44 citations · 50 across the 10 of their papers we have counts for
10 papers
Attention-driven GUI Grounding: Leveraging Pretrained Multimodal Large Language Models without Fine-Tuning
Hai-Ming Xu, Qi Chen, Lei Wang +1
Recent advancements in Multimodal Large Language Models (MLLMs) have generated significant interest in their ability to autonomously interact with and interpret Graphical User Inte…
Enhancing Fine-Grained Visual Recognition in the Low-Data Regime Through Feature Magnitude Regularization
Avraham Chapman, Haiming Xu, Lingqiao Liu
Training a fine-grained image recognition model with limited data presents a significant challenge, as the subtle differences between categories may not be easily discernible amids…
MuseBarControl: Enhancing Fine-Grained Control in Symbolic Music Generation through Pre-Training and Counterfactual Loss
Yangyang Shu, Haiming Xu, Ziqin Zhou +2
Automatically generating symbolic music-music scores tailored to specific human needs-can be highly beneficial for musicians and enthusiasts. Recent studies have shown promising re…
A Simple-but-effective Baseline for Training-free Class-Agnostic Counting
Yuhao Lin, Haiming Xu, Lingqiao Liu +1
Class-Agnostic Counting (CAC) seeks to accurately count objects in a given image with only a few reference examples. While previous methods achieving this relied on additional trai…
Progressive Feature Adjustment for Semi-supervised Learning from Pretrained Models
Hai-Ming Xu, Lingqiao Liu, Hao Chen +2
As an effective way to alleviate the burden of data annotation, semi-supervised learning (SSL) provides an attractive solution due to its ability to leverage both labeled and unlab…
Revisiting Image Reconstruction for Semi-supervised Semantic Segmentation
Yuhao Lin, Haiming Xu, Lingqiao Liu +2
Autoencoding, which aims to reconstruct the input images through a bottleneck latent representation, is one of the classic feature representation learning strategies. It has been s…
Semi-supervised Semantic Segmentation with Prototype-based Consistency Regularization
Hai-Ming Xu, Lingqiao Liu, Qiuchen Bian +1
Semi-supervised semantic segmentation requires the model to effectively propagate the label information from limited annotated images to unlabeled ones. A challenge for such a per-…
Improving Fine-Grained Visual Recognition in Low Data Regimes via Self-Boosting Attention Mechanism
Yangyang Shu, Baosheng Yu, Haiming Xu +1
The challenge of fine-grained visual recognition often lies in discovering the key discriminative regions. While such regions can be automatically identified from a large-scale lab…
Progressive Class Semantic Matching for Semi-supervised Text Classification
Hai-Ming Xu, Lingqiao Liu, Ehsan Abbasnejad
Semi-supervised learning is a promising way to reduce the annotation cost for text-classification. Combining with pre-trained language models (PLMs), e.g., BERT, recent semi-superv…
Semi-supervised Learning via Conditional Rotation Angle Estimation
Hai-Ming Xu, Lingqiao Liu, Dong Gong
Self-supervised learning (SlfSL), aiming at learning feature representations through ingeniously designed pretext tasks without human annotation, has achieved compelling progress i…