Single object tracking aims to locate one specific target in video sequences,
given its initial state. Classical trackers rely solely on visual cues,
restricting their ability to handle challenges such as appearance variations,
ambiguity, and distractions. Hence, Vision-Language (VL) tracking has emerged
as a promising approach, incorporating language descriptions to directly
provide high-level semantics and enhance tracking performance. However, current
VL trackers have not fully exploited the power of VL learning, as they suffer
from limitations such as heavily relying on off-the-shelf backbones for feature
extraction, ineffective VL fusion designs, and the absence of VL-related loss
functions. Consequently, we present a novel tracker that progressively explores
target-centric semantics for VL tracking. Specifically, we propose the first
Synchronous Learning Backbone (SLB) for VL tracking, which consists of two
novel modules: the Target Enhance Module (TEM) and the Semantic Aware Module
(SAM). These modules enable the tracker to perceive target-related semantics
and comprehend the context of both visual and textual modalities at the same
pace, facilitating VL feature extraction and fusion at different semantic
levels. Moreover, we devise the dense matching loss to further strengthen
multi-modal representation learning. Extensive experiments on VL tracking
datasets demonstrate the superiority and effectiveness of our methods.

这篇研究论文介绍了一种新颖的追踪器，通过逐步探索目标中心语义来进行视觉 - 语言追踪，其中包括两个关键模块：目标增强模块（TEM）和语义感知模块（SAM），并采用密集匹配损失以增强多模态表示学习。实验证明了该方法的优越性和有效性。

超越视觉线索：同步探索面向目标的语义用于视觉 - 语言追踪

Beyond Visual Cues: Synchronously Exploring Target-Centric Semantics for  Vision-Language Tracking

In this paper, we present a simple, flexible and effective vision-language
(VL) tracking pipeline, termed \textbf{MMTrack}, which casts VL tracking as a
token generation task. Traditional paradigms address VL tracking task
indirectly with sophisticated prior designs, making them over-specialize on the
features of specific architectures or mechanisms. In contrast, our proposed
framework serializes language description and bounding box into a sequence of
discrete tokens. In this new design paradigm, all token queries are required to
perceive the desired target and directly predict spatial coordinates of the
target in an auto-regressive manner. The design without other prior modules
avoids multiple sub-tasks learning and hand-designed loss functions,
significantly reducing the complexity of VL tracking modeling and allowing our
tracker to use a simple cross-entropy loss as unified optimization objective
for VL tracking task. Extensive experiments on TNL2K, LaSOT, LaSOT$_{\rm{ext}}$
and OTB99-Lang benchmarks show that our approach achieves promising results,
compared to other state-of-the-arts.

该研究提出了一种简单、灵活且有效的视觉 - 语言（VL）跟踪管道，称为 MMTrack，它将 VL 跟踪视为一个标记生成任务，并通过将语言描述和边界框序列化为一系列离散标记来实现。通过避免多个子任务的学习和手动设计的损失函数，该设计范式极大地降低了 VL 跟踪建模的复杂性，并允许跟踪器使用简单的交叉熵损失作为统一的优化目标。在 TNL2K、LaSOT、LaSOT_ext 和 OTB99-Lang 基准测试上的广泛实验表明，与其他最先进的方法相比，我们的方法取得了令人满意的结果。

迈向视觉语言跟踪的统一标记学习

Towards Unified Token Learning for Vision-Language Tracking

Current mainstream vision-language (VL) tracking framework consists of three
parts, \ie a visual feature extractor, a language feature extractor, and a
fusion model. To pursue better performance, a natural modus operandi for VL
tracking is employing customized and heavier unimodal encoders, and multi-modal
fusion models. Albeit effective, existing VL trackers separate feature
extraction and feature integration, resulting in extracted features that lack
semantic guidance and have limited target-aware capability in complex
scenarios, \eg similar distractors and extreme illumination. In this work,
inspired by the recent success of exploring foundation models with unified
architecture for both natural language and computer vision tasks, we propose an
All-in-One framework, which learns joint feature extraction and interaction by
adopting a unified transformer backbone. Specifically, we mix raw vision and
language signals to generate language-injected vision tokens, which we then
concatenate before feeding into the unified backbone architecture. This
approach achieves feature integration in a unified backbone, removing the need
for carefully-designed fusion modules and resulting in a more effective and
efficient VL tracking framework. To further improve the learning efficiency, we
introduce a multi-modal alignment module based on cross-modal and intra-modal
contrastive objectives, providing more reasonable representations for the
unified All-in-One transformer backbone. Extensive experiments on five
benchmarks, \ie OTB99-L, TNL2K, LaSOT, LaSOT$_{\rm Ext}$ and WebUAV-3M,
demonstrate the superiority of the proposed tracker against existing
state-of-the-arts on VL tracking. Codes will be made publicly available.

现有主流的视觉语言（VL）跟踪框架由三部分组成，即视觉特征提取器，语言特征提取器和融合模型。本文提出了一个全新的、一体化的框架，通过采用统一的 Transformer 骨干结构，学习联合特征提取和交互，实现了特征的统一融合，消除了特征集成和融合模块的需求，从而在视觉语言跟踪方面获得了更有效和高效的结果。