Search Results for author:
Found 65 papers, 33 papers with code
Multiple Instance Learning with Coarse-to-Fine Self-Distillation
In this work, we present PathMIL, a framework designed to improve MIL through two perspectives: (1) employing instance-level supervision and (2) learning inter-instance contextual information on bag level.
Spatially-Adaptive Hash Encodings For Neural Surface Reconstruction
Positional encodings are a common component of neural scene reconstruction methods, and provide a way to bias the learning of neural fields towards coarser or finer representations.
DepthCues: Evaluating Monocular Depth Perception in Large Vision Models
To investigate this, we examine whether the monocular depth cues, similar to those used by the human visual system, emerge in these models.
InstanSeg: an embedding-based instance segmentation algorithm optimized for accurate, efficient and portable cell segmentation
Cell and nucleus segmentation are fundamental tasks for quantitative bioimage analysis.
Odd-One-Out: Anomaly Detection by Comparing with Neighbors
This paper introduces a novel anomaly detection (AD) problem that focuses on identifying `odd-looking' objects relative to the other instances in a given scene.
Looking 3D: Anomaly Detection with 2D-3D Alignment
Automatic anomaly detection based on visual cues holds practical significance in various domains, such as manufacturing and product quality assessment.
Articulate your NeRF: Unsupervised articulated object modeling via conditional view synthesis
Given two observations of an object in different articulation states, our method learns the geometry and appearance of object parts by using an implicit model from the first observation, distils the part segmentation and articulation from the second observation while rendering the latter observation.
Coarse or Fine? Recognising Action End States without Labels
We use our synthetic data to train a model based on UNet and test it on real images showing coarsely/finely cut objects.
Improving Semantic Correspondence with Viewpoint-Guided Spherical Maps
To address these limitations, we propose a new approach for semantic correspondence estimation that supplements discriminative self-supervised features with 3D understanding via a weak geometric spherical prior.
Semi-supervised multimodal coreference resolution in image narrations
In this paper, we study multimodal coreference resolution, specifically where a longer descriptive text, i. e., a narration is paired with an image.
Multi-task Learning with 3D-Aware Regularization
Deep neural networks have become a standard building block for designing models that can perform multiple dense computer vision tasks such as depth estimation and semantic segmentation thanks to their ability to capture complex correlations in high dimensional feature space across tasks.
Explicit Neural Surfaces: Learning Continuous Geometry With Deformation Fields
We introduce Explicit Neural Surfaces (ENS), an efficient smooth surface representation that directly encodes topology with a deformation field from a known base domain.
Learning Action Changes by Measuring Verb-Adverb Textual Relationships
The goal of this work is to understand the way actions are performed in videos.
Ranked #2 on
Video-Adverb Retrieval
on HowTo100M Adverbs
Accelerating Self-Supervised Learning via Efficient Training Strategies
Recently the focus of the computer vision community has shifted from expensive supervised learning towards self-supervised learning of visual representations.
ViewNeRF: Unsupervised Viewpoint Estimation Using Category-Level Neural Radiance Fields
We introduce ViewNeRF, a Neural Radiance Field-based viewpoint estimation method that learns to predict category-level viewpoints directly from images during training.
ViewNet: Unsupervised Viewpoint Estimation from Conditional Generation
Understanding the 3D world without supervision is currently a major challenge in computer vision as the annotations required to supervise deep networks for tasks in this domain are expensive to obtain on a large scale.
Who are you referring to? Coreference resolution in image narrations
Coreference resolution aims to identify words and phrases which refer to same entity in a text, a core task in natural language processing.
RenderDiffusion: Image Diffusion for 3D Reconstruction, Inpainting and Generation
In this paper, we present RenderDiffusion, the first diffusion model for 3D generation and inference, trained using only monocular 2D supervision.
Learning to Annotate Part Segmentation with Gradient Matching
The success of state-of-the-art deep neural networks heavily relies on the presence of large-scale labelled datasets, which are extremely expensive and time-consuming to annotate.
Distilling Representations from GAN Generator via Squeeze and Span
In recent years, generative adversarial networks (GANs) have been an actively studied topic and shown to successfully produce high-quality realistic images in various domains.
FaceMAE: Privacy-Preserving Face Recognition via Masked Autoencoders
Firstly, randomly masked face images are used to train the reconstruction module in FaceMAE.
