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Language-guided Residual Graph Attention Network and Data Augmentation for Visual Grounding
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-319-46448-0_49openalex-snapshot
Grounding of Textual Phrases in Images by Reconstruction
inferredcites →https://doi.org/10.1007/s11263-016-0981-7openalex-snapshot
Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations
inferredcites →https://doi.org/10.1016/j.neucom.2022.07.028openalex-snapshot
CLIP4Clip: An empirical study of CLIP for end to end video clip retrieval and captioning
inferredcites →https://doi.org/10.1016/j.patrec.2021.01.011openalex-snapshot
ROSNet: Robust one-stage network for CT lesion detection
inferredcites →https://doi.org/10.1016/j.patrec.2022.05.024openalex-snapshot
Referring expression grounding by multi-context reasoning
inferredcites →https://doi.org/10.1103/physreve.51.4282openalex-snapshot
Social force model for pedestrian dynamics
inferredcites →https://doi.org/10.1109/78.650093openalex-snapshot
Bidirectional recurrent neural networks
inferredcites →https://doi.org/10.1109/acpr.2015.7486599openalex-snapshot
Very deep convolutional neural network based image classification using small training sample size
inferredcites →https://doi.org/10.1109/cvpr.1993openalex-snapshot
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
inferredcites →https://doi.org/10.1109/cvpr.2018.00636openalex-snapshot
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
inferredcites →https://doi.org/10.1109/cvpr.2019.00206openalex-snapshot
Neighbourhood Watch: Referring Expression Comprehension via Language-Guided Graph Attention Networks
inferredcites →https://doi.org/10.1109/iccv.1995.466933openalex-snapshot
Proceedings of IEEE International Conference on Computer Vision
inferredcites →https://doi.org/10.1109/iccv.2019.00474openalex-snapshot
Dynamic Graph Attention for Referring Expression Comprehension
inferredcites →https://doi.org/10.1109/icip46576.2022.9897564openalex-snapshot
Residual Graph Attention Network and Expression-Respect Data Augmentation Aided Visual Grounding
inferredcites →https://doi.org/10.1109/iros.1994openalex-snapshot
Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'94)
inferredcites →https://doi.org/10.1109/mce.2022.3181759openalex-snapshot
Lightweight Deep Learning: An Overview
inferredcites →https://doi.org/10.1109/mdm48529.2020.00044openalex-snapshot
RESGCN: RESidual Graph Convolutional Network based Free Dock Prediction in Bike Sharing System
inferredcites →https://doi.org/10.1109/tip.2014.2298982openalex-snapshot
Learning and Recognition of On-Premise Signs From Weakly Labeled Street View Images
inferredcites →https://doi.org/10.1109/tmm.2021.3050059openalex-snapshot
Spatiotemporal Dilated Convolution With Uncertain Matching for Video-Based Crowd Estimation
inferredcites →https://doi.org/10.1109/tmm.2022.3197365openalex-snapshot
Facial Chirality: From Visual Self-Reflection to Robust Facial Feature Learning
inferredcites →https://doi.org/10.1109/tpami.2016.2577031openalex-snapshot
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
inferredcites →https://doi.org/10.1109/tpami.2020.2973983openalex-snapshot
Relationship-Embedded Representation Learning for Grounding Referring Expressions
inferredcites →https://doi.org/10.1145/3316767openalex-snapshot
Multi-source Multi-level Attention Networks for Visual Question Answering
inferredcites →https://doi.org/10.1145/3359753openalex-snapshot
A Hierarchical CNN-RNN Approach for Visual Emotion Classification
inferredcites →https://doi.org/10.1145/3386725openalex-snapshot
Constrained LSTM and Residual Attention for Image Captioning
inferredcites →https://doi.org/10.1145/3394171.3414012openalex-snapshot
AU-assisted Graph Attention Convolutional Network for Micro-Expression Recognition
inferredcites →https://doi.org/10.1145/3441628openalex-snapshot
