Search Results for author:
Found 12 papers, 4 papers with code
Breaking Modality Gap in RGBT Tracking: Coupled Knowledge Distillation
To handle this issue, we take original RGB and TIR networks as the teachers, and distill their content knowledge into two student networks respectively by the style-content orthogonal feature decoupling scheme.
Ranked #3 on
Rgb-T Tracking
on RGBT210
RGBT 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 multimodal representation, due to large computational burden.
Cross-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-search correlation computation.
Ranked #6 on
Rgb-T Tracking
on GTOT
AFter: Attention-based Fusion Router for RGBT Tracking
In particular, we design a fusion structure space based on the hierarchical attention network, each attention-based fusion unit corresponding to a fusion operation and a combination of these attention units corresponding to a fusion structure.
Ranked #5 on
Rgb-T Tracking
on RGBT234
Transformer RGBT Tracking with Spatio-Temporal Multimodal Tokens
We introduce independent dynamic template tokens to interact with the search region, embedding temporal information to address appearance changes, while also retaining the involvement of the initial static template tokens in the joint feature extraction process to ensure the preservation of the original reliable target appearance information that prevent deviations from the target appearance caused by traditional temporal updates.
Ranked #13 on
Rgb-T Tracking
on RGBT210
Nighttime Person Re-Identification via Collaborative Enhancement Network with Multi-domain Learning
To perform effective collaborative modeling between image relighting and person ReID tasks, we integrate the multilevel feature interactions in CENet.
Modality-missing RGBT Tracking: Invertible Prompt Learning and High-quality Benchmarks
To address this challenge, we propose a novel invertible prompt learning approach, which integrates the content-preserving prompts into a well-trained tracking model to adapt to various modality-missing scenarios, for robust RGBT tracking.
Illumination Distillation Framework for Nighttime Person Re-Identification and A New Benchmark
The illumination enhancement branch first estimates an enhanced image from the nighttime image using a nonlinear curve mapping method and then extracts the enhanced features.
RGBT Tracking via Multi-Adapter Network with Hierarchical Divergence Loss
In specific, we use the modified VGG-M as the generality adapter to extract the modality-shared target representations. To extract the modality-specific features while reducing the computational complexity, we design a modality adapter, which adds a small block to the generality adapter in each layer and each modality in a parallel manner.
Ranked #13 on
Rgb-T Tracking
on GTOT
Duality-Gated Mutual Condition Network for RGBT Tracking
To deal with the tracking failure caused by sudden camera motion, which often occurs in RGBT tracking, we design a resampling strategy based on optical flow algorithms.
Ranked #26 on
Rgb-T Tracking
on RGBT234
Challenge-Aware RGBT Tracking
RGB and thermal source data suffer from both shared and specific challenges, and how to explore and exploit them plays a critical role to represent the target appearance in RGBT tracking.
Ranked #12 on
Rgb-T Tracking
on GTOT
Multi-Adapter RGBT Tracking
In a specific, the generality adapter is to extract shared object representations, the modality adapter aims at encoding modality-specific information to deploy their complementary advantages, and the instance adapter is to model the appearance properties and temporal variations of a certain object.