Synthesizing Informative Training Samples with GAN
However, traditional GANs generated images are not as informative as the real training samples when being used to train deep neural networks.
Universal Representations: A Unified Look at Multiple Task and Domain Learning
We propose a unified look at jointly learning multiple vision tasks and visual domains through universal representations, a single deep neural network.
3D Equivariant Graph Implicit Functions
In this work, we address two key limitations of such representations, in failing to capture local 3D geometric fine details, and to learn from and generalize to shapes with unseen 3D transformations.
CAFE: Learning to Condense Dataset by Aligning Features
Dataset condensation aims at reducing the network training effort through condensing a cumbersome training set into a compact synthetic one.
Learning Multiple Dense Prediction Tasks from Partially Annotated Data
Despite the recent advances in multi-task learning of dense prediction problems, most methods rely on expensive labelled datasets.
Not All Relations are Equal: Mining Informative Labels for Scene Graph Generation
Scene graph generation (SGG) aims to capture a wide variety of interactions between pairs of objects, which is essential for full scene understanding.
Dataset Condensation with Distribution Matching
Computational cost of training state-of-the-art deep models in many learning problems is rapidly increasing due to more sophisticated models and larger datasets.
Ranked #8 on
Dataset Distillation - 1IPC
on TinyImageNet
Visual Representation Learning over Latent Domains
A fundamental shortcoming of deep neural networks is their specialization to a single task and domain.
Cross-domain Few-shot Learning with Task-specific Adapters
In this paper, we look at the problem of cross-domain few-shot classification that aims to learn a classifier from previously unseen classes and domains with few labeled samples.
Ranked #4 on
Few-Shot Image Classification
on Meta-Dataset
cross-domain few-shot learning
Few-Shot Image Classification
Semi-supervised Viewpoint Estimation with Geometry-aware Conditional Generation
There is a growing interest in developing computer vision methods that can learn from limited supervision.
Learning Foreground-Background Segmentation from Improved Layered GANs
Deep learning approaches heavily rely on high-quality human supervision which is nonetheless expensive, time-consuming, and error-prone, especially for image segmentation task.
Universal Representation Learning from Multiple Domains for Few-shot Classification
In this paper, we look at the problem of few-shot classification that aims to learn a classifier for previously unseen classes and domains from few labeled samples.
Ranked #6 on
Few-Shot Image Classification
on Meta-Dataset
Dataset Condensation with Differentiable Siamese Augmentation
In many machine learning problems, large-scale datasets have become the de-facto standard to train state-of-the-art deep networks at the price of heavy computation load.
Ranked #5 on
Dataset Distillation - 1IPC
on CUB-200-2011
Reducing Implicit Bias in Latent Domain Learning
A fundamental shortcoming of deep neural networks is their specialization to a single task and domain.
Deep Anomaly Detection by Residual Adaptation
Deep anomaly detection is a difficult task since, in high dimensions, it is hard to completely characterize a notion of "differentness" when given only examples of normality.
Knowledge Distillation for Multi-task Learning
We then learn the multi-task model for minimizing task-specific loss and for producing the same feature with task-specific models.
Dataset Condensation with Gradient Matching
As the state-of-the-art machine learning methods in many fields rely on larger datasets, storing datasets and training models on them become significantly more expensive.
Ranked #4 on
Dataset Distillation - 1IPC
on CUB-200-2011
Continual Representation Learning for Biometric Identification
With the explosion of digital data in recent years, continuously learning new tasks from a stream of data without forgetting previously acquired knowledge has become increasingly important.
Latent Domain Learning with Dynamic Residual Adapters
While recent techniques in domain adaptation and multi-domain learning enable the learning of more domain-agnostic features, their success relies on the presence of domain labels, typically requiring manual annotation and careful curation of datasets.
iDLG: Improved Deep Leakage from Gradients
Particularly, our approach can certainly extract the ground-truth labels as opposed to DLG, hence we name it Improved DLG (iDLG).
Learning to Impute: A General Framework for Semi-supervised Learning
Recent semi-supervised learning methods have shown to achieve comparable results to their supervised counterparts while using only a small portion of labels in image classification tasks thanks to their regularization strategies.
Injecting Prior Knowledge into Image Caption Generation
Automatically generating natural language descriptions from an image is a challenging problem in artificial intelligence that requires a good understanding of the visual and textual signals and the correlations between them.
