Improving Token-based Object Detection with Video

• URL: http://arxiv.org/abs/2506.22562v1
• Date: Fri, 27 Jun 2025 18:21:58 GMT
• Title: Improving Token-based Object Detection with Video
• Authors: Abhineet Singh, Nilanjan Ray,
• Abstract summary: This paper improves upon the Pix2Seq object detector by extending it for videos.<n>It represents objects as variable-length sequences of discrete tokens.<n>It outputs the video objects as fully integrated and indivisible 3D boxes or tracklets.
• Score: 4.995343972237369
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper improves upon the Pix2Seq object detector by extending it for videos. In the process, it introduces a new way to perform end-to-end video object detection that improves upon existing video detectors in two key ways. First, by representing objects as variable-length sequences of discrete tokens, we can succinctly represent widely varying numbers of video objects, with diverse shapes and locations, without having to inject any localization cues in the training process. This eliminates the need to sample the space of all possible boxes that constrains conventional detectors and thus solves the dual problems of loss sparsity during training and heuristics-based postprocessing during inference. Second, it conceptualizes and outputs the video objects as fully integrated and indivisible 3D boxes or tracklets instead of generating image-specific 2D boxes and linking these boxes together to construct the video object, as done in most conventional detectors. This allows it to scale effortlessly with available computational resources by simply increasing the length of the video subsequence that the network takes as input, even generalizing to multi-object tracking if the subsequence can span the entire video. We compare our video detector with the baseline Pix2Seq static detector on several datasets and demonstrate consistent improvement, although with strong signs of being bottlenecked by our limited computational resources. We also compare it with several video detectors on UA-DETRAC to show that it is competitive with the current state of the art even with the computational bottleneck. We make our code and models publicly available.

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