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20212025
most citedMemorizing Complementation Network for Few-Shot Class-Incremental Learning

67 citations · 122 across the 9 of their papers we have counts for

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cs.CV2025

Optimal Transport Adapter Tuning for Bridging Modality Gaps in Few-Shot Remote Sensing Scene Classification

Zhong Ji, Ci Liu, Jingren Liu +3

Few-Shot Remote Sensing Scene Classification (FS-RSSC) presents the challenge of classifying remote sensing images with limited labeled samples. Existing methods typically emphasiz…

cs.CV2025

Underlying Semantic Diffusion for Effective and Efficient In-Context Learning

Zhong Ji, Weilong Cao, Yan Zhang +3

Diffusion models has emerged as a powerful framework for tasks like image controllable generation and dense prediction. However, existing models often struggle to capture underlyin…

cs.CV2023★ 15 cited

Hierarchical Matching and Reasoning for Multi-Query Image Retrieval

Zhong Ji, Zhihao Li, Yan Zhang +3

As a promising field, Multi-Query Image Retrieval (MQIR) aims at searching for the semantically relevant image given multiple region-specific text queries. Existing works mainly fo…

cs.CV2023★ 10 cited

Transformer-based stereo-aware 3D object detection from binocular images

Hanqing Sun, Yanwei Pang, Jiale Cao +2

Transformers have shown promising progress in various visual object detection tasks, including monocular 2D/3D detection and surround-view 3D detection. More importantly, the atten…

cs.CV2023★ 28 cited

Deep Intra-Image Contrastive Learning for Weakly Supervised One-Step Person Search

Jiabei Wang, Yanwei Pang, Jiale Cao +3

Weakly supervised person search aims to perform joint pedestrian detection and re-identification (re-id) with only person bounding-box annotations. Recently, the idea of contrastiv…

cs.CV2023★ 1 cited

USER: Unified Semantic Enhancement with Momentum Contrast for Image-Text Retrieval

Yan Zhang, Zhong Ji, Di Wang +2

As a fundamental and challenging task in bridging language and vision domains, Image-Text Retrieval (ITR) aims at searching for the target instances that are semantically relevant…