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Found 53 papers, 25 papers with code
SAMPLING: Scene-adaptive Hierarchical Multiplane Images Representation for Novel View Synthesis from a Single Image
Recent novel view synthesis methods obtain promising results for relatively small scenes, e. g., indoor environments and scenes with a few objects, but tend to fail for unbounded outdoor scenes with a single image as input.
Substance or Style: What Does Your Image Embedding Know?
We find that image-text models (CLIP and ALIGN) are better at recognizing new examples of style transfer than masking-based models (CAN and MAE).
The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation
Denoising diffusion probabilistic models have transformed image generation with their impressive fidelity and diversity.
A Tale of Two Features: Stable Diffusion Complements DINO for Zero-Shot Semantic Correspondence
Text-to-image diffusion models have made significant advances in generating and editing high-quality images.
ContactArt: Learning 3D Interaction Priors for Category-level Articulated Object and Hand Poses Estimation
We propose a new dataset and a novel approach to learning hand-object interaction priors for hand and articulated object pose estimation.
LOCATE: Localize and Transfer Object Parts for Weakly Supervised Affordance Grounding
A key step to acquire this skill is to identify what part of the object affords each action, which is called affordance grounding.
VQ3D: Learning a 3D-Aware Generative Model on ImageNet
Recent work has shown the possibility of training generative models of 3D content from 2D image collections on small datasets corresponding to a single object class, such as human faces, animal faces, or cars.
Accidental Light Probes
Recovering lighting in a scene from a single image is a fundamental problem in computer vision.
Self-supervised AutoFlow
Recently, AutoFlow has shown promising results on learning a training set for optical flow, but requires ground truth labels in the target domain to compute its search metric.
Face Deblurring using Dual Camera Fusion on Mobile Phones
To the best of our knowledge, our work is the first mobile solution for face motion deblurring that works reliably and robustly over thousands of images in diverse motion and lighting conditions.
SAMURAI: Shape And Material from Unconstrained Real-world Arbitrary Image collections
Our method works on in-the-wild online image collections of an object and produces relightable 3D assets for several use-cases such as AR/VR.
An Extendable, Efficient and Effective Transformer-based Object Detector
Transformers have been widely used in numerous vision problems especially for visual recognition and detection.
Adaptive Transformers for Robust Few-shot Cross-domain Face Anti-spoofing
While recent face anti-spoofing methods perform well under the intra-domain setups, an effective approach needs to account for much larger appearance variations of images acquired in complex scenes with different sensors for robust performance.
Disentangling Architecture and Training for Optical Flow
Our newly trained RAFT achieves an Fl-all score of 4. 31% on KITTI 2015, more accurate than all published optical flow methods at the time of writing.
Kubric: A scalable dataset generator
Data is the driving force of machine learning, with the amount and quality of training data often being more important for the performance of a system than architecture and training details.
FILM: Frame Interpolation for Large Motion
Recent methods use multiple networks to estimate optical flow or depth and a separate network dedicated to frame synthesis.
Ranked #2 on
Video Frame Interpolation
on Xiph-4k
Revisiting RCAN: Improved Training for Image Super-Resolution
Image super-resolution (SR) is a fast-moving field with novel architectures attracting the spotlight.
ViSER: Video-Specific Surface Embeddings for Articulated 3D Shape Reconstruction
The surface embeddings are implemented as coordinate-based MLPs that are fit to each video via consistency and contrastive reconstruction losses. Experimental results show that ViSER compares favorably against prior work on challenging videos of humans with loose clothing and unusual poses as well as animals videos from DAVIS and YTVOS.
Pyramid Adversarial Training Improves ViT Performance
In this work, we present pyramid adversarial training (PyramidAT), a simple and effective technique to improve ViT's overall performance.
Ranked #6 on
Domain Generalization
on ImageNet-C
(using extra training data)
ViDT: An Efficient and Effective Fully Transformer-based Object Detector
Transformers are transforming the landscape of computer vision, especially for recognition tasks.
Ranked #11 on
Object Detection
on COCO 2017 val
LASR: Learning Articulated Shape Reconstruction from a Monocular Video
Remarkable progress has been made in 3D reconstruction of rigid structures from a video or a collection of images.
AutoFlow: Learning a Better Training Set for Optical Flow
Synthetic datasets play a critical role in pre-training CNN models for optical flow, but they are painstaking to generate and hard to adapt to new applications.
Adaptive Prototype Learning and Allocation for Few-Shot Segmentation
By integrating the SGC and GPA together, we propose the Adaptive Superpixel-guided Network (ASGNet), which is a lightweight model and adapts to object scale and shape variation.
Ranked #55 on
Few-Shot Semantic Segmentation
on COCO-20i (5-shot)
HumanGPS: Geodesic PreServing Feature for Dense Human Correspondences
In this paper, we address the problem of building dense correspondences between human images under arbitrary camera viewpoints and body poses.
