Search Results for author:
Found 5 papers, 2 papers with code
Dataset Bias Mitigation in Multiple-Choice Visual Question Answering and Beyond
Vision-language (VL) understanding tasks evaluate models' comprehension of complex visual scenes through multiple-choice questions.
Understanding ME? Multimodal Evaluation for Fine-grained Visual Commonsense
Visual commonsense understanding requires Vision Language (VL) models to not only understand image and text but also cross-reference in-between to fully integrate and achieve comprehension of the visual scene described.
Query Adaptive Few-Shot Object Detection with Heterogeneous Graph Convolutional Networks
Few-shot object detection (FSOD) aims to detect never-seen objects using few examples.
Meta Faster R-CNN: Towards Accurate Few-Shot Object Detection with Attentive Feature Alignment
To improve the fine-grained few-shot proposal classification, we propose a novel attentive feature alignment method to address the spatial misalignment between the noisy proposals and few-shot classes, thus improving the performance of few-shot object detection.
Drug Selection via Joint Push and Learning to Rank
We have developed a new learning-to-rank method, denoted as pLETORg , that predicts drug ranking structures in each cell line via using drug latent vectors and cell line latent vectors.
