Alina Kuznetsova
I received PhD degree (German: Dr.-Ing.) from Leibniz University of Hannover from Institute for Information Processing in 2016. During my PhD studies I mostly focused on free hand pose estimation from a single depth frame. Apart from that, I worked on general pose estimation and object detection and several other research projects. More details at akuznetso.github.io.
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Beyond SOT: Tracking Multiple Generic Objects at Once
Christoph Mayer
Martin Danelljan
Vittorio Ferrari
Luc Van Gool
WACV'24 (2024)
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Generic Object Tracking (GOT) is the problem of tracking target objects, specified by bounding boxes in the first frame of a video. While the task has received much attention in the last decades, researchers have almost exclusively focused on the single object setting. However multiobject GOT poses its own challenges and is more attractive in real-world applications. We attribute the lack of research interest into this problem to the absence of suitable benchmarks. In this work, we introduce a new largescale GOT benchmark, LaGOT, containing multiple annotated target objects per sequence. Our benchmark allows users to tackle key remaining challenges in GOT, aiming to increase robustness and reduce computation through joint
tracking of multiple objects simultaneously. In addition, we propose a transformer-based GOT tracker baseline capable of joint processing of multiple objects through shared computation. Our approach achieves a 4× faster run-time in case of 10 concurrent objects compared to tracking each object independently and outperforms existing single object trackers on our new benchmark. In addition, our approach achieves highly competitive results on single-object GOT datasets, setting a new state of the art on TrackingNet with a success rate AUC of 84.4%. Our benchmark, code, results and trained models are available at https://github.com/visionml/pytracking.
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Efficient video annotation with visual interpolation and frame selection guidance
Aakrati Talati
Keith Simmons
Vittorio Ferrari
WACV 2021, WACV 2021, pp. 14
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We introduce a unified framework for generic video annotation with bounding boxes. Video annotation is a long-standing problem, as it is a tedious and time-consuming process. We tackle two important challenges of video annotation: (1) automatic temporal interpolation and extrapolation of bounding boxes provided by a human annotator on a subset of all frames, and (2) automatic selection of frames
to annotate manually. Our contribution is two-fold: first, we propose a model that has both interpolating and extrapolating capabilities; second, we propose a guiding mechanism that sequentially generates suggestions for what frame to annotate next, based on the annotations made previously.
We extensively evaluate our approach on several challenging datasets in simulation and demonstrate a reduction in terms of the number of manual bounding boxes drawn by 60% over linear interpolation and by 35% over an off-the-shelf tracker. Moreover, we also show 10% annotation time improvement over a state-of-the-art method for video annotation with bounding boxes. Finally, we run human annotation experiments and provide extensive analysis of the results, showing that our approach reduces actual measured annotation time by 50% compared to commonly used linear interpolation.
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Factors of Influence for Transfer Learning across Diverse Appearance Domains and Task Types
Michael Gygli
Vittorio Ferrari
Transactions on Pattern Analysis and Machine Intelligence (TPAMI) (2021)
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Transfer learning enables to re-use knowledge learned on a source task to help learning a target task. A simple form of transfer learning is common in current state-of-the-art computer vision models, i.e. pre-training a model for image classification on the ILSVRC dataset, and then fine-tune on any target task. However, previous systematic studies of transfer learning have been limited and the circumstances in which it is expected to work are not fully understood. In this paper we carry out an extensive experimental exploration of transfer learning across vastly different image domains (consumer photos, autonomous driving, aerial imagery, underwater, indoor scenes, synthetic, close-ups) and task types (semantic segmentation, object detection, depth estimation, keypoint detection). Importantly, these are all complex, structured output tasks types relevant to modern computer vision applications.
In total we carry out over 2000 transfer learning experiments, including many where the source and target come from different image domains, task types, or both. We systematically analyze these experiments to understand the impact of image domain, task type, and dataset size on transfer learning performance. Our study leads to several insights and concrete recommendations: (1) for most tasks there exists a source which significantly outperforms ILSVRC'12 pre-training; (2) the image domain is the most important factor for achieving positive transfer; (3) the source dataset should \emph{include} the image domain of the target dataset to achieve best results; (4) at the same time, we observe only small negative effects when the image domain of the source task is much broader than that of the target; (5) transfer across task types can be beneficial, but its success is heavily dependent on both the source and target task types.
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The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale
Mohamad Hassan Mohamad Rom
Neil Alldrin
Ivan Krasin
Matteo Malloci
Vittorio Ferrari
IJCV (2020) (to appear)
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We present Open Images V4, a dataset of 9.2M images with unified annotations for image classification, object detection and visual relationship detection. The images have a Creative Commons Attribution license that allows to share and adapt the material, and they have been collected from Flickr without a predefined list of class names or tags, leading to natural class statistics and avoiding an initial design bias. Open Images V4 offers large scale across several dimensions: 30.1M image-level labels for 19.8k concepts, 15.4M bounding boxes for 600 object classes, and 375k visual relationship annotations involving 57 classes. For object detection in particular, we provide 15x more bounding boxes than the next largest datasets (15.4M boxes on 1.9M images). The images often show complex scenes with several objects (8 annotated objects per image on average). We annotated visual relationships between them, which support visual relationship detection, an emerging task that requires structured reasoning. We provide in-depth comprehensive statistics about the dataset, we validate the quality of the annotations, we study how the performance of several modern models evolves with increasing amounts of training data, and we demonstrate two applications made possible by having unified annotations of multiple types coexisting in the same images. We hope that the scale, quality, and variety of Open Images V4 will foster further research and innovation even beyond the areas of image classification, object detection, and visual relationship detection.
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