Search Results for author:
Found 68 papers, 32 papers with code
Joint Geodesic Upsampling of Depth Images
We propose an algorithm utilizing geodesic distances to upsample a low resolution depth image using a registered high resolution color image.
Recursive Context Propagation Network for Semantic Scene Labeling
Then a top-down propagation of the aggregated information takes place that enhances the contextual information of each local feature.
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.
Layered Interpretation of Street View Images
We propose a layered street view model to encode both depth and semantic information on street view images for autonomous driving.
Deep Gaussian Conditional Random Field Network: A Model-based Deep Network for Discriminative Denoising
We propose a novel deep network architecture for image\\ denoising based on a Gaussian Conditional Random Field (GCRF) model.
Learning to Remove Multipath Distortions in Time-of-Flight Range Images for a Robotic Arm Setup
We use the robotic arm to automatically collect a large amount of ToF range images containing various multipath distortions.
Gaussian Conditional Random Field Network for Semantic Segmentation
In contrast to the existing approaches that use discrete Conditional Random Field (CRF) models, we propose to use a Gaussian CRF model for the task of semantic segmentation.
Coupled Generative Adversarial Networks
We propose coupled generative adversarial network (CoGAN) for learning a joint distribution of multi-domain images.
Attentional Network for Visual Object Detection
We propose augmenting deep neural networks with an attention mechanism for the visual object detection task.
Unsupervised Image-to-Image Translation Networks
Unsupervised image-to-image translation aims at learning a joint distribution of images in different domains by using images from the marginal distributions in individual domains.
Domain Adaptation
Multimodal Unsupervised Image-To-Image Translation
+2
Tactics of Adversarial Attack on Deep Reinforcement Learning Agents
In the strategically-timed attack, the adversary aims at minimizing the agent's reward by only attacking the agent at a small subset of time steps in an episode.
Deep 360 Pilot: Learning a Deep Agent for Piloting through 360° Sports Video
Watching a 360{\deg} sports video requires a viewer to continuously select a viewing angle, either through a sequence of mouse clicks or head movements.
CASENet: Deep Category-Aware Semantic Edge Detection
To this end, we propose a novel end-to-end deep semantic edge learning architecture based on ResNet and a new skip-layer architecture where category-wise edge activations at the top convolution layer share and are fused with the same set of bottom layer features.
Ranked #1 on
Edge Detection
on Cityscapes test
Deep 360 Pilot: Learning a Deep Agent for Piloting Through 360deg Sports Videos
Given the main object and previously selected viewing angles, our method regresses a shift in viewing angle to move to the next one.
MoCoGAN: Decomposing Motion and Content for Video Generation
The proposed framework generates a video by mapping a sequence of random vectors to a sequence of video frames.
PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume
It then uses the warped features and features of the first image to construct a cost volume, which is processed by a CNN to estimate the optical flow.
Ranked #3 on
Dense Pixel Correspondence Estimation
on HPatches
Dense Pixel Correspondence Estimation
Optical Flow Estimation
Detecting Adversarial Attacks on Neural Network Policies with Visual Foresight
Our core idea is that the adversarial examples targeting at a neural network-based policy are not effective for the frame prediction model.
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
We present a new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks (conditional GANs).
Ranked #2 on
Sketch-to-Image Translation
on COCO-Stuff
Conditional Image Generation
Fundus to Angiography Generation
+5
Learning Binary Residual Representations for Domain-specific Video Streaming
Specifically, we target a streaming setting where the videos to be streamed from a server to a client are all in the same domain and they have to be compressed to a small size for low-latency transmission.
Localization-Aware Active Learning for Object Detection
Active learning - a class of algorithms that iteratively searches for the most informative samples to include in a training dataset - has been shown to be effective at annotating data for image classification.
Reblur2Deblur: Deblurring Videos via Self-Supervised Learning
Motion blur is a fundamental problem in computer vision as it impacts image quality and hinders inference.
