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Found 8 papers, 2 papers with code
Few-Shot Semantic Segmentation with Democratic Attention Networks
The crux of few-shot segmentation is to extract object information from the support image and then propagate it to guide the segmentation of query images.
OmniMedVQA: A New Large-Scale Comprehensive Evaluation Benchmark for Medical LVLM
A significant challenge arises from the scarcity of diverse medical images spanning various modalities and anatomical regions, which is essential in real-world medical applications.
Beyond One-to-One: Rethinking the Referring Image Segmentation
In this paper, we address this issue from two perspectives.
Tiny LVLM-eHub: Early Multimodal Experiments with Bard
Secondly, it conducts an in-depth analysis of LVLMs' predictions using the ChatGPT Ensemble Evaluation (CEE), which leads to a robust and accurate evaluation and exhibits improved alignment with human evaluation compared to the word matching approach.
Double Graphs Regularized Multi-view Subspace Clustering
In this paper, we propose a novel Double Graphs Regularized Multi-view Subspace Clustering (DGRMSC) method, which aims to harness both global and local structural information of multi-view data in a unified framework.
Global Weighted Tensor Nuclear Norm for Tensor Robust Principal Component Analysis
Tensor Robust Principal Component Analysis (TRPCA), which aims to recover a low-rank tensor corrupted by sparse noise, has attracted much attention in many real applications.
Alignment Enhancement Network for Fine-grained Visual Categorization
However, they are still inefficient to fully use the cross-layer information based on the simple aggregation strategy, while existing pairwise learning methods also fail to explore long-range interactions between different images.
Ranked #8 on
Fine-Grained Image Classification
on FGVC Aircraft
Fine-Grained Image Classification
Fine-Grained Visual Categorization
NAS-Count: Counting-by-Density with Neural Architecture Search
Most of the recent advances in crowd counting have evolved from hand-designed density estimation networks, where multi-scale features are leveraged to address the scale variation problem, but at the expense of demanding design efforts.
