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zichuanlin
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@@ -429,9 +429,17 @@ <h2 class="title is-3"><img id="painting_icon" width="3%" src="https://cdn-icons
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<div class="container is-max-desktop">
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<div class="columns is-centered">
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<h2 class="title is-4"><img id="painting_icon" width="4%" src="https://cdn-icons-png.flaticon.com/512/1698/1698535.png"> <span style="font-size: 100%;">Visual Search:</span> Mini-o3 achieves the state-of-the-art results among various benchmarks </h2>
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<h2 class="title is-4"><img id="painting_icon" width="4%" src="https://cdn-icons-png.flaticon.com/512/1698/1698535.png"> AdaptVision achieves a sota-level accuracy-efficiency trade-off across multiple VQA benchmarks </h2>
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<div>
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<style>
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<div class="columns is-centered">
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<centering>
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<div style="text-align: center;">
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<img id="result" width="100%" src="images/main_result.png">
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</div>
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</centering>
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</div>
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<!-- <style>
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table.perf-table {
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border-collapse: collapse;
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width: 95%;
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color: #444;
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margin-top: 6px;
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}
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</style>
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</style> -->
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<table class="perf-table">
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<!-- <table class="perf-table">
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<caption>
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Performance comparisons among existing models and ours on visual search tasks.
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The sizes of all listed models are 7B. We report Avg@32 to reduce variance caused by randomness.
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Performance comparison with previous efficient VLM methods.
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Vanilla denotes the Qwen2.5-VL-7B-Instruct model. Down-Sample uses a 1/4-resolution image as input to the Vanilla model.
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``\#Token'' indicates the visual token consumption ratio relative to the vanilla model across all benchmarks.
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``Avg.'' denotes the average performance relative to the vanilla model on all benchmarks.
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</caption>
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<thead>
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<td>65.5</td>
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</table>
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</table> -->
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<div class="table-notes">
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<div>† The models only report the metric of Avg@1 and the model weights are not available.</div>

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