Search Results for author:
Found 30 papers, 17 papers with code
Polar Prototype Networks
The structure is defined by polar prototypes, points on the hypersphere of the output space.
Maximum Class Separation as Inductive Bias in One Matrix
This paper proposes a simple alternative: encoding maximum separation as an inductive bias in the network by adding one fixed matrix multiplication before computing the softmax activations.
Less than Few: Self-Shot Video Instance Segmentation
The goal of this paper is to bypass the need for labelled examples in few-shot video understanding at run time.
Hyperbolic Image Segmentation
For image segmentation, the current standard is to perform pixel-level optimization and inference in Euclidean output embedding spaces through linear hyperplanes.
Universal Prototype Transport for Zero-Shot Action Recognition and Localization
For universal action models, we first seek to find a hyperspherical optimal transport mapping from unseen action prototypes to the set of all projected test videos.
Zero-Shot Action Recognition from Diverse Object-Scene Compositions
This paper investigates the problem of zero-shot action recognition, in the setting where no training videos with seen actions are available.
Diagnosing Errors in Video Relation Detectors
Video relation detection forms a new and challenging problem in computer vision, where subjects and objects need to be localized spatio-temporally and a predicate label needs to be assigned if and only if there is an interaction between the two.
Transductive Universal Transport for Zero-Shot Action Recognition
For universal object models, we outline a weighted transport variant from unseen action embeddings to object embeddings directly.
Social Fabric: Tubelet Compositions for Video Relation Detection
We also propose Social Fabric: an encoding that represents a pair of object tubelets as a composition of interaction primitives.
Frequency-Supervised MR-to-CT Image Synthesis
In this paper, we find that all existing approaches share a common limitation: reconstruction breaks down in and around the high-frequency parts of CT images.
On Measuring and Controlling the Spectral Bias of the Deep Image Prior
The deep image prior showed that a randomly initialized network with a suitable architecture can be trained to solve inverse imaging problems by simply optimizing it's parameters to reconstruct a single degraded image.
Hyperbolic Busemann Learning with Ideal Prototypes
To be able to compute proximities to ideal prototypes, we introduce the penalised Busemann loss.
Unsharp Mask Guided Filtering
The state-of-the-art leverages deep networks to estimate the two core coefficients of the guided filter.
Object Priors for Classifying and Localizing Unseen Actions
This work strives for the classification and localization of human actions in videos, without the need for any labeled video training examples.
Few-Shot Transformation of Common Actions into Time and Space
This paper introduces the task of few-shot common action localization in time and space.
Adversarial Self-Supervised Scene Flow Estimation
This work proposes a metric learning approach for self-supervised scene flow estimation.
PointMixup: Augmentation for Point Clouds
In this paper, we define data augmentation between point clouds as a shortest path linear interpolation.
Ranked #3 on
3D Point Cloud Data Augmentation
on ModelNet40
3D Point Cloud Classification
3D Point Cloud Data Augmentation
+2
Localizing the Common Action Among a Few Videos
The start and end of an action in a long untrimmed video is determined based on just a hand-full of trimmed video examples containing the same action, without knowing their common class label.
Open Cross-Domain Visual Search
In this paper, we make the step towards an open setting where multiple visual domains are available.
4-Connected Shift Residual Networks
Interestingly, when incorporating shifts to all point-wise convolutions in residual networks, 4-connected shifts outperform 8-connected shifts.
Counting with Focus for Free
To assist both the density estimation and the focus from segmentation, we also introduce an improved kernel size estimator for the point annotations.
Hyperspherical Prototype Networks
This paper introduces hyperspherical prototype networks, which unify classification and regression with prototypes on hyperspherical output spaces.
Spatio-Temporal Instance Learning: Action Tubes from Class Supervision
Rather than disconnecting the spatio-temporal learning from the training, we propose Spatio-Temporal Instance Learning, which enables action localization directly from box proposals in video frames.
Multiple Instance Learning
Spatio-Temporal Action Localization
+1
Pointly-Supervised Action Localization
Experimental evaluation on three action localization datasets shows our pointly-supervised approach (i) is as effective as traditional box-supervision at a fraction of the annotation cost, (ii) is robust to sparse and noisy point annotations, (iii) benefits from pseudo-points during inference, and (iv) outperforms recent weakly-supervised alternatives.
Localizing Actions from Video Labels and Pseudo-Annotations
The goal of this paper is to determine the spatio-temporal location of actions in video.
Spatial-Aware Object Embeddings for Zero-Shot Localization and Classification of Actions
Action localization and classification experiments on four contemporary action video datasets support our proposal.
Spot On: Action Localization from Pointly-Supervised Proposals
Rather than annotating boxes, we propose to annotate actions in video with points on a sparse subset of frames only.
The ImageNet Shuffle: Reorganized Pre-training for Video Event Detection
To deal with the problems of over-specific classes and classes with few images, we introduce a bottom-up and top-down approach for reorganization of the ImageNet hierarchy based on all its 21, 814 classes and more than 14 million images.
Water Detection through Spatio-Temporal Invariant Descriptors
Experimental evaluation on the Video Water Database and the DynTex database indicates the effectiveness of the proposed algorithm, outperforming multiple algorithms for dynamic texture recognition and material recognition by ca.
No Spare Parts: Sharing Part Detectors for Image Categorization
This work aims for image categorization using a representation of distinctive parts.
