Search Results for author: Li Mi

Found 6 papers, 0 papers with code

ConGeo: Robust Cross-view Geo-localization across Ground View Variations

no code implementations20 Mar 2024 Li Mi, Chang Xu, Javiera Castillo-Navarro, Syrielle Montariol, Wen Yang, Antoine Bosselut, Devis Tuia

Cross-view geo-localization aims at localizing a ground-level query image by matching it to its corresponding geo-referenced aerial view.

ConVQG: Contrastive Visual Question Generation with Multimodal Guidance

no code implementations20 Feb 2024 Li Mi, Syrielle Montariol, Javiera Castillo-Navarro, Xianjie Dai, Antoine Bosselut, Devis Tuia

However, generating focused questions using textual constraints while enforcing a high relevance to the image content remains a challenge, as VQG systems often ignore one or both forms of grounding.

Question Generation Question-Generation

Object-Relation Reasoning Graph for Action Recognition

no code implementations CVPR 2022 Yangjun Ou, Li Mi, Zhenzhong Chen

By combining an object-level graph (OG) and a relation-level graph (RG), the proposed OR2G catches the attribute transitions of objects and reasons about the relationship transitions between objects simultaneously.

Action Recognition Attribute +3

Predicate correlation learning for scene graph generation

no code implementations6 Jul 2021 Leitian Tao, Li Mi, Nannan Li, Xianhang Cheng, Yaosi Hu, Zhenzhong Chen

For a typical Scene Graph Generation (SGG) method, there is often a large gap in the performance of the predicates' head classes and tail classes.

Graph Generation Scene Graph Generation

Visual Relationship Forecasting in Videos

no code implementations2 Jul 2021 Li Mi, Yangjun Ou, Zhenzhong Chen

To evaluate the VRF task, we introduce two video datasets named VRF-AG and VRF-VidOR, with a series of spatio-temporally localized visual relation annotations in a video.

Decision Making Object

Hierarchical Graph Attention Network for Visual Relationship Detection

no code implementations CVPR 2020 Li Mi, Zhenzhong Chen

Object-level graph aims to capture the interactions between objects, while the triplet-level graph models the dependencies among relation triplets.

Feature Correlation Graph Attention +3

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