-Shot Transfer Image Classification
on ObjectNet
LiT: Zero-Shot Transfer with Locked-image text Tuning
This paper presents contrastive-tuning, a simple method employing contrastive training to align image and text models while still taking advantage of their pre-training.
The Impact of Reinitialization on Generalization in Convolutional Neural Networks
Recent results suggest that reinitializing a subset of the parameters of a neural network during training can improve generalization, particularly for small training sets.
Continental-Scale Building Detection from High Resolution Satellite Imagery
Identifying the locations and footprints of buildings is vital for many practical and scientific purposes.
A Generalized Lottery Ticket Hypothesis
We introduce a generalization to the lottery ticket hypothesis in which the notion of "sparsity" is relaxed by choosing an arbitrary basis in the space of parameters.
Scaling Vision with Sparse Mixture of Experts
We present a Vision MoE (V-MoE), a sparse version of the Vision Transformer, that is scalable and competitive with the largest dense networks.
Ranked #1 on
Few-Shot Image Classification
on ImageNet - 5-shot
MLP-Mixer: An all-MLP Architecture for Vision
Convolutional Neural Networks (CNNs) are the go-to model for computer vision.
Ranked #17 on
Image Classification
on OmniBenchmark
Deep Ensembles for Low-Data Transfer Learning
In the low-data regime, it is difficult to train good supervised models from scratch.
Ranked #6 on
Image Classification
on VTAB-1k
(using extra training data)
Scalable Transfer Learning with Expert Models
We explore the use of expert representations for transfer with a simple, yet effective, strategy.
Ranked #11 on
Image Classification
on VTAB-1k
(using extra training data)
What Do Neural Networks Learn When Trained With Random Labels?
We show how this alignment produces a positive transfer: networks pre-trained with random labels train faster downstream compared to training from scratch even after accounting for simple effects, such as weight scaling.
Predicting Neural Network Accuracy from Weights
Furthermore, the predictors are able to rank networks trained on different, unobserved datasets and with different architectures.
Measuring Compositional Generalization: A Comprehensive Method on Realistic Data
We present a large and realistic natural language question answering dataset that is constructed according to this method, and we use it to analyze the compositional generalization ability of three machine learning architectures.
Ranked #5 on
Semantic Parsing
on CFQ
Fast Multi-language LSTM-based Online Handwriting Recognition
We describe an online handwriting system that is able to support 102 languages using a deep neural network architecture.
