Search Results for author:
Found 29 papers, 11 papers with code
Visual Prompting for Generalized Few-shot Segmentation: A Multi-scale Approach
These learned visual prompts are used to prompt a multiscale transformer decoder to facilitate accurate dense predictions.
System Identification of Neural Systems: Going Beyond Images to Modelling Dynamics
Towards this end, we propose a system identification study focused on comparing single image vs. video understanding models with respect to the visual cortex recordings.
A Survey on African Computer Vision Datasets, Topics and Researchers
The aim is to provide a survey of African computer vision topics, datasets and researchers.
TAM-VT: Transformation-Aware Multi-scale Video Transformer for Segmentation and Tracking
In this work we propose a novel, clip-based DETR-style encoder-decoder architecture, which focuses on systematically analyzing and addressing aforementioned challenges.
Two-stage Joint Transductive and Inductive learning for Nuclei Segmentation
In this study, we propose a novel approach to nuclei segmentation that leverages the available labelled and unlabelled data.
Multiscale Memory Comparator Transformer for Few-Shot Video Segmentation
We present a meta-learned Multiscale Memory Comparator (MMC) for few-shot video segmentation that combines information across scales within a transformer decoder.
Towards a Better Understanding of the Computer Vision Research Community in Africa
Moreover, we study all computer vision publications beyond top-tier venues in different African regions to find that mainly Northern and Southern Africa are publishing in computer vision with 68. 5% and 15. 9% of publications, resp.
A Unified Multiscale Encoder-Decoder Transformer for Video Segmentation
In this paper, we present an end-to-end trainable unified multiscale encoder-decoder transformer that is focused on dense prediction tasks in video.
Quantifying and Learning Static vs. Dynamic Information in Deep Spatiotemporal Networks
(ii) Some datasets that are assumed to be biased toward dynamics are actually biased toward static information.
A Deeper Dive Into What Deep Spatiotemporal Networks Encode: Quantifying Static vs. Dynamic Information
To show the efficacy of our approach, we analyse two widely studied tasks, action recognition and video object segmentation.
Temporal Transductive Inference for Few-Shot Video Object Segmentation
In this paper, we present a simple but effective temporal transductive inference (TTI) approach that leverages temporal consistency in the unlabelled video frames during few-shot inference.
Video Class Agnostic Segmentation with Contrastive Learning for Autonomous Driving
Semantic segmentation in autonomous driving predominantly focuses on learning from large-scale data with a closed set of known classes without considering unknown objects.
Video Class Agnostic Segmentation Benchmark for Autonomous Driving
We formalize the task of video class agnostic segmentation from monocular video sequences in autonomous driving to account for unknown objects.
Monocular Instance Motion Segmentation for Autonomous Driving: KITTI InstanceMotSeg Dataset and Multi-task Baseline
Moving object segmentation is a crucial task for autonomous vehicles as it can be used to segment objects in a class agnostic manner based on their motion cues.
Weakly Supervised Few-shot Object Segmentation using Co-Attention with Visual and Semantic Embeddings
Our results show that few-shot segmentation benefits from utilizing word embeddings, and that we are able to perform few-shot segmentation using stacked joint visual semantic processing with weak image-level labels.
One-Shot Weakly Supervised Video Object Segmentation
Conventional few-shot object segmentation methods learn object segmentation from a few labelled support images with strongly labelled segmentation masks.
AMP: Adaptive Masked Proxies for Few-Shot Segmentation
Our method is evaluated on PASCAL-5^i dataset and outperforms the state-of-the-art in the few-shot semantic segmentation.
Adaptive Masked Weight Imprinting for Few-Shot Segmentation
Deep learning has mainly thrived by training on large-scale datasets.
Adaptive Masked Proxies for Few-Shot Segmentation
Our method is evaluated on PASCAL-$5^i$ dataset and outperforms the state-of-the-art in the few-shot semantic segmentation.
Video Object Segmentation using Teacher-Student Adaptation in a Human Robot Interaction (HRI) Setting
A human teacher can show potential objects of interest to the robot, which is able to self adapt to the teaching signal without providing manual segmentation labels.
ShuffleSeg: Real-time Semantic Segmentation Network
It is shown that skip architecture in the decoding method provides the best compromise for the goal of real-time performance, while it provides adequate accuracy by utilizing higher resolution feature maps for a more accurate segmentation.
RTSeg: Real-time Semantic Segmentation Comparative Study
In this paper, we address this gap by presenting a real-time semantic segmentation benchmarking framework with a decoupled design for feature extraction and decoding methods.
MODNet: Moving Object Detection Network with Motion and Appearance for Autonomous Driving
Our experiments show that the proposed method outperforms state of the art methods that utilize motion cue only with 21. 5% in mAP on KITTI MOD.
Deep Semantic Segmentation for Automated Driving: Taxonomy, Roadmap and Challenges
In this paper, the semantic segmentation problem is explored from the perspective of automated driving.
4-DoF Tracking for Robot Fine Manipulation Tasks
One of these trackers is a newly developed learning based tracker that relies on learning discriminative correlation filters while the other is a refinement of a recent 8 DoF RANSAC based tracker adapted with a new appearance model for tracking 4 DoF motion.
Convolutional Gated Recurrent Networks for Video Segmentation
This architecture is tested for both binary and semantic video segmentation tasks.
Unifying Registration based Tracking: A Case Study with Structural Similarity
This paper adapts a popular image quality measure called structural similarity for high precision registration based tracking while also introducing a simpler and faster variant of the same.
Parking Stall Vacancy Indicator System Based on Deep Convolutional Neural Networks
Visual detection methods represent a cost-effective option, since they can take advantage of hardware usually already available in many parking lots, namely cameras.
Recurrent Fully Convolutional Networks for Video Segmentation
Accordingly, we propose a novel method for online segmentation of video sequences that incorporates temporal data.