A Closed-form Solution to Photorealistic Image Stylization
Photorealistic image stylization concerns transferring style of a reference photo to a content photo with the constraint that the stylized photo should remain photorealistic.
Multimodal Unsupervised Image-to-Image Translation
To translate an image to another domain, we recombine its content code with a random style code sampled from the style space of the target domain.
Multimodal Unsupervised Image-To-Image Translation
Translation
+1
Learning Superpixels With Segmentation-Aware Affinity Loss
Specifically, we propose a new loss function that takes the segmentation error into account for affinity learning.
Domain Stylization: A Strong, Simple Baseline for Synthetic to Real Image Domain Adaptation
Deep neural networks have largely failed to effectively utilize synthetic data when applied to real images due to the covariate shift problem.
Superpixel Sampling Networks
Superpixels provide an efficient low/mid-level representation of image data, which greatly reduces the number of image primitives for subsequent vision tasks.
Video-to-Video Synthesis
We study the problem of video-to-video synthesis, whose goal is to learn a mapping function from an input source video (e. g., a sequence of semantic segmentation masks) to an output photorealistic video that precisely depicts the content of the source video.
Unsupervised Stylish Image Description Generation via Domain Layer Norm
It can learn to generate stylish image descriptions that are more related to image content and can be trained with the arbitrary monolingual corpus without collecting new paired image and stylish descriptions.
Models Matter, So Does Training: An Empirical Study of CNNs for Optical Flow Estimation
We investigate two crucial and closely related aspects of CNNs for optical flow estimation: models and training.
Ranked #7 on
Optical Flow Estimation
on KITTI 2012
Context-Aware Synthesis and Placement of Object Instances
Learning to insert an object instance into an image in a semantically coherent manner is a challenging and interesting problem.
Semantic Image Synthesis with Spatially-Adaptive Normalization
Previous methods directly feed the semantic layout as input to the deep network, which is then processed through stacks of convolution, normalization, and nonlinearity layers.
Ranked #3 on
Sketch-to-Image Translation
on COCO-Stuff
CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification
Urban traffic optimization using traffic cameras as sensors is driving the need to advance state-of-the-art multi-target multi-camera (MTMC) tracking.
STEP: Spatio-Temporal Progressive Learning for Video Action Detection
In this paper, we propose Spatio-TEmporal Progressive (STEP) action detector---a progressive learning framework for spatio-temporal action detection in videos.
Ranked #7 on
Action Detection
on UCF101-24
Meta-Sim: Learning to Generate Synthetic Datasets
Training models to high-end performance requires availability of large labeled datasets, which are expensive to get.
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.
PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flows
Specifically, we learn a two-level hierarchy of distributions where the first level is the distribution of shapes and the second level is the distribution of points given a shape.
Ranked #4 on
Point Cloud Generation
on ShapeNet Car
Neural Turtle Graphics for Modeling City Road Layouts
We propose Neural Turtle Graphics (NTG), a novel generative model for spatial graphs, and demonstrate its applications in modeling city road layouts.
Few-shot Video-to-Video Synthesis
To address the limitations, we propose a few-shot vid2vid framework, which learns to synthesize videos of previously unseen subjects or scenes by leveraging few example images of the target at test time.
Ranked #1 on
Video-to-Video Synthesis
on YouTube Dancing
Dancing to Music
In the analysis phase, we decompose a dance into a series of basic dance units, through which the model learns how to move.
Ranked #3 on
Motion Synthesis
on BRACE
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.
On the distance between two neural networks and the stability of learning
This paper relates parameter distance to gradient breakdown for a broad class of nonlinear compositional functions.
Learning to Generate Multiple Style Transfer Outputs for an Input Sentence
Specifically, we decompose the latent representation of the input sentence to a style code that captures the language style variation and a content code that encodes the language style-independent content.
Style Example-Guided Text Generation using Generative Adversarial Transformers
The style encoder extracts a style code from the reference example, and the text decoder generates texts based on the style code and the context.
Instance-aware, Context-focused, and Memory-efficient Weakly Supervised Object Detection
Weakly supervised learning has emerged as a compelling tool for object detection by reducing the need for strong supervision during training.
