Papers Rgb-T Tracking
“Rgb-T Tracking” 태그가 달린 논문 57편 · 필터 해제
Group Orthogonal Low-Rank Adaptation for RGB-T Tracking
Parameter-efficient fine-tuning has emerged as a promising paradigm in RGB-T tracking, enabling downstream task adaptation by freezing pretrained parameters and fine-tuning only a small set of parameters. This set forms …
parameter-efficient fine-tuningRgb-T TrackingRobust RGB-T Tracking via Learnable Visual Fourier Prompt Fine-tuning and Modality Fusion Prompt Generation
Recently, visual prompt tuning is introduced to RGB-Thermal (RGB-T) tracking as a parameter-efficient finetuning (PEFT) method. However, these PEFT-based RGB-T tracking methods typically rely solely on spatial domain inf…
Visual Prompt TuningRgb-T TrackingLightweight RGB-T Tracking with Mobile Vision Transformers
Single-modality object tracking (e.g., RGB-only) encounters difficulties in challenging imaging conditions, such as low illumination and adverse weather conditions. To solve this, multimodal tracking (e.g., RGB-T models)…
GPUObject TrackingRgb-T TrackingModality-Guided Dynamic Graph Fusion and Temporal Diffusion for Self-Supervised RGB-T Tracking
To reduce the reliance on large-scale annotations, self-supervised RGB-T tracking approaches have garnered significant attention. However, the omission of the object region by erroneous pseudo-label or the introduction o…
DenoisingGraph AttentionPseudo LabelRgb-T TrackingBreaking Shallow Limits: Task-Driven Pixel Fusion for Gap-free RGBT Tracking
Current RGBT tracking methods often overlook the impact of fusion location on mitigating modality gap, which is key factor to effective tracking. Our analysis reveals that shallower fusion yields smaller distribution gap…
Representation LearningRgb-T TrackingAdaptive Perception for Unified Visual Multi-modal Object Tracking
Recently, many multi-modal trackers prioritize RGB as the dominant modality, treating other modalities as auxiliary, and fine-tuning separately various multi-modal tasks. This imbalance in modality dependence limits the …
Object TrackingRgb-T TrackingBTMTrack: Robust RGB-T Tracking via Dual-template Bridging and Temporal-Modal Candidate Elimination
RGB-T tracking leverages the complementary strengths of RGB and thermal infrared (TIR) modalities to address challenging scenarios such as low illumination and adverse weather. However, existing methods often fail to eff…
Rgb-T TrackingPURA: Parameter Update-Recovery Test-Time Adaption for RGB-T Tracking
Maintaining stable tracking of objects in domain shift scenarios is crucial for RGB-T tracking, prompting us to explore the use of unlabeled test sample information for effective online model adaptation. However, cur…
Rgb-T TrackingTest-time AdaptationSUTrack: Towards Simple and Unified Single Object Tracking
In this paper, we propose a simple yet unified single object tracking (SOT) framework, dubbed SUTrack. It consolidates five SOT tasks (RGB-based, RGB-Depth, RGB-Thermal, RGB-Event, RGB-Language Tracking) into a unified m…
Object TrackingRgb-T TrackingExploiting Multimodal Spatial-temporal Patterns for Video Object Tracking
Multimodal tracking has garnered widespread attention as a result of its ability to effectively address the inherent limitations of traditional RGB tracking. However, existing multimodal trackers mainly focus on the fusi…
MambaObject TrackingRgb-T TrackingVideo Object TrackingBreaking Modality Gap in RGBT Tracking: Coupled Knowledge Distillation
Modality gap between RGB and thermal infrared (TIR) images is a crucial issue but often overlooked in existing RGBT tracking methods. It can be observed that modality gap mainly lies in the image style difference. In thi…
Knowledge DistillationRgb-T TrackingCross Fusion RGB-T Tracking with Bi-directional Adapter
Many state-of-the-art RGB-T trackers have achieved remarkable results through modality fusion. However, these trackers often either overlook temporal information or fail to fully utilize it, resulting in an ineffective b…
Rgb-T TrackingRGBT Tracking via All-layer Multimodal Interactions with Progressive Fusion Mamba
Existing RGBT tracking methods often design various interaction models to perform cross-modal fusion of each layer, but can not execute the feature interactions among all layers, which plays a critical role in robust mul…
AllMambamultimodal interactionRgb-T TrackingMambaVT: Spatio-Temporal Contextual Modeling for robust RGB-T Tracking
Existing RGB-T tracking algorithms have made remarkable progress by leveraging the global interaction capability and extensive pre-trained models of the Transformer architecture. Nonetheless, these methods mainly adopt i…
MambaRgb-T TrackingCross-modulated Attention Transformer for RGBT Tracking
Existing Transformer-based RGBT trackers achieve remarkable performance benefits by leveraging self-attention to extract uni-modal features and cross-attention to enhance multi-modal feature interaction and template-sear…
Rgb-T TrackingTransformer-based RGB-T Tracking with Channel and Spatial Feature Fusion
How to better fuse cross-modal features is the core issue of RGB-T tracking. Some previous methods either insufficiently fuse RGB and TIR features, or depend on intermediaries containing information from both modalities …
Rgb-T TrackingAFter: Attention-based Fusion Router for RGBT Tracking
Multi-modal feature fusion as a core investigative component of RGBT tracking emerges numerous fusion studies in recent years. However, existing RGBT tracking methods widely adopt fixed fusion structures to integrate mul…
Neural Architecture SearchRgb-T TrackingRevisiting RGBT Tracking Benchmarks from the Perspective of Modality Validity: A New Benchmark, Problem, and Method
RGBT tracking draws increasing attention due to its robustness in multi-modality warranting (MMW) scenarios, such as nighttime and bad weather, where relying on a single sensing modality fails to ensure stable tracking r…
Mixture-of-ExpertsRgb-T TrackingMiddle Fusion and Multi-Stage, Multi-Form Prompts for Robust RGB-T Tracking
RGB-T tracking, a vital downstream task of object tracking, has made remarkable progress in recent years. Yet, it remains hindered by two major challenges: 1) the trade-off between performance and efficiency; 2) the scar…
FormObject TrackingPrompt LearningRgb-T TrackingFrom Two-Stream to One-Stream: Efficient RGB-T Tracking via Mutual Prompt Learning and Knowledge Distillation
Due to the complementary nature of visible light and thermal infrared modalities, object tracking based on the fusion of visible light images and thermal images (referred to as RGB-T tracking) has received increasing att…
Knowledge DistillationObject TrackingPrompt LearningRgb-T Tracking