TAM-VT: Transformation-Aware Multi-Scale Video Transformer for Segmentation and Tracking

Video Object Segmentation (VOS) has emerged as an increasingly important problem with availability of larger datasets and more complex and realistic settings, which involve long videos with global motion (e.g., in egocentric settings), depicting small objects undergoing both rigid and non-rigid (including state) deformations. While a number of recent approaches have been explored for this task, these data characteristics still present challenges. In this work we propose a novel, clip-based DETR-style encoder-decoder architecture, which focuses on systematically analyzing and addressing aforementioned challenges. Specifically, we propose a novel transformation-aware loss thatfocuses learning on portions of the video where an object undergoes significant deformations - a form of “soft” hard examples mining. Further, we propose a multiplicative time-coded memory, beyond vanilla additive positional encoding, which helps propagate context across long videos. Finally, we incorporate these in our proposed holistic multiscale video transformer for tracking via multi-scale memory matching and decoding to ensure sensitivity and accuracy for long videos and small objects. Our model enables on-line inference with long videos in a windowed fashion, by breaking the video into clips and propagating context among them. We illustrate that short clip length and longer memory with learned time-coding are important design choices for improved performance. Collectively, these technical contributions enable our model to achieve new state-of-the-art (SoTA) performance on two complex egocentric datasets - VISOR [13] and VOST [44], while achieving comparable to SoTA results on the conventional VOS benchmark, DAVIS’ 17 [38]. Detailed ablations validate our design choices and provide insights into the importance of parameter choices and impact on performance.<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>Code is available at the project page.

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Publication Details

Published
2025-02-26
DOI
https://doi.org/10.1109/wacv61041.2025.00808
Citations
1
Primary Topic
Image Processing Techniques and Applications
Type
article
Field-Weighted Citation Impact
1.14
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article

TAM-VT: Transformation-Aware Multi-Scale Video Transformer for Segmentation and Tracking

Raghav Goyal, Mennatullah Siam, Wan-Cyuan Fan, Leonid Sigal
1 citations
Image Processing Techniques and Applications
1.14
article

TAM-VT: Transformation-Aware Multi-Scale Video Transformer for Segmentation and Tracking

Raghav Goyal, Mennatullah Siam, Wan-Cyuan Fan, Leonid Sigal
article en
1 citations

Abstract

Video Object Segmentation (VOS) has emerged as an increasingly important problem with availability of larger datasets and more complex and realistic settings, which involve long videos with global motion (e.g., in egocentric settings), depicting small objects undergoing both rigid and non-rigid (including state) deformations. While a number of recent approaches have been explored for this task, these data characteristics still present challenges. In this work we propose a novel, clip-based DETR-style encoder-decoder architecture, which focuses on systematically analyzing and addressing aforementioned challenges. Specifically, we propose a novel transformation-aware loss thatfocuses learning on portions of the video where an object undergoes significant deformations - a form of “soft” hard examples mining. Further, we propose a multiplicative time-coded memory, beyond vanilla additive positional encoding, which helps propagate context across long videos. Finally, we incorporate these in our proposed holistic multiscale video transformer for tracking via multi-scale memory matching and decoding to ensure sensitivity and accuracy for long videos and small objects. Our model enables on-line inference with long videos in a windowed fashion, by breaking the video into clips and propagating context among them. We illustrate that short clip length and longer memory with learned time-coding are important design choices for improved performance. Collectively, these technical contributions enable our model to achieve new state-of-the-art (SoTA) performance on two complex egocentric datasets - VISOR [13] and VOST [44], while achieving comparable to SoTA results on the conventional VOS benchmark, DAVIS’ 17 [38]. Detailed ablations validate our design choices and provide insights into the importance of parameter choices and impact on performance.<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>Code is available at the project page.

University of British Columbia (CA), Universidad Braulio Carrillo (CR)
Openalex Percentile: Top 22%
Image Processing Techniques and Applications
1.14
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