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Found 62 papers, 27 papers with code
PICNIQ: Pairwise Comparisons for Natural Image Quality Assessment
Blind image quality assessment (BIQA) approaches, while promising for automating image quality evaluation, often fall short in real-world scenarios due to their reliance on a generic quality standard applied uniformly across diverse images.
Revisiting Feature Prediction for Learning Visual Representations from Video
This paper explores feature prediction as a stand-alone objective for unsupervised learning from video and introduces V-JEPA, a collection of vision models trained solely using a feature prediction objective, without the use of pretrained image encoders, text, negative examples, reconstruction, or other sources of supervision.
Generalized Portrait Quality Assessment
Automated and robust portrait quality assessment (PQA) is of paramount importance in high-impact applications such as smartphone photography.
Fine Dense Alignment of Image Bursts through Camera Pose and Depth Estimation
This positions our approach as a versatile tool for various burst image processing applications.
Dense Optical Tracking: Connecting the Dots
Code, data, and videos showcasing the capabilities of our approach are available in the project webpage: https://16lemoing. github. io/dot .
Towards Real-World Focus Stacking with Deep Learning
Focus stacking is widely used in micro, macro, and landscape photography to reconstruct all-in-focus images from multiple frames obtained with focus bracketing, that is, with shallow depth of field and different focus planes.
Revisiting Deformable Convolution for Depth Completion
Our study reveals that, different from prior work, deformable convolution needs to be applied on an estimated depth map with a relatively high density for better performance.
MC-JEPA: A Joint-Embedding Predictive Architecture for Self-Supervised Learning of Motion and Content Features
Self-supervised learning of visual representations has been focusing on learning content features, which do not capture object motion or location, and focus on identifying and differentiating objects in images and videos.
Combining multi-spectral data with statistical and deep-learning models for improved exoplanet detection in direct imaging at high contrast
Exoplanet detection by direct imaging is a difficult task: the faint signals from the objects of interest are buried under a spatially structured nuisance component induced by the host star.
An Image Quality Assessment Dataset for Portraits
This costly procedure can be partially replaced by automated learning-based methods for image quality assessment (IQA).
Pixel-wise Agricultural Image Time Series Classification: Comparisons and a Deformable Prototype-based Approach
Improvements in Earth observation by satellites allow for imagery of ever higher temporal and spatial resolution.
WALDO: Future Video Synthesis using Object Layer Decomposition and Parametric Flow Prediction
This paper presents WALDO (WArping Layer-Decomposed Objects), a novel approach to the prediction of future video frames from past ones.
Learning Reward Functions for Robotic Manipulation by Observing Humans
Unlike prior work on leveraging human videos to teach robots, our method, Human Offline Learned Distances (HOLD) requires neither a priori data from the robot environment, nor a set of task-specific human demonstrations, nor a predefined notion of correspondence across morphologies, yet it is able to accelerate training of several manipulation tasks on a simulated robot arm compared to using only a sparse reward obtained from task completion.
VICRegL: Self-Supervised Learning of Local Visual Features
Most recent self-supervised methods for learning image representations focus on either producing a global feature with invariance properties, or producing a set of local features.
High Dynamic Range and Super-Resolution from Raw Image Bursts
Photographs captured by smartphones and mid-range cameras have limited spatial resolution and dynamic range, with noisy response in underexposed regions and color artefacts in saturated areas.
Active Learning Strategies for Weakly-supervised Object Detection
On COCO, using on average 10 fully-annotated images per class, or equivalently 1% of the training set, BiB also reduces the performance gap (in AP) between the weakly-supervised detector and the fully-supervised Fast RCNN by over 70%, showing a good trade-off between performance and data efficiency.
Assembly Planning from Observations under Physical Constraints
This paper addresses the problem of copying an unknown assembly of primitives with known shape and appearance using information extracted from a single photograph by an off-the-shelf procedure for object detection and pose estimation.
Online Learning and Control of Complex Dynamical Systems from Sensory Input
Identifying an effective model of a dynamical system from sensory data and using it for future state prediction and control is challenging.
Localizing Objects with Self-Supervised Transformers and no Labels
We also show that training a class-agnostic detector on the discovered objects boosts results by another 7 points.