Ranked #1 on
Weakly Supervised Object Detection
on COCO test-dev
Learning compositional functions via multiplicative weight updates
This paper proves that multiplicative weight updates satisfy a descent lemma tailored to compositional functions.
COCO-FUNIT: Few-Shot Unsupervised Image Translation with a Content Conditioned Style Encoder
Unsupervised image-to-image translation intends to learn a mapping of an image in a given domain to an analogous image in a different domain, without explicit supervision of the mapping.
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.
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.
UFO$^2$: A Unified Framework towards Omni-supervised Object Detection
Existing work on object detection often relies on a single form of annotation: the model is trained using either accurate yet costly bounding boxes or cheaper but less expressive image-level tags.
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.
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.
LNS-Madam: Low-Precision Training in Logarithmic Number System using Multiplicative Weight Update
Representing deep neural networks (DNNs) in low-precision is a promising approach to enable efficient acceleration and memory reduction.
Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D Shape Synthesis
The core of DMTet includes a deformable tetrahedral grid that encodes a discretized signed distance function and a differentiable marching tetrahedra layer that converts the implicit signed distance representation to the explicit surface mesh representation.
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.
Generating Long Videos of Dynamic Scenes
Existing video generation methods often fail to produce new content as a function of time while maintaining consistencies expected in real environments, such as plausible dynamics and object persistence.
Learning to Relight Portrait Images via a Virtual Light Stage and Synthetic-to-Real Adaptation
An effective approach is to supervise the training of deep neural networks with a high-fidelity dataset of desired input-output pairs, captured with a light stage.
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.
eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers
Therefore, in contrast to existing works, we propose to train an ensemble of text-to-image diffusion models specialized for different synthesis stages.
Ranked #14 on
Text-to-Image Generation
on MS COCO
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.
Magic3D: High-Resolution Text-to-3D Content Creation
DreamFusion has recently demonstrated the utility of a pre-trained text-to-image diffusion model to optimize Neural Radiance Fields (NeRF), achieving remarkable text-to-3D synthesis results.
Ranked #2 on
Text to 3D
on T$^3$Bench
Re-ViLM: Retrieval-Augmented Visual Language Model for Zero and Few-Shot Image Captioning
Augmenting pretrained language models (LMs) with a vision encoder (e. g., Flamingo) has obtained the state-of-the-art results in image-to-text generation.
DiffCollage: Parallel Generation of Large Content with Diffusion Models
We present DiffCollage, a compositional diffusion model that can generate large content by leveraging diffusion models trained on generating pieces of the large content.
Preserve Your Own Correlation: A Noise Prior for Video Diffusion Models
Despite tremendous progress in generating high-quality images using diffusion models, synthesizing a sequence of animated frames that are both photorealistic and temporally coherent is still in its infancy.
Ranked #8 on
Text-to-Video Generation
on UCF-101
Neuralangelo: High-Fidelity Neural Surface Reconstruction
Neural surface reconstruction has been shown to be powerful for recovering dense 3D surfaces via image-based neural rendering.
ATT3D: Amortized Text-to-3D Object Synthesis
Text-to-3D modelling has seen exciting progress by combining generative text-to-image models with image-to-3D methods like Neural Radiance Fields.
DistriFusion: Distributed Parallel Inference for High-Resolution Diffusion Models
To overcome this dilemma, we observe the high similarity between the input from adjacent diffusion steps and propose displaced patch parallelism, which takes advantage of the sequential nature of the diffusion process by reusing the pre-computed feature maps from the previous timestep to provide context for the current step.
Condition-Aware Neural Network for Controlled Image Generation
In parallel to prior conditional control methods, CAN controls the image generation process by dynamically manipulating the weight of the neural network.
UFO²: A Unified Framework towards Omni-supervised Object Detection
Existing work on object detection often relies on a single form of annotation: the model is trained using either accurate yet costly bounding boxes or cheaper but less expressive image-level tags.
