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07/31/2023
Predicting masked tokens in stochastic locations improves masked image modeling
Self-supervised learning is a promising paradigm in deep learning that e...
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06/16/2023
Robot Learning with Sensorimotor Pre-training
We present a self-supervised sensorimotor pre-training approach for robo...
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05/24/2023
Refocusing Is Key to Transfer Learning
Transfer learning involves adapting a pre-trained model to novel downstr...
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05/24/2023
Flan-MoE: Scaling Instruction-Finetuned Language Models with Sparse Mixture of Experts
The explosive growth of language models and their applications have led ...
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05/10/2023
Incorporating Structured Representations into Pretrained Vision Language Models Using Scene Graphs
Vision and Language (VL) models have demonstrated remarkable zero-shot p...
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03/30/2023
PAIR-Diffusion: Object-Level Image Editing with Structure-and-Appearance Paired Diffusion Models
Image editing using diffusion models has witnessed extremely fast-paced ...
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03/13/2023
Scaling Vision-Language Models with Sparse Mixture of Experts
The field of natural language processing (NLP) has made significant stri...
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03/06/2023
Learning Humanoid Locomotion with Transformers
We present a sim-to-real learning-based approach for real-world humanoid...
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11/28/2022
G^3: Geolocation via Guidebook Grounding
We demonstrate how language can improve geolocation: the task of predict...
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09/01/2022
Visual Prompting via Image Inpainting
How does one adapt a pre-trained visual model to novel downstream tasks ...
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04/18/2022
Learning to Listen: Modeling Non-Deterministic Dyadic Facial Motion
We present a framework for modeling interactional communication in dyadi...
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06/08/2021
DETReg: Unsupervised Pretraining with Region Priors for Object Detection
Unsupervised pretraining has recently proven beneficial for computer vis...
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09/16/2020
Evaluating Self-Supervised Pretraining Without Using Labels
A common practice in unsupervised representation learning is to use labe...
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06/18/2020
Fully Test-time Adaptation by Entropy Minimization
Faced with new and different data during testing, a model must adapt its...
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04/21/2020
ParkPredict: Motion and Intent Prediction of Vehicles in Parking Lots
We investigate the problem of predicting driver behavior in parking lots...
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03/16/2020
Frustratingly Simple Few-Shot Object Detection
Detecting rare objects from a few examples is an emerging problem. Prior...
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03/09/2020
A New Meta-Baseline for Few-Shot Learning
Meta-learning has become a popular framework for few-shot learning in re...
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12/20/2019
Something-Else: Compositional Action Recognition with Spatial-Temporal Interaction Networks
Human action is naturally compositional: humans can easily recognize and...
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12/15/2018
Hierarchical Discrete Distribution Decomposition for Match Density Estimation
Existing deep learning methods for pixel correspondence output a point e...
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12/05/2018
Few-shot Object Detection via Feature Reweighting
This work aims to solve the challenging few-shot object detection proble...
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11/13/2018
Deep Object Centric Policies for Autonomous Driving
While learning visuomotor skills in an end-to-end manner is appealing, d...
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10/16/2018
Discriminator Rejection Sampling
We propose a rejection sampling scheme using the discriminator of a GAN ...
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07/19/2018
Compositional GAN: Learning Conditional Image Composition
Generative Adversarial Networks (GANs) can produce images of surprising ...
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06/07/2018
Speaker-Follower Models for Vision-and-Language Navigation
Navigation guided by natural language instructions presents a challengin...
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06/05/2018
Deep Mixture of Experts via Shallow Embedding
Larger networks generally have greater representational power at the cos...
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05/12/2018
BDD100K: A Diverse Driving Video Database with Scalable Annotation Tooling
Datasets drive vision progress and autonomous driving is a critical visi...
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12/01/2017
Multi-Content GAN for Few-Shot Font Style Transfer
In this work, we focus on the challenge of taking partial observations o...
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07/20/2017
Deep Layer Aggregation
Convolutional networks have had great success in image classification an...
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05/20/2016
Fully Convolutional Networks for Semantic Segmentation
Convolutional networks are powerful visual models that yield hierarchies...
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05/03/2015
Sequence to Sequence -- Video to Text
Real-world videos often have complex dynamics; and methods for generatin...
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04/02/2015
End-to-End Training of Deep Visuomotor Policies
Policy search methods can allow robots to learn control policies for a w...
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11/15/2013