Part-wise Spatio-temporal Attention Driven CNN-based 3D Human Action Recognition
inferredcites →https://doi.org/10.1145/3451215openalex-snapshot
Knowledge-aware Multi-modal Adaptive Graph Convolutional Networks for Fake News Detection
inferredcites →https://doi.org/10.1145/3451390openalex-snapshot
Fine-Grained Visual Textual Alignment for Cross-Modal Retrieval Using Transformer Encoders
inferredcites →https://doi.org/10.1145/3474085.3475251openalex-snapshot
ROSITA: Enhancing Vision-and-Language Semantic Alignments via Cross- and Intra-modal Knowledge Integration
inferredcites →https://doi.org/10.1145/3489142openalex-snapshot
Inner Knowledge-based Img2Doc Scheme for Visual Question Answering
inferredcites →https://doi.org/10.1145/3587251openalex-snapshot
Transformer-Based Visual Grounding with Cross-Modality Interaction
inferredcites →https://doi.org/10.1186/s40649-019-0069-yopenalex-snapshot
Graph convolutional networks: a comprehensive review
inferredcites →https://doi.org/10.1609/aaai.v28i1.8946openalex-snapshot
Extracting Keyphrases from Research Papers Using Citation Networks
inferredcites →https://doi.org/10.18653/v1/d16-1openalex-snapshot
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing
inferredcites →https://doi.org/10.18653/v1/n16-1openalex-snapshot
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
inferredcites →https://doi.org/10.18653/v1/n19-1423openalex-snapshot
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
inferredcites →https://doi.org/10.18653/v1/p19-1openalex-snapshot
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
inferredcites →https://doi.org/10.4000/ijcol.478openalex-snapshot
Large scale datasets for Image and Video Captioning in Italian
inferredcites →https://doi.org/10.4230/lipics.itp.2023.19openalex-snapshot
Exploiting Generative AI to Scale up Intelligent Tutoring Systems
inferredcites →https://doi.org/10.48550/arxiv.2006.06195openalex-snapshot
Large-Scale Adversarial Training for Vision-and-Language Representation Learning
inferredcites →https://doi.org/10.48550/arxiv.2107.06383openalex-snapshot
How Much Can CLIP Benefit Vision-and-Language Tasks?
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1016/j.eswa.2025.128857openalex-snapshot
Multimodal context-aware consistency alignment for vision-language tasks
inferred← citeshttps://doi.org/10.1016/j.eswa.2026.132501openalex-snapshot
SUAD: semantic understanding and attribute discrimination for visual grounding
inferred← citeshttps://doi.org/10.1016/j.patcog.2024.111222openalex-snapshot
Graph-based referring expression comprehension with expression-guided selective filtering and noun-oriented reasoning
inferred← citeshttps://doi.org/10.1109/cvpr52733.2024.02309openalex-snapshot
Distraction is All You Need: Memory-Efficient Image Immunization against Diffusion-Based Image Editing
inferred← citeshttps://doi.org/10.1109/mce.2023.3343919openalex-snapshot
Human–Object Interaction Detection: An Overview
inferred← citeshttps://doi.org/10.1109/tcsvt.2024.3407785openalex-snapshot
Visual Grounding With Dual Knowledge Distillation
inferred← citeshttps://doi.org/10.1109/tcsvt.2025.3528657openalex-snapshot
Graph-Based Group Division Network for Referring Expression Comprehension
inferred← citeshttps://doi.org/10.1109/tip.2025.3620129openalex-snapshot
Multi-Perspective Cross-Modal Object Encoding for Referring Expression Comprehension
inferred← citeshttps://doi.org/10.1109/tmm.2024.3521844openalex-snapshot
Make Graph-Based Referring Expression Comprehension Great Again Through Expression-Guided Dynamic Gating and Regression
inferred← citeshttps://doi.org/10.1109/tpami.2026.3673741openalex-snapshot
Confidence-Aware Pseudo-Label Self-Correction for Weakly Supervised Visual Grounding
inferred← citeshttps://doi.org/10.1145/3664647.3681660openalex-snapshot
ResVG: Enhancing Relation and Semantic Understanding in Multiple Instances for Visual Grounding
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