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Learning Transferable Perturbations for Image Captioning

https://doi.org/10.1145/3478024
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1109/cvpr.2015.7299087openalex-snapshot
CIDEr: Consensus-based image description evaluation
inferredcites →https://doi.org/10.1109/cvpr.2017.108openalex-snapshot
StyleNet: Generating Attractive Visual Captions with Styles
inferredcites →https://doi.org/10.1109/cvpr.2017.127openalex-snapshot
Semantic Compositional Networks for Visual Captioning
inferredcites →https://doi.org/10.1109/cvpr.2017.131openalex-snapshot
Self-Critical Sequence Training for Image Captioning
inferredcites →https://doi.org/10.1109/cvpr.2018.00728openalex-snapshot
Discriminability Objective for Training Descriptive Captions
inferredcites →https://doi.org/10.1109/cvpr.2019.00160openalex-snapshot
MirrorGAN: Learning Text-To-Image Generation by Redescription
inferredcites →https://doi.org/10.1109/iccv.2017.153openalex-snapshot
Adversarial Examples for Semantic Segmentation and Object Detection
inferredcites →https://doi.org/10.1109/tnnls.2018.2886017openalex-snapshot
Adversarial Examples: Attacks and Defenses for Deep Learning
inferredcites →https://doi.org/10.1145/3295748openalex-snapshot
A Comprehensive Survey of Deep Learning for Image Captioning
inferredcites →https://doi.org/10.1162/tacl_a_00166openalex-snapshot
From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions
inferredcites →https://doi.org/10.18653/v1/p17-4012openalex-snapshot
OpenNMT: Open-Source Toolkit for Neural Machine Translation
inferredcites →https://doi.org/10.3115/1073083.1073135openalex-snapshot
BLEU
inferredcites →https://doi.org/10.48550/arxiv.1412.6980openalex-snapshot
Adam: A Method for Stochastic Optimization
inferredcites →https://doi.org/10.5555/2566972.2566993openalex-snapshot
Framing image description as a ranking task

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-031-94266-2_6openalex-snapshot
A Survey of Image Captioning Adversarial Attacks
inferred← citeshttps://doi.org/10.1007/s10489-024-05823-1openalex-snapshot
Cross-modality