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- ActivityNet-QA: A Dataset for Understanding Complex Web Videos via Question Answering793
This work introduces ActivityNet-QA, a fully annotated and large scale VideoQA dataset, which consists of 58,000 QA pairs on 5,800 complex web videos derived from the popular ActivityNet dataset and explores various video representation strategies to improve videoQA performance.
- Multi-modal Factorized Bilinear Pooling with Co-attention Learning for Visual Question Answering757
A Multi-modal Factorized Bilinear (MFB) pooling approach to efficiently and effectively combine multi- modal features, which results in superior performance for VQA compared with other bilinear pooling approaches.
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- Beyond Bilinear: Generalized Multimodal Factorized High-Order Pooling for Visual Question Answering557
A DNN architecture is designed to integrate all these aforementioned modules into a unified model for achieving superior VQA performance and an ensemble of the authors' MFH models achieve the state-of-the-art performance on the large-scale VQ a data sets and win the runner-up in V QA Challenge 2017.
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- Multimodal Transformer With Multi-View Visual Representation for Image Captioning466
Inspired by the success of the Transformer model in machine translation, this work extends it to a Multimodal Transformer (MT) model for image captioning that significantly outperforms the previous state-of-the-art methods.
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- A comprehensive survey on pretrained foundation models: a history from BERT to ChatGPT411
This survey aims to shed light on the research of the PFMs on scalability, security, logical reasoning ability, cross-domain learning ability, cross-domain learning ability, and user-friendly interactive ability for artificial general intelligence.
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- VisDrone-DET2020: The Vision Meets Drone Object Detection in Image Challenge Results263
Based on the selected 29 robust detection methods, the experimental results comprehensively are discussed, which shows the effectiveness of ensemble learning and data augmentation in drone captured object detection.
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- Local Deep-Feature Alignment for Unsupervised Dimension Reduction214
Experimental results on data visualization, clustering, and classification show that the LDFA method is competitive with several well-known dimension reduction techniques, and exploiting locality in deep learning is a research topic worth further exploring.
- Prompting Large Language Models with Answer Heuristics for Knowledge-Based Visual Question Answering211
- Deep Graph-neighbor Coherence Preserving Network for Unsupervised Cross-modal Hashing207
A deep graph-neighbor coherence preserving network (DGCPN), which addresses the inaccurate similarity problem by exploring and exploiting the data's intrinsic relationships in a graph and regulates comprehensive similarity preserving losses.
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- DeepSim: Deep similarity for image quality assessment176
Thorough experiments conducted on standard databases show that the proposed novel full-reference IQA framework, codenamed DeepSim, can accurately predict human perceived image quality and outperforms previous state-of-the-art performance.
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- Rethinking Diversified and Discriminative Proposal Generation for Visual Grounding150
This paper introduces the diversity and discrimination simultaneously when generating proposals, and in doing so proposes Diversified and Discriminative Proposal Networks model (DDPN), a high performance baseline model for visual grounding and evaluates it on four benchmark datasets.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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