16 citations · 27 across the 7 of their papers we have counts for
6 papers · 1 filter
LANDMARK: Language-guided Representation Enhancement Framework for Scene Graph Generation
Xiaoguang Chang, Teng Wang, Shaowei Cai +1
Scene graph generation (SGG) is a sophisticated task that suffers from both complex visual features and dataset long-tail problem. Recently, various unbiased strategies have been p…
Transformer-Guided Convolutional Neural Network for Cross-View Geolocalization
Teng Wang, Shujuan Fan, Daikun Liu +1
Ground-to-aerial geolocalization refers to localizing a ground-level query image by matching it to a reference database of geo-tagged aerial imagery. This is very challenging due t…
Biasing Like Human: A Cognitive Bias Framework for Scene Graph Generation
Xiaoguang Chang, Teng Wang, Changyin Sun +1
Scene graph generation is a sophisticated task because there is no specific recognition pattern (e.g., "looking at" and "near" have no conspicuous difference concerning vision, whe…
Discriminative multi-view Privileged Information learning for image re-ranking
Jun Li, Chang Xu, Wankou Yang +3
Conventional multi-view re-ranking methods usually perform asymmetrical matching between the region of interest (ROI) in the query image and the whole target image for similarity c…
Inverse Visual Question Answering: A New Benchmark and VQA Diagnosis Tool
Feng Liu, Tao Xiang, Timothy M. Hospedales +2
In recent years, visual question answering (VQA) has become topical. The premise of VQA's significance as a benchmark in AI, is that both the image and textual question need to be…
Crowd Counting via Weighted VLAD on Dense Attribute Feature Maps
Biyun Sheng, Chunhua Shen, Guosheng Lin +3
Crowd counting is an important task in computer vision, which has many applications in video surveillance. Although the regression-based framework has achieved great improvements f…