Search Results for author:
Found 11 papers, 8 papers with code
Scaling 4D Representations
Scaling has not yet been convincingly demonstrated for pure self-supervised learning from video.
BootsTAP: Bootstrapped Training for Tracking-Any-Point
To endow models with greater understanding of physics and motion, it is useful to enable them to perceive how solid surfaces move and deform in real scenes.
Ranked #1 on
Point Tracking
on TAP-Vid-RGB-Stacking
Making the Most of What You Have: Adapting Pre-trained Visual Language Models in the Low-data Regime
Large-scale visual language models are widely used as pre-trained models and then adapted for various downstream tasks.
Zorro: the masked multimodal transformer
Attention-based models are appealing for multimodal processing because inputs from multiple modalities can be concatenated and fed to a single backbone network - thus requiring very little fusion engineering.
Flamingo: a Visual Language Model for Few-Shot Learning
Building models that can be rapidly adapted to novel tasks using only a handful of annotated examples is an open challenge for multimodal machine learning research.
Ranked #1 on
Action Recognition
on RareAct
Broaden Your Views for Self-Supervised Video Learning
Most successful self-supervised learning methods are trained to align the representations of two independent views from the data.
Game Plan: What AI can do for Football, and What Football can do for AI
The rapid progress in artificial intelligence (AI) and machine learning has opened unprecedented analytics possibilities in various team and individual sports, including baseball, basketball, and tennis.
Transformation-based Adversarial Video Prediction on Large-Scale Data
Recent breakthroughs in adversarial generative modeling have led to models capable of producing video samples of high quality, even on large and complex datasets of real-world video.
Ranked #9 on
Video Generation
on BAIR Robot Pushing
Predicting Future Instance Segmentation by Forecasting Convolutional Features
We apply the "detection head'" of Mask R-CNN on the predicted features to produce the instance segmentation of future frames.
Predicting Deeper into the Future of Semantic Segmentation
The ability to predict and therefore to anticipate the future is an important attribute of intelligence.
Semantic Segmentation using Adversarial Networks
Adversarial training has been shown to produce state of the art results for generative image modeling.
