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Found 19 papers, 4 papers with code
Knowing Where to Focus: Attention-Guided Alignment for Text-based Person Search
be masked fail to contribute efficient interaction in the cross-modal MLM and hampers the representation alignment.
RLE: A Unified Perspective of Data Augmentation for Cross-Spectral Re-identification
From this view, we unify all data augmentation strategies for cross-spectral re-identification by mimicking such local linear transformations and categorizing them into moderate transformation and radical transformation.
PartFormer: Awakening Latent Diverse Representation from Vision Transformer for Object Re-Identification
Extracting robust feature representation is critical for object re-identification to accurately identify objects across non-overlapping cameras.
Feature Denoising Diffusion Model for Blind Image Quality Assessment
Blind Image Quality Assessment (BIQA) aims to evaluate image quality in line with human perception, without reference benchmarks.
Prompt Decoupling for Text-to-Image Person Re-identification
In the first stage, we freeze the two encoders from CLIP and solely focus on optimizing the prompts to alleviate domain gap between the original training data of CLIP and downstream tasks.
Adaptive Feature Selection for No-Reference Image Quality Assessment by Mitigating Semantic Noise Sensitivity
Specifically, QFM-IQM enhances the semantic noise distinguish capabilities by matching image pairs with similar quality scores but varying semantic features as adversarial semantic noise and adaptively adjusting the upstream task's features by reducing sensitivity to adversarial noise perturbation.
Less is More: Learning Reference Knowledge Using No-Reference Image Quality Assessment
Concretely, by innovatively introducing a novel feature distillation method in IQA, we propose a new framework to learn comparative knowledge from non-aligned reference images.
Approximated Prompt Tuning for Vision-Language Pre-trained Models
Prompt tuning is a parameter-efficient way to deploy large-scale pre-trained models to downstream tasks by adding task-specific tokens.
Attention Disturbance and Dual-Path Constraint Network for Occluded Person Re-identification
To address this issue, we propose a novel transformer-based Attention Disturbance and Dual-Path Constraint Network (ADP) to enhance the generalization of attention networks.
Spectral Aware Softmax for Visible-Infrared Person Re-Identification
Based on the observation and analysis of SA-Softmax, we modify the SA-Softmax with the Feature Mask and Absolute-Similarity Term to alleviate the ambiguous optimization during model training.
Exploring Invariant Representation for Visible-Infrared Person Re-Identification
Cross-spectral person re-identification, which aims to associate identities to pedestrians across different spectra, faces a main challenge of the modality discrepancy.
Unsupervised Domain Adaptation on Person Re-Identification via Dual-level Asymmetric Mutual Learning
The knowledge transfer between two networks is based on an asymmetric mutual learning manner.
CycleTrans: Learning Neutral yet Discriminative Features for Visible-Infrared Person Re-Identification
Visible-infrared person re-identification (VI-ReID) is a task of matching the same individuals across the visible and infrared modalities.
Dynamic Prototype Mask for Occluded Person Re-Identification
Although person re-identification has achieved an impressive improvement in recent years, the common occlusion case caused by different obstacles is still an unsettled issue in real application scenarios.
Discover Cross-Modality Nuances for Visible-Infrared Person Re-Identification
In this paper, we propose a joint Modality and Pattern Alignment Network (MPANet) to discover cross-modality nuances in different patterns for visible-infrared person Re-ID, which introduces a modality alleviation module and a pattern alignment module to jointly extract discriminative features.
Occlude Them All: Occlusion-Aware Attention Network for Occluded Person Re-ID
To avoid such problematic models in occluded person ReID, we propose the Occlusion-Aware Mask Network (OAMN).
Learning Task-oriented Disentangled Representations for Unsupervised Domain Adaptation
This drawback limits the flexibility of UDA in complicated open-set tasks where no labels are shared between domains.
Dual Distribution Alignment Network for Generalizable Person Re-Identification
Domain generalization (DG) serves as a promising solution to handle person Re-Identification (Re-ID), which trains the model using labels from the source domain alone, and then directly adopts the trained model to the target domain without model updating.
Domain Generalization
Generalizable Person Re-identification
Video-based Person Re-identification with Two-stream Convolutional Network and Co-attentive Snippet Embedding
Recently, the applications of person re-identification in visual surveillance and human-computer interaction are sharply increasing, which signifies the critical role of such a problem.
Optical Flow Estimation
Video-Based Person Re-Identification