NormGrad: Finding the Pixels that Matter for Training
In this paper, we are rather interested by the locations of an image that contribute to the model's training.
Image Deconvolution with Deep Image and Kernel Priors
Image deconvolution is the process of recovering convolutional degraded images, which is always a hard inverse problem because of its mathematically ill-posed property.
Deep Multiple Instance Learning with Gaussian Weighting
The instance representation is shared by both instance classification and weighting streams.
Unsupervised Learning of Landmarks by Descriptor Vector Exchange
Equivariance to random image transformations is an effective method to learn landmarks of object categories, such as the eyes and the nose in faces, without manual supervision.
Ranked #1 on
Unsupervised Facial Landmark Detection
on 300W
Personalised aesthetics with residual adapters
The use of computational methods to evaluate aesthetics in photography has gained interest in recent years due to the popularization of convolutional neural networks and the availability of new annotated datasets.
Self-supervised Learning of Interpretable Keypoints from Unlabelled Videos
We propose KeypointGAN, a new method for recognizing the pose of objects from a single image that for learning uses only unlabelled videos and a weak empirical prior on the object poses.
Weakly Supervised Gaussian Networks for Action Detection
Detecting temporal extents of human actions in videos is a challenging computer vision problem that requires detailed manual supervision including frame-level labels.
Modelling and unsupervised learning of symmetric deformable object categories
We propose a new approach to model and learn, without manual supervision, the symmetries of natural objects, such as faces or flowers, given only images as input.
Unsupervised Learning of Object Landmarks through Conditional Image Generation
We propose a method for learning landmark detectors for visual objects (such as the eyes and the nose in a face) without any manual supervision.
Efficient parametrization of multi-domain deep neural networks
A practical limitation of deep neural networks is their high degree of specialization to a single task and visual domain.
Unsupervised learning of object frames by dense equivariant image labelling
One of the key challenges of visual perception is to extract abstract models of 3D objects and object categories from visual measurements, which are affected by complex nuisance factors such as viewpoint, occlusion, motion, and deformations.
Ranked #3 on
Unsupervised Facial Landmark Detection
on AFLW-MTFL
Learning multiple visual domains with residual adapters
There is a growing interest in learning data representations that work well for many different types of problems and data.
Unsupervised learning of object landmarks by factorized spatial embeddings
Learning automatically the structure of object categories remains an important open problem in computer vision.
Ranked #2 on
Unsupervised Facial Landmark Detection
on AFLW-MTFL
Universal representations:The missing link between faces, text, planktons, and cat breeds
With the advent of large labelled datasets and high-capacity models, the performance of machine vision systems has been improving rapidly.
Ranked #14 on
Continual Learning
on visual domain decathlon (10 tasks)
Action Recognition with Dynamic Image Networks
This is a powerful idea because it allows to convert any video to an image so that existing CNN models pre-trained for the analysis of still images can be immediately extended to videos.
Self-Supervised Video Representation Learning With Odd-One-Out Networks
On action classification, our method obtains 60. 3\% on the UCF101 dataset using only UCF101 data for training which is approximately 10% better than current state-of-the-art self-supervised learning methods.
Ranked #47 on
Self-Supervised Action Recognition
on UCF101
Integrated perception with recurrent multi-task neural networks
Modern discriminative predictors have been shown to match natural intelligences in specific perceptual tasks in image classification, object and part detection, boundary extraction, etc.
Dynamic Image Networks for Action Recognition
We introduce the concept of dynamic image, a novel compact representation of videos useful for video analysis especially when convolutional neural networks (CNNs) are used.
Ranked #62 on
Action Recognition
on HMDB-51
Weakly Supervised Deep Detection Networks
Weakly supervised learning of object detection is an important problem in image understanding that still does not have a satisfactory solution.
Ranked #3 on
Weakly Supervised Object Detection
on HICO-DET
Weakly Supervised Object Detection With Convex Clustering
However, as learning appearance and localization are two interconnected tasks, the optimization is not convex and the procedure can easily get stuck in a poor local minimum, the algorithm "misses" the object in some images.
Object Classification with Adaptable Regions
In classification of objects substantial work has gone into improving the low level representation of an image by considering various aspects such as different features, a number of feature pooling and coding techniques and considering different kernels.
