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<h1 class="title is-1 publication-title">Context-Aware Integration of Language and Visual References for Natural Language Tracking</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://scholar.google.com/citations?user=k2PdG8kAAAAJ&hl=en">Yanyan Shao</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://heshuting555.github.io">Shuting He</a><sup>2</sup>,
</span>
<span class="author-block">
<a href="https://person.zju.edu.cn/en/yeqi/">Qi Ye</a><sup>3</sup>,
</span>
<span class="author-block">
<a href="">Yuchao Feng</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://whluo.github.io">Wenhan Luo</a><sup>4</sup>,
</span>
<span class="author-block">
<a href="https://person.zju.edu.cn/en/jmchen">Jiming Chen</a><sup>1,3</sup>,
</span>
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<span class="author-block"><sup>1</sup>Zhejiang University of Technology</span>
<span class="author-block"><sup>2</sup>Nanyang Technological University</span>
<span class="author-block"><sup>3</sup>Zhejiang University</span>
<span class="author-block"><sup>4</sup>The Hong Kong University of Science and Technology</span>
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<h2 class="subtitle has-text-centered" style="font-size: 16px">
Given a video sequence, the tracking object is characterized as “white bird on the left” of the initial frame. Existing
two-step approaches separately perform language-search matching (a) and appearance-search matching (b). However, “on the
left” which is inconsistent with the current target and the background contained in the grounded target may confuse the identification of the target.
In contrast, our QueryNLT (c) forms a dynamic and context-aware query for target localization by integrating visual and language references. (Zoom in for a better view).
</h2>
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<h2 class="title is-3">Abstract</h2>
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<p>
Tracking by natural language specification (TNL) aims
to consistently localize a target in a video sequence
given a linguistic description in the initial frame. Existing methodologies perform language-based and templatebased matching for target reasoning separately and merge
the matching results from two sources, which suffer from
tracking drift when language and visual templates missalign with the dynamic target state and ambiguity in the
later merging stage. To tackle the issues, we propose a joint
multi-modal tracking framework with 1) a prompt modulation module to leverage the complementarity between
temporal visual templates and language expressions, enabling precise and context-aware appearance and linguistic
cues, and 2) a unified target decoding module to integrate
the multi-modal reference cues and executes the integrated
queries on the search image to predict the target location in
an end-to-end manner directly. This design ensures spatiotemporal consistency by leveraging historical visual information and introduces an integrated solution, generating
predictions in a single step. Extensive experiments conducted on TNL2K, OTB-Lang, LaSOT, and RefCOCOg validate the efficacy of our proposed approach. The results
demonstrate competitive performance against state-of-theart methods for both tracking and grounding.
</p>
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To address the limitations, in this paper, we propose to leverage spatio-temporal information of
hand-object interaction to track interactive objects under these challenging cases.
Without prior knowledge of the general objects to be tracked like object tracking problems,
we first utilize the spatial relation between hands and objects to adaptively discover
the interacting objects from the scene.
Second, the consistency and continuity of the appearance of objects between successive frames are
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The quantitative results demonstrate that our proposed
method outperforms the state-of-the-art methods. Specifically, in scenes with continuous interaction with
different objects, we achieve an impressive improvement of about 10% as evaluated using the Average
Precision (AP) metric. Our qualitative findings also illustrate that our method can produce more
continuous trajectories for interacting objects.
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Overview of our proposed framework. It comprises three key components: a feature extraction module for extracting image and
text features, a prompt modulation module that generates precise appearance and language descriptions of the target, and a target decoding
module that jointly establishes the correlation between the search image and the multi-modal target prompts for target retrieval.
</h2>
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<h2 class="title">BibTeX</h2>
<pre><code>@article{tio2023,
author = {Shao, Yanyan and Shuting, He and Qi, Ye and Yuchao, Feng and Wenhan, Luo and Jiming, Chen},
title = {Context-Aware Integration of Language and Visual References for Natural Language Tracking},
journal = {CVPR},
year = {2024},
}</code></pre>
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