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<h1 class="title is-1 publication-title">SiamABC: Improving Accuracy and Generalization for Efficient Visual Tracking</h1>
<div class="is-size-5 publication-authors">
<!-- Paper authors -->
<span class="author-block">
<a href="https://ramzaveri.com/" target="_blank">Ram Zaveri</a>,</span>
<span class="author-block">
<a href="https://www.shivangapatel.com/" target="_blank">Shivang Patel</a>,</span>
<span class="author-block">
<a href="https://directory.statler.wvu.edu/faculty-staff-directory/yu-gu" target="_blank">Yu Gu</a>,
</span>
<span class="author-block">
<a href="https://vision.csee.wvu.edu/people/gianfranco-doretto/" target="_blank">Gianfranco Doretto</a>,
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block">West Virginia University<br>WACV, 2025</span>
</div>
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<a href="https://arxiv.org/pdf/2411.18855" target="_blank"
class="external-link button is-normal is-rounded is-dark">
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🤗
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</span>
<span>Demo</span>
</a>
</span> -->
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<a href="https://huggingface.co/collections/MVRL/SiamABC-datasets-672292b5a68683b4272950db" target="_blank"
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</span>
<span>Datasets</span>
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</span> -->
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<span>arXiv</span>
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</div>
</div>
</section>
<!-- Teaser video-->
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<img src="static/images/fig_qual.png" alt="SiamABC">
<h2 class="subtitle has-text-centered">
SiamABC (S-Tiny) is efficient and resilient against out-of-distribution tracking in adverse visibility conditions (AVisT benchmark).
</h2>
</div>
</div>
</section>
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<!-- Paper abstract -->
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<h2 class="title is-3">Abstract</h2>
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<p>
Efficient visual trackers overfit to their training distributions and lack generalization abilities, resulting in them performing well on their respective in-distribution (ID) test sets and not as well on out-of-distribution (OOD) sequences, imposing limitations to their deployment in-the-wild under constrained resources. We introduce SiamABC, a highly efficient Siamese tracker that significantly improves tracking performance, even on OOD sequences. SiamABC takes advantage of new architectural designs in the way it bridges the dynamic variability of the target, and of new losses for training. Also, it directly addresses OOD tracking generalization by including a fast backward-free dynamic test-time adaptation method that continuously adapts the model according to the dynamic visual changes of the target. Our extensive experiments suggest that SiamABC shows remarkable performance gains in OOD sets while maintaining accurate performance on the ID benchmarks. SiamABC outperforms MixFormerV2-S by 7.6% on the OOD AVisT benchmark while being 3x faster (100 FPS) on a CPU.
</p>
</div>
</div>
</div>
</div>
</section>
<!-- End paper abstract -->
<section class="hero is-small">
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<div class="container">
<h2 class="title is-3 has-text-centered"> Overall Approach</h2>
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<div class="column is-four-fifths">
<!-- First Image -->
<div class="publication-image" id="image-container-1">
<img src="static/images/approach.png" alt="method">
<h3>
The Feature Extraction Block uses a readily available backbone to process the frames. The RelationAware Block exploits representational relations among the dual-template and dual-search-region through our losses, where dual-template and dual-search-region representations are obtained via our learnable FMF layer. The Heads Block learns lightweight convolution layers to infer the bounding box and the classification score through standard tracking losses. During inference, the tracker adapts to every instance through our Dynamic Test-Time Adaptation framework.
<!-- </h3><br>
<img src="static/images/att.png" alt="method">
<h3>
We also introduce a novel lightweight and effective attention mechanism.
</h3> -->
</div>
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</div>
</section>
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<h2 class="title is-3 has-text-centered">OOD Comparison</h2>
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<!-- First Image -->
<div class="publication-image" id="image-container-1">
<img src="static/images/fig_quant.png" alt="method" style="max-width: 50%;">
<h3>
Comparison of our trackers with others on the AVisT dataset on a CPU. We show the success score (AUC) (vertical axis), speed (horizontal axis), and relative number of FLOPs (circles) of the trackers. Our trackers outperform other efficient trackers in terms of both speed and accuracy.
