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View article: Leveraging Prior Knowledge of Diffusion Model for Person Search
Leveraging Prior Knowledge of Diffusion Model for Person Search Open
Person search aims to jointly perform person detection and re-identification by localizing and identifying a query person within a gallery of uncropped scene images. Existing methods predominantly utilize ImageNet pre-trained backbones, wh…
View article: Visual Representation Alignment for Multimodal Large Language Models
Visual Representation Alignment for Multimodal Large Language Models Open
Multimodal large language models (MLLMs) trained with visual instruction tuning have achieved strong performance across diverse tasks, yet they remain limited in vision-centric tasks such as object counting or spatial reasoning. We attribu…
View article: Domain Generalization for Person Re-identification: A Survey Towards Domain-Agnostic Person Matching
Domain Generalization for Person Re-identification: A Survey Towards Domain-Agnostic Person Matching Open
Person Re-identification (ReID) aims to retrieve images of the same individual captured across non-overlapping camera views, making it a critical component of intelligent surveillance systems. Traditional ReID methods assume that the train…
View article: AceVFI: A Comprehensive Survey of Advances in Video Frame Interpolation
AceVFI: A Comprehensive Survey of Advances in Video Frame Interpolation Open
Video Frame Interpolation (VFI) is a fundamental Low-Level Vision (LLV) task that synthesizes intermediate frames between existing ones while maintaining spatial and temporal coherence. VFI techniques have evolved from classical motion com…
View article: Subnet-Aware Dynamic Supernet Training for Neural Architecture Search
Subnet-Aware Dynamic Supernet Training for Neural Architecture Search Open
N-shot neural architecture search (NAS) exploits a supernet containing all candidate subnets for a given search space. The subnets are typically trained with a static training strategy (e.g., using the same learning rate (LR) scheduler and…
View article: Bi-directional Contrastive Learning for Domain Adaptive Semantic Segmentation
Bi-directional Contrastive Learning for Domain Adaptive Semantic Segmentation Open
We present a novel unsupervised domain adaptation method for semantic segmentation that generalizes a model trained with source images and corresponding ground-truth labels to a target domain. A key to domain adaptive semantic segmentation…
View article: Disentangled Representations for Short-Term and Long-Term Person Re-Identification
Disentangled Representations for Short-Term and Long-Term Person Re-Identification Open
We address the problem of person re-identification (reID), that is, retrieving person images from a large dataset, given a query image of the person of interest. A key challenge is to learn person representations robust to intra-class vari…
View article: Video-based Person Re-identification with Spatial and Temporal Memory Networks
Video-based Person Re-identification with Spatial and Temporal Memory Networks Open
Video-based person re-identification (reID) aims to retrieve person videos with the same identity as a query person across multiple cameras. Spatial and temporal distractors in person videos, such as background clutter and partial occlusio…
View article: Learning Disentangled Representation for Robust Person Re-identification
Learning Disentangled Representation for Robust Person Re-identification Open
We address the problem of person re-identification (reID), that is, retrieving person images from a large dataset, given a query image of the person of interest. A key challenge is to learn person representations robust to intra-class vari…
View article: Temporally Consistent Depth Prediction with Flow-Guided Memory Units
Temporally Consistent Depth Prediction with Flow-Guided Memory Units Open
Predicting depth from a monocular video sequence is an important task for autonomous driving. Although it has advanced considerably in the past few years, recent methods based on convolutional neural networks (CNNs) discard temporal cohere…