Search Results for author:
Found 27 papers, 13 papers with code
MoTaDual: Modality-Task Dual Alignment for Enhanced Zero-shot Composed Image Retrieval
Composed Image Retrieval (CIR) is a challenging vision-language task, utilizing bi-modal (image+text) queries to retrieve target images.
Contactless Fingerprint Recognition Using 3D Graph Matching
Then, a novel 3D graph matching is conducted in 3D space according to the extracted 3D feature.
Hierarchical IoU Tracking based on Interval
Multi-Object Tracking (MOT) aims to detect and associate all targets of given classes across frames.
MindShot: Brain Decoding Framework Using Only One Image
Brain decoding, which aims at reconstructing visual stimuli from brain signals, primarily utilizing functional magnetic resonance imaging (fMRI), has recently made positive progress.
Bring Adaptive Binding Prototypes to Generalized Referring Expression Segmentation
Referring Expression Segmentation (RES) has attracted rising attention, aiming to identify and segment objects based on natural language expressions.
MLS-Track: Multilevel Semantic Interaction in RMOT
The new trend in multi-object tracking task is to track objects of interest using natural language.
PracticalDG: Perturbation Distillation on Vision-Language Models for Hybrid Domain Generalization
Domain Generalization (DG) aims to resolve distribution shifts between source and target domains, and current DG methods are default to the setting that data from source and target domains share identical categories.
Enhancing Functional Safety in Automotive AMS Circuits through Unsupervised Machine Learning
Our approach encompasses a systematic analysis of anomaly abstraction at multiple levels pertaining to the automotive domain, from hardware- to block-level, where anomalies are injected to create diverse fault scenarios.
YYDS: Visible-Infrared Person Re-Identification with Coarse Descriptions
To this end, we present the Refer-VI-ReID settings, which aims to match target visible images from both infrared images and coarse language descriptions (e. g., "a man with red top and black pants") to complement the missing color information.
Instance Paradigm Contrastive Learning for Domain Generalization
In this paper, we propose an instance paradigm contrastive learning framework, introducing contrast between original features and novel paradigms to alleviate domain-specific distractions.
iKUN: Speak to Trackers without Retraining
Referring multi-object tracking (RMOT) aims to track multiple objects based on input textual descriptions.
Video-based Visible-Infrared Person Re-Identification with Auxiliary Samples
Previous methods focus on learning from cross-modality person images in different cameras.
Word4Per: Zero-shot Composed Person Retrieval
Searching for specific person has great social benefits and security value, and it often involves a combination of visual and textual information.
Ranked #1 on
Zero-shot Composed Person Retrieval
on ITCPR dataset
(using extra training data)
Now and Future of Artificial Intelligence-based Signet Ring Cell Diagnosis: A Survey
Since signet ring cells (SRCs) are associated with high peripheral metastasis rate and dismal survival, they play an important role in determining surgical approaches and prognosis, while they are easily missed by even experienced pathologists.
Boundary-Refined Prototype Generation: A General End-to-End Paradigm for Semi-Supervised Semantic Segmentation
In addition, to enhance the classification boundaries, we sample and cluster high- and low-confidence features separately based on confidence estimation, facilitating the generation of prototypes closer to the class boundaries.
Dynamic Clustering and Cluster Contrastive Learning for Unsupervised Person Re-identification
To address this problem, we propose a dynamic clustering and cluster contrastive learning (DCCC) method.
EnsembleMOT: A Step towards Ensemble Learning of Multiple Object Tracking
Multiple Object Tracking (MOT) has rapidly progressed in recent years.
OMG: Observe Multiple Granularities for Natural Language-Based Vehicle Retrieval
For the textual representation, one global embedding, three local embeddings and a color-type prompt embedding are extracted to represent various granularities of semantic features.
StrongSORT: Make DeepSORT Great Again
As a result, the construction of a good baseline for a fair comparison is essential.
Ranked #10 on
Multi-Object Tracking
on MOT20
(using extra training data)
Cross-Platform Modeling of Users' Behavior on Social Media
Structured and unstructured data of same users shared by NetEase Music and Sina Weibo have been collected for cross-platform analysis of correlations between music preference and other users' characteristics.
Automatic Conditional Generation of Personalized Social Media Short Texts
Automatic text generation has received much attention owing to rapid development of deep neural networks.
Scale Aggregation Network for Accurate and Efficient Crowd Counting
In this paper, we propose a novel encoder-decoder network, called extit{Scale Aggregation Network (SANet)}, for accurate and efficient crowd counting.
Ranked #6 on
Crowd Counting
on WorldExpo’10
Accurate and Efficient Similarity Search for Large Scale Face Recognition
The challenge 2 of MS-Celeb-1M is a classification task.
Precise Box Score: Extract More Information from Datasets to Improve the Performance of Face Detection
The proposed training strategy uses the anchors with IoUs between the first and second threshold, which can consistently improve the performance of face detection.
Optimizing Region Selection for Weakly Supervised Object Detection
This region selection procedure is further integrated into a CNN-based weakly supervised detection (WSD) framework, and can be performed in each stochastic gradient descent mini-batch during training.
Contrastive-center loss for deep neural networks
The deep convolutional neural network(CNN) has significantly raised the performance of image classification and face recognition.
Recurrent Convolutional Neural Network Regression for Continuous Pain Intensity Estimation in Video
To overcome this problem, we propose a real-time regression framework based on the recurrent convolutional neural network for automatic frame-level pain intensity estimation.