Improving Deep Stereo Network Generalization with Geometric Priors
End-to-end deep learning methods have advanced stereo vision in recent years and obtained excellent results when the training and test data are similar.
Learnable Cost Volume Using the Cayley Representation
Cost volume is an essential component of recent deep models for optical flow estimation and is usually constructed by calculating the inner product between two feature vectors.
Hierarchical Contrastive Motion Learning for Video Action Recognition
Thus, the motion features at higher levels are trained to gradually capture semantic dynamics and evolve more discriminative for action recognition.
SENSE: a Shared Encoder Network for Scene-flow Estimation
We introduce a compact network for holistic scene flow estimation, called SENSE, which shares common encoder features among four closely-related tasks: optical flow estimation, disparity estimation from stereo, occlusion estimation, and semantic segmentation.
Image Formation Model Guided Deep Image Super-Resolution
We present a simple and effective image super-resolution algorithm that imposes an image formation constraint on the deep neural networks via pixel substitution.
Video Stitching for Linear Camera Arrays
Despite the long history of image and video stitching research, existing academic and commercial solutions still produce strong artifacts.
Unsupervised Video Interpolation Using Cycle Consistency
We further introduce a pseudo supervised loss term that enforces the interpolated frames to be consistent with predictions of a pre-trained interpolation model.
Ranked #1 on
Video Frame Interpolation
on UCF101
(PSNR (sRGB) metric)
Pixel-Adaptive Convolutional Neural Networks
In addition, we also demonstrate that PAC can be used as a drop-in replacement for convolution layers in pre-trained networks, resulting in consistent performance improvements.
A Fusion Approach for Multi-Frame Optical Flow Estimation
To date, top-performing optical flow estimation methods only take pairs of consecutive frames into account.
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 #5 on
Optical Flow Estimation
on KITTI 2012
Rendering Portraitures from Monocular Camera and Beyond
Specifically, we first exploit Convolutional Neural Networks to estimate the relative depth and portrait segmentation maps from a single input image.
Learning Data Terms for Non-blind Deblurring
We propose a simple and effective discriminative framework to learn data terms that can adaptively handle blurred images in the presence of severe noise and outliers.
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.
Learning Superpixels With Segmentation-Aware Affinity Loss
Specifically, we propose a new loss function that takes the segmentation error into account for affinity learning.
Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation
We address the unsupervised learning of several interconnected problems in low-level vision: single view depth prediction, camera motion estimation, optical flow, and segmentation of a video into the static scene and moving regions.
Ranked #52 on
Monocular Depth Estimation
on KITTI Eigen split
Learning Dual Convolutional Neural Networks for Low-Level Vision
These problems usually involve the estimation of two components of the target signals: structures and details.
Learning Rigidity in Dynamic Scenes with a Moving Camera for 3D Motion Field Estimation
Estimation of 3D motion in a dynamic scene from a temporal pair of images is a core task in many scene understanding problems.
SPLATNet: Sparse Lattice Networks for Point Cloud Processing
We present a network architecture for processing point clouds that directly operates on a collection of points represented as a sparse set of samples in a high-dimensional lattice.
Ranked #25 on
Semantic Segmentation
on ScanNet
(test mIoU metric)
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.
Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation
Finally, the two input images are warped and linearly fused to form each intermediate frame.
Learning to Super-Resolve Blurry Face and Text Images
We present an algorithm to directly restore a clear high-resolution image from a blurry low-resolution input.
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
Cascaded Scene Flow Prediction using Semantic Segmentation
Given two consecutive frames from a pair of stereo cameras, 3D scene flow methods simultaneously estimate the 3D geometry and motion of the observed scene.
Blind Image Deblurring Using Dark Channel Prior
Therefore, enforcing the sparsity of the dark channel helps blind deblurring on various scenarios, including natural, face, text, and low-illumination images.
Ranked #7 on
Deblurring
on RealBlur-R (trained on GoPro)
Optical Flow with Semantic Segmentation and Localized Layers
Existing optical flow methods make generic, spatially homogeneous, assumptions about the spatial structure of the flow.
Layered RGBD Scene Flow Estimation
As consumer depth sensors become widely available, estimating scene flow from RGBD sequences has received increasing attention.
Local Layering for Joint Motion Estimation and Occlusion Detection
To handle such situations, we propose a local layering model where motion and occlusion relationships are inferred jointly.
A Fully-Connected Layered Model of Foreground and Background Flow
Layered models allow scene segmentation and motion estimation to be formulated together and to inform one another.
Layered image motion with explicit occlusions, temporal consistency, and depth ordering
We present a new probabilistic model of optical flow in layers that addresses many of the shortcomings of previous approaches.
