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

• URL: http://arxiv.org/abs/2312.08514v2
• Date: Tue, 9 Apr 2024 18:23:39 GMT
• Title: TAM-VT: Transformation-Aware Multi-scale Video Transformer for Segmentation and Tracking
• Authors: Raghav Goyal, Wan-Cyuan Fan, Mennatullah Siam, Leonid Sigal,
• Abstract summary: Video Object (VOS) has emerged as an increasingly important problem with availability of larger datasets and more complex and realistic settings. 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 that focuses learning on portions of the video where an object undergoes significant deformations.
• Score: 33.75267864844047
• License: http://creativecommons.org/licenses/by/4.0/
• 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 that focuses 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 multi-scale 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 and VOST, while achieving comparable to SoTA results on the conventional VOS benchmark, DAVIS'17. A series of detailed ablations validate our design choices as well as provide insights into the importance of parameter choices and their impact on performance.

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