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jinxiang-liu authored Jun 1, 2024
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<br /> To measure the improvement by exploiting the unlabeled frames with GTM of the previous method including TPAVI and AVSegFormer, we establish the baseline by discarding the unlabeled frames and only using the labeled frames for model training. We perform experiments on AVSBench-S4 dataset and compare the performance with two typical methods TPAVI and AVSegFormer. The results on TPAVI baseline model are shown in the table, compared to the model trained with only labeled frames (w/o GTM), previous method such as TPAVI, based on global temporal modeling (w/ GTM) achieves only marginal performance gain: 0.14 gain with ResNet and 0.58 gain with PVT in mIoU. The results demonstrate the major issue of current methods: the underutilization of the unlabeled frames to boost the performance for the AVS task. Based on the observation, we intend to devise a more effective method to fully exploit the unlabeled frames for the AVS task.
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<img class="center" src="resources/algorithm.jpg" width="400px"></p>
<img class="center" src="resources/baselines.png" width="400px"></p>
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