1 citations · 1 across the 3 of their papers we have counts for
4 papers
Attention-disentangled Uniform Orthogonal Feature Space Optimization for Few-shot Object Detection
Taijin Zhao, Heqian Qiu, Yu Dai +4
Few-shot object detection (FSOD) aims to detect objects with limited samples for novel classes, while relying on abundant data for base classes. Existing FSOD approaches, predomina…
MCF-VC: Mitigate Catastrophic Forgetting in Class-Incremental Learning for Multimodal Video Captioning
Huiyu Xiong, Lanxiao Wang, Heqian Qiu +3
To address the problem of catastrophic forgetting due to the invisibility of old categories in sequential input, existing work based on relatively simple categorization tasks has m…
GRSDet: Learning to Generate Local Reverse Samples for Few-shot Object Detection
Hefei Mei, Taijin Zhao, Shiyuan Tang +5
Few-shot object detection (FSOD) aims to achieve object detection only using a few novel class training data. Most of the existing methods usually adopt a transfer-learning strateg…
RefCrowd: Grounding the Target in Crowd with Referring Expressions
Heqian Qiu, Hongliang Li, Taijin Zhao +3
Crowd understanding has aroused the widespread interest in vision domain due to its important practical significance. Unfortunately, there is no effort to explore crowd understandi…