5 papers
Selectivity Drives Efficiency: Dataset Pruning for Visual Place Recognition
Tong Jin, Yunpeng Liu, Shuyu Hu +3
The paper introduces a place-wise dataset pruning method for visual place recognition that selects informative locations using intra-place diversity and inter-place similarity metr…
Where, What, Why, and Importance: Structured Defect Grounding for Text-to-Image Feedback
Huaisong Zhang, Hao Yu, Yuxuan Zhang +7
Despite generating increasingly photorealistic images, text-to-image (T2I) models still exhibit localized, subtle, and structurally complex failures. Diagnosing these failures requ…
Towards Implicit Aggregation: Robust Image Representation for Place Recognition in the Transformer Era
Feng Lu, Tong Jin, Canming Ye +3
Visual place recognition (VPR) is typically regarded as a specific image retrieval task, whose core lies in representing images as global descriptors. Over the past decade, dominan…
SelaVPR++: Towards Seamless Adaptation of Foundation Models for Efficient Place Recognition
Feng Lu, Tong Jin, Xiangyuan Lan +4
Recent studies show that the visual place recognition (VPR) method using pre-trained visual foundation models can achieve promising performance. In our previous work, we propose a…
EDTformer: An Efficient Decoder Transformer for Visual Place Recognition
Tong Jin, Feng Lu, Shuyu Hu +2
Visual place recognition (VPR) aims to determine the general geographical location of a query image by retrieving visually similar images from a large geo-tagged database. To obtai…