Ranked #4 on
Weakly-Supervised Object Localization
on CUB-200-2011
(Top-1 Localization Accuracy metric)
CCVS: Context-aware Controllable Video Synthesis
The prediction model is doubly autoregressive, in the latent space of an autoencoder for forecasting, and in image space for updating contextual information, which is also used to enforce spatio-temporal consistency through a learnable optical flow module.
Ranked #8 on
Video Generation
on BAIR Robot Pushing
Residual Reinforcement Learning from Demonstrations
Residual reinforcement learning (RL) has been proposed as a way to solve challenging robotic tasks by adapting control actions from a conventional feedback controller to maximize a reward signal.
Large-Scale Unsupervised Object Discovery
Extensive experiments on COCO and OpenImages show that, in the single-object discovery setting where a single prominent object is sought in each image, the proposed LOD (Large-scale Object Discovery) approach is on par with, or better than the state of the art for medium-scale datasets (up to 120K images), and over 37% better than the only other algorithms capable of scaling up to 1. 7M images.
NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results
This paper reviews the NTIRE2021 challenge on burst super-resolution.
VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning
Recent self-supervised methods for image representation learning are based on maximizing the agreement between embedding vectors from different views of the same image.
Representation Learning
Self-Supervised Image Classification
+2
Unsupervised Layered Image Decomposition into Object Prototypes
We present an unsupervised learning framework for decomposing images into layers of automatically discovered object models.
Lucas-Kanade Reloaded: End-to-End Super-Resolution from Raw Image Bursts
This presentation addresses the problem of reconstructing a high-resolution image from multiple lower-resolution snapshots captured from slightly different viewpoints in space and time.
Learning to Jointly Deblur, Demosaick and Denoise Raw Images
We address the problem of non-blind deblurring and demosaicking of noisy raw images.
Learning to Compose Hypercolumns for Visual Correspondence
Feature representation plays a crucial role in visual correspondence, and recent methods for image matching resort to deeply stacked convolutional layers.
Ranked #2 on
Semantic correspondence
on Caltech-101
Toward unsupervised, multi-object discovery in large-scale image collections
This paper addresses the problem of discovering the objects present in a collection of images without any supervision.
Ranked #1 on
Multi-object colocalization
on VOC_all
End-to-end Interpretable Learning of Non-blind Image Deblurring
Non-blind image deblurring is typically formulated as a linear least-squares problem regularized by natural priors on the corresponding sharp picture's gradients, which can be solved, for example, using a half-quadratic splitting method with Richardson fixed-point iterations for its least-squares updates and a proximal operator for the auxiliary variable updates.
A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game Encoding
We introduce a general framework for designing and training neural network layers whose forward passes can be interpreted as solving non-smooth convex optimization problems, and whose architectures are derived from an optimization algorithm.
Structured and Localized Image Restoration
We present a novel approach to image restoration that leverages ideas from localized structured prediction and non-linear multi-task learning.
Fully Trainable and Interpretable Non-Local Sparse Models for Image Restoration
Non-local self-similarity and sparsity principles have proven to be powerful priors for natural image modeling.
Learning Semantic Correspondence Exploiting an Object-level Prior
We address the problem of semantic correspondence, that is, establishing a dense flow field between images depicting different instances of the same object or scene category.
Deformable Kernel Networks for Joint Image Filtering
Previous methods based on convolutional neural networks (CNNs) combine nonlinear activations of spatially-invariant kernels to estimate structural details and regress the filtering result.
SPair-71k: A Large-scale Benchmark for Semantic Correspondence
In this paper, we present a new large-scale benchmark dataset of semantically paired images, SPair-71k, which contains 70, 958 image pairs with diverse variations in viewpoint and scale.
Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features
Establishing visual correspondences under large intra-class variations requires analyzing images at different levels, from features linked to semantics and context to local patterns, while being invariant to instance-specific details.
Ranked #1 on
Semantic correspondence
on Caltech-101
Unsupervised Image Matching and Object Discovery as Optimization
Learning with complete or partial supervision is powerful but relies on ever-growing human annotation efforts.
Ranked #2 on
Single-object colocalization
on Object Discovery
SFNet: Learning Object-aware Semantic Correspondence
We address the problem of semantic correspondence, that is, establishing a dense flow field between images depicting different instances of the same object or scene category.
Deformable kernel networks for guided depth map upsampling
We address the problem of upsampling a low-resolution (LR) depth map using a registered high-resolution (HR) color image of the same scene.
On the Solvability of Viewing Graphs
A set of fundamental matrices relating pairs of cameras in some configuration can be represented as edges of a "viewing graph".
Kernel Square-Loss Exemplar Machines for Image Retrieval
Zepeda and Perez have recently demonstrated the promise of the exemplar SVM (ESVM) as a feature encoder for image retrieval.
SCNet: Learning Semantic Correspondence
This paper addresses the problem of establishing semantic correspondences between images depicting different instances of the same object or scene category.
Proposal Flow: Semantic Correspondences from Object Proposals
Finding image correspondences remains a challenging problem in the presence of intra-class variations and large changes in scene layout.
General models for rational cameras and the case of two-slit projections
The rational camera model recently introduced in [19] provides a general methodology for studying abstract nonlinear imaging systems and their multi-view geometry.
Congruences and Concurrent Lines in Multi-View Geometry
We present a new framework for multi-view geometry in computer vision.
Consistency of Silhouettes and Their Duals
Silhouettes provide rich information on three-dimensional shape, since the intersection of the associated visual cones generates the "visual hull", which encloses and approximates the original shape.
The Joint Image Handbook
Given multiple perspective photographs, point correspondences form the "joint image", effectively a replica of three dimensional space distributed across its two-dimensional projections.
Proposal Flow
Finding image correspondences remains a challenging problem in the presence of intra-class variations and large changes in scene layout.~Semantic flow methods are designed to handle images depicting different instances of the same object or scene category.
Robust Image Filtering Using Joint Static and Dynamic Guidance
Regularizing images under a guidance signal has been used in various tasks in computer vision and computational photography, particularly for noise reduction and joint upsampling.
Weakly-Supervised Alignment of Video With Text
Given vectorial features for both video and text, we propose to cast this task as a temporal assignment problem, with an implicit linear mapping between the two feature modalities.
Unsupervised Object Discovery and Tracking in Video Collections
This paper addresses the problem of automatically localizing dominant objects as spatio-temporal tubes in a noisy collection of videos with minimal or even no supervision.
Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal
In this paper, we address the problem of estimating and removing non-uniform motion blur from a single blurry image.
Unsupervised Object Discovery and Localization in the Wild: Part-based Matching with Bottom-up Region Proposals
This paper addresses unsupervised discovery and localization of dominant objects from a noisy image collection with multiple object classes.
Sparse Modeling for Image and Vision Processing
In recent years, a large amount of multi-disciplinary research has been conducted on sparse models and their applications.
Weakly Supervised Action Labeling in Videos Under Ordering Constraints
We are given a set of video clips, each one annotated with an {\em ordered} list of actions, such as "walk" then "sit" then "answer phone" extracted from, for example, the associated text script.
Trinocular Geometry Revisited
When do the visual rays associated with triplets of point correspondences converge, that is, intersect in a common point?
Finding Matches in a Haystack: A Max-Pooling Strategy for Graph Matching in the Presence of Outliers
A major challenge in real-world feature matching problems is to tolerate the numerous outliers arising in typical visual tasks.
Learning to Estimate and Remove Non-uniform Image Blur
This paper addresses the problem of restoring images subjected to unknown and spatially varying blur caused by defocus or linear (say, horizontal) motion.
Efficient Optimization for Discriminative Latent Class Models
To avoid this problem, we introduce a local approximation of this cost function, which leads to a quadratic non-convex optimization problem over a product of simplices.
Task-Driven Dictionary Learning
Modeling data with linear combinations of a few elements from a learned dictionary has been the focus of much recent research in machine learning, neuroscience and signal processing.
Supervised Dictionary Learning
It is now well established that sparse signal models are well suited to restoration tasks and can effectively be learned from audio, image, and video data.
