Search Results for author:
Found 24 papers, 10 papers with code
SPACE: Speech-driven Portrait Animation with Controllable Expression
It uses a multi-stage approach, combining the controllability of facial landmarks with the high-quality synthesis power of a pretrained face generator.
Implicit Warping for Animation with Image Sets
We present a new implicit warping framework for image animation using sets of source images through the transfer of the motion of a driving video.
AdaViT: Adaptive Tokens for Efficient Vision Transformer
A-ViT achieves this by automatically reducing the number of tokens in vision transformers that are processed in the network as inference proceeds.
Ranked #34 on
Efficient ViTs
on ImageNet-1K (with DeiT-S)
Multimodal Conditional Image Synthesis with Product-of-Experts GANs
Existing conditional image synthesis frameworks generate images based on user inputs in a single modality, such as text, segmentation, sketch, or style reference.
See through Gradients: Image Batch Recovery via GradInversion
In this work, we introduce GradInversion, using which input images from a larger batch (8 - 48 images) can also be recovered for large networks such as ResNets (50 layers), on complex datasets such as ImageNet (1000 classes, 224x224 px).
GANcraft: Unsupervised 3D Neural Rendering of Minecraft Worlds
We represent the world as a continuous volumetric function and train our model to render view-consistent photorealistic images for a user-controlled camera.
One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing
We propose a neural talking-head video synthesis model and demonstrate its application to video conferencing.
Generative Adversarial Networks for Image and Video Synthesis: Algorithms and Applications
The generative adversarial network (GAN) framework has emerged as a powerful tool for various image and video synthesis tasks, allowing the synthesis of visual content in an unconditional or input-conditional manner.
World-Consistent Video-to-Video Synthesis
This is because they lack knowledge of the 3D world being rendered and generate each frame only based on the past few frames.
Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion
We introduce DeepInversion, a new method for synthesizing images from the image distribution used to train a deep neural network.
UNAS: Differentiable Architecture Search Meets Reinforcement Learning
Our framework brings the best of both worlds, and it enables us to search for architectures with both differentiable and non-differentiable criteria in one unified framework while maintaining a low search cost.
Importance Estimation for Neural Network Pruning
On ResNet-101, we achieve a 40% FLOPS reduction by removing 30% of the parameters, with a loss of 0. 02% in the top-1 accuracy on ImageNet.
Contextual Translation Embedding for Visual Relationship Detection and Scene Graph Generation
The previous VTransE model maps entities and predicates into a low-dimensional embedding vector space where the predicate is interpreted as a translation vector between the embedded features of the bounding box regions of the subject and the object.
Few-Shot Unsupervised Image-to-Image Translation
Unsupervised image-to-image translation methods learn to map images in a given class to an analogous image in a different class, drawing on unstructured (non-registered) datasets of images.
Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights
This work presents a method for adapting a single, fixed deep neural network to multiple tasks without affecting performance on already learned tasks.
PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning
This paper presents a method for adding multiple tasks to a single deep neural network while avoiding catastrophic forgetting.
Recurrent Models for Situation Recognition
This work proposes Recurrent Neural Network (RNN) models to predict structured 'image situations' -- actions and noun entities fulfilling semantic roles related to the action.
Ranked #10 on
Grounded Situation Recognition
on SWiG
Grounded Situation Recognition
Human-Object Interaction Detection
+1
Combining Multiple Cues for Visual Madlibs Question Answering
This paper presents an approach for answering fill-in-the-blank multiple choice questions from the Visual Madlibs dataset.
Solving Visual Madlibs with Multiple Cues
This paper focuses on answering fill-in-the-blank style multiple choice questions from the Visual Madlibs dataset.
Learning Models for Actions and Person-Object Interactions with Transfer to Question Answering
This paper proposes deep convolutional network models that utilize local and global context to make human activity label predictions in still images, achieving state-of-the-art performance on two recent datasets with hundreds of labels each.
Ranked #6 on
Human-Object Interaction Detection
on HICO
General Classification
Human-Object Interaction Detection
+4
Learning Informative Edge Maps for Indoor Scene Layout Prediction
We learn to predict 'informative edge' probability maps using two recent methods that exploit local and global context, respectively: structured edge detection forests, and a fully convolutional network for pixelwise labeling.
Part Localization using Multi-Proposal Consensus for Fine-Grained Categorization
We present a simple deep learning framework to simultaneously predict keypoint locations and their respective visibilities and use those to achieve state-of-the-art performance for fine-grained classification.
Unsupervised Network Pretraining via Encoding Human Design
Our idea is to pretrain the network through the task of replicating the process of hand-designed feature extraction.