</h3>
</div>
</div>
</div>
</div>
</section>
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<h2 class="title is-3 has-text-centered">Dynamic Test-Time Adaptation </h2>
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<div class="column is-four-fifths">
<!-- First Image -->
<div class="publication-image" id="image-container-1">
<img src="static/images/table_4.png" alt="method" style="max-width: 70%;">
<h3>
Comparative study on test-time adaptation (TTA) approaches on AVisT as it involves various extreme distribution shifts with real-world corruptions and ITB as the next most challenging benchmark.
</h3>
</div>
</div>
</div>
</div>
</section>
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<h2 class="title is-3 has-text-centered">VOT benchmark Comparison</h2>
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<!-- First Image -->
<div class="publication-image" id="image-container-1">
<img src="static/images/table_1.png" alt="method">
<h3>
Comparative study on VOT2020 Benchmark.
</h3>
</div>
</div>
</div>
</div>
</section>
<section class="hero is-small">
<div class="hero-body">
<div class="container">
<h2 class="title is-3 has-text-centered">AVisT, NFS30, UAV123, TrackingNet, GOT-10k, and LaSOT benchmarks</h2>
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<div class="column is-four-fifths">
<!-- First Image -->
<div class="publication-image" id="image-container-1">
<img src="static/images/table_2.png" alt="method">
<h3>
Comparative Study with other SOTA approaches on various benchmarks including AVisT, NFS30, UAV123, TrackingNet, GOT-10k, and LaSOT.
</h3>
</div>
</div>
</div>
</div>
</section>
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<h2 class="title is-3 has-text-centered">ITB, OTB, TC128, and DTB70 benchmarks</h2>
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<div class="column is-four-fifths">
<!-- First Image -->
<div class="publication-image" id="image-container-1">
<img src="static/images/table_3.png" alt="method">
<h3>
Comparative study on ITB, OTB, TC128, and DTB70 benchmarks in terms of their AUC score.
</h3>
</div>
</div>
</div>
</div>
</section>
<!-- <section class="hero is-small">
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<h2 class="title is-3">🌏 Inference</h2>
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<h3>
We provide a simple way to access all our models through rshf and huggingface.
</h3>
<pre><code class="python">from transformers import PretrainedConfig
from rshf.taxabind import TaxaBind
config = PretrainedConfig.from_pretrained("MVRL/taxabind-config")
taxabind = TaxaBind(config)
# Loads open_clip style model
model = taxabind.get_image_text_encoder()
tokenizer = taxabind.get_tokenizer()
processor = taxabind.get_image_processor()</code></pre>
<h3>
For more information on how to load other encoders, please refer to the <a href="https://github.com/mvrl/TaxaBind?tab=readme-ov-file#%EF%B8%8F-usage" target="_blank">GitHub</a>.
</h3>
<!-- First Image -->
<div class="publication-image" id="image-container-1">
</div>
</div>
</div>
</div>
</section> -->
<!-- <section class="hero is-small">
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<h2 class="title is-3">🤗 HuggingFace Demo</h2>
<div class="columns is-centered">
<div class="column is-four-fifths">
<h3>
Demo of species image to satellite image retrieval using TaxaBind. This demo is on cpu so it may take a while!
</h3>
<div class="publication-image" id="image-container-1">
<iframe
src="https://mvrl-taxabind-demo.hf.space"
frameborder="0"
width="850"
height="650"
></iframe>
</div>
</div>
</div>
</div>
</section> -->
<!--BibTex citation -->
<section class="section" id="BibTeX">
<div class="container is-max-desktop content has-text-centered">
<h2 class="title">BibTeX</h2>
<pre><code>@inproceedings{zaveri2025siamabc,
title={Improving Accuracy and Generalization for Efficient Visual Tracking},
author={Zaveri, Ram and Patel, Shivang and Gu, Yu and Doretto, Gianfranco},
booktitle={Winter Conference on Applications of Computer Vision},
year={2025},
organization={IEEE/CVF}
}</code></pre>
</div>
</section>
<!--End BibTex citation -->
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