Search Results for author:
Found 6 papers, 1 papers with code
Enabling Visual Composition and Animation in Unsupervised Video Generation
We call our model CAGE for visual Composition and Animation for video GEneration.
Multi-View Unsupervised Image Generation with Cross Attention Guidance
The growing interest in novel view synthesis, driven by Neural Radiance Field (NeRF) models, is hindered by scalability issues due to their reliance on precisely annotated multi-view images.
Spatio-Temporal Crop Aggregation for Video Representation Learning
We propose Spatio-temporal Crop Aggregation for video representation LEarning (SCALE), a novel method that enjoys high scalability at both training and inference time.
Efficient Video Prediction via Sparsely Conditioned Flow Matching
We call our model Random frame conditioned flow Integration for VidEo pRediction, or, in short, RIVER.
Representation Learning by Detecting Incorrect Location Embeddings
We represent object parts with image tokens and train a ViT to detect which token has been combined with an incorrect positional embedding.
Ranked #91 on
Image Classification
on ObjectNet
(using extra training data)
KOALA: A Kalman Optimization Algorithm with Loss Adaptivity
Moreover, we show that the Kalman Filter dynamical model for the evolution of the unknown parameters can be used to capture the gradient dynamics of advanced methods such as Momentum and Adam.
