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Found 17 papers, 9 papers with code
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.
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.
Structural Information Guided Multimodal Pre-training for Vehicle-centric Perception
To address this issue, we propose a novel vehicle-centric pre-training framework called VehicleMAE, which incorporates the structural information including the spatial structure from vehicle profile information and the semantic structure from informative high-level natural language descriptions for effective masked vehicle appearance reconstruction.
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.
Physics Inspired Hybrid Attention for SAR Target Recognition
There has been a recent emphasis on integrating physical models and deep neural networks (DNNs) for SAR target recognition, to improve performance and achieve a higher level of physical interpretability.
Boundary-Refined Prototype Generation: A General End-to-End Paradigm for Semi-Supervised Semantic Segmentation
Prototype-based classification is a classical method in machine learning, and recently it has achieved remarkable success in semi-supervised semantic segmentation.
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.
Causality-based Dual-Contrastive Learning Framework for Domain Generalization
In this paper, we propose a Dual-Contrastive Learning (DCL) module on feature and prototype contrast.
Bag of Tricks for Out-of-Distribution Generalization
Recently, out-of-distribution (OOD) generalization has attracted attention to the robustness and generalization ability of deep learning based models, and accordingly, many strategies have been made to address different aspects related to this issue.
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 #7 on
Multi-Object Tracking
on MOT17
(using extra training data)
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.
