header

Profile


photo

M.Sc. Ali Athar
Room 129
Phone: +49 241 80 24663
Fax: +49 241 80 22731
Email: athar (at) vision.rwth-aachen.de



Publications


HODOR: High-level Object Descriptors for Object Re-segmentation in Video Learned from Static Images


Ali Athar, Jonathon Luiten, Alexander Hermans, Deva Ramanan, Bastian Leibe
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2022 (Oral)
pubimg

Existing state-of-the-art methods for Video Object Segmentation (VOS) learn low-level pixel-to-pixel correspondences between frames to propagate object masks across video. This requires a large amount of densely annotated video data, which is costly to annotate, and largely redundant since frames within a video are highly correlated. In light of this, we propose HODOR: a novel method that tackles VOS by effectively leveraging annotated static images for understanding object appearance and scene context. We encode object instances and scene information from an image frame into robust high-level descriptors which can then be used to re-segment those objects in different frames. As a result, HODOR achieves state-of-the-art performance on the DAVIS and YouTube-VOS benchmarks compared to existing methods trained without video annotations. Without any architectural modification, HODOR can also learn from video context around single annotated video frames by utilizing cyclic consistency, whereas other methods rely on dense, temporally consistent annotations.

» Show BibTeX

@article{Athar22CVPR,
title = {{HODOR: High-level Object Descriptors for Object Re-segmentation in Video Learned from Static Images}},
author = {Athar, Ali and Luiten, Jonathon and Hermans, Alexander and Ramanan, Deva and Leibe, Bastian},
journal = {{IEEE Conference on Computer Vision and Pattern Recognition (CVPR'22)}},
year = {2022}
}





Differentiable Soft-Masked Attention


Ali Athar, Jonathon Luiten, Alexander Hermans, Deva Ramanan, Bastian Leibe
Transformers for Vision Workshop at CVPR 2022

Transformers have become prevalent in computer vision due to their performance and flexibility in modelling complex operations. Of particular significance is the ‘cross-attention’ operation, which allows a vector representation (e.g. of an object in an image) to be learned by ‘attending’ to an arbitrarily sized set of input features. Recently, ‘Masked Attention’ was proposed in which a given object representation only attends to those image pixel features for which the segmentation mask of that object is active. This specialization of attention proved beneficial for various image and video segmentation tasks. In this paper, we propose another specialization of attention which enables attending over ‘soft-masks’ (those with continuous mask probabilities instead of binary values), and is also differentiable through these mask probabilities, thus allowing the mask used for attention to be learned within the network without requiring direct loss supervision. This can be useful for several applications. Specifically, we employ our ‘Differentiable Soft-Masked Attention’ for the task of Weakly Supervised Video Object Segmentation (VOS), where we develop a transformer-based network for VOS which only requires a single annotated image frame for training, but can also benefit from cycle consistency training on a video with just one annotated frame. Although there is no loss for masks in unlabeled frames, the network is still able to segment objects in those frames due to our novel attention formulation.




STEm-Seg: Spatio-temporal Embeddings for Instance Segmentation in Videos


Ali Athar, Sabarinath Mahadevan, Aljoša Ošep, Laura Leal-Taixé, Bastian Leibe
ECCV '20
pubimg

Existing methods for instance segmentation in videos typically involve multi-stage pipelines that follow the tracking-by-detection paradigm and model a video clip as a sequence of images. Multiple networks are used to detect objects in individual frames, and then associate these detections over time. Hence, these methods are often non-end-to-end trainable and highly tailored to specific tasks. In this paper, we propose a different approach that is well-suited to a variety of tasks involving instance segmentation in videos. In particular, we model a video clip as a single 3D spatio-temporal volume, and propose a novel approach that segments and tracks instances across space and time in a single stage. Our problem formulation is centered around the idea of spatio-temporal embeddings which are trained to cluster pixels belonging to a specific object instance over an entire video clip. To this end, we introduce (i) novel mixing functions that enhance the feature representation of spatio-temporal embeddings, and (ii) a single-stage, proposal-free network that can reason about temporal context. Our network is trained end-to-end to learn spatio-temporal embeddings as well as parameters required to cluster these embeddings, thus simplifying inference. Our method achieves state-of-the-art results across multiple datasets and tasks.

» Show Videos
» Show BibTeX

@inproceedings{AtharMahadevan20ECCV,
title={STEm-Seg: Spatio-temporal Embeddings for Instance Segmentation in Videos},
author={Athar, Ali and Mahadevan, Sabarinath and O{\v{s}}ep, Aljo{\v{s}}a and Leal-Taix{\'e}, Laura and Leibe, Bastian},
booktitle=ECCV,
year={2020}
}





Making a Case for 3D Convolutions for Object Segmentation in Videos


Sabarinath Mahadevan, Ali Athar, Aljoša Ošep, Sebastian Hennen, Laura Leal-Taixé, Bastian Leibe
BMVC'20
pubimg

The task of object segmentation in videos is usually accomplished by processing appearance and motion information separately using standard 2D convolutional networks, followed by a learned fusion of the two sources of information. On the other hand, 3D convolutional networks have been successfully applied for video classification tasks, but have not been leveraged as effectively to problems involving dense per-pixel interpretation of videos compared to their 2D convolutional counterparts and lag behind the aforementioned networks in terms of performance. In this work, we show that 3D CNNs can be effectively applied to dense video prediction tasks such as salient object segmentation. We propose a simple yet effective encoder-decoder network architecture consisting entirely of 3D convolutions that can be trained end-to-end using a standard cross-entropy loss. To this end, we leverage an efficient 3D encoder, and propose a 3D decoder architecture, that comprises novel 3D Global Convolution layers and 3D Refinement modules. Our approach outperforms existing state-of-the-arts by a large margin on the DAVIS'16 Unsupervised, FBMS and ViSal dataset benchmarks in addition to being faster, thus showing that our architecture can efficiently learn expressive spatio-temporal features and produce high quality video segmentation masks.

» Show Videos
» Show BibTeX

@inproceedings{Mahadevan20BMVC,
title={Making a Case for 3D Convolutions for Object Segmentation in Videos},
author={Mahadevan, Sabarinath and Athar, Ali and O{\v{s}}ep, Aljo{\v{s}}a and Hennen, Sebastian and Leal-Taix{\'e}, Laura and Leibe, Bastian},
booktitle={BMVC},
year={2020}
}





Disclaimer Home Visual Computing institute RWTH Aachen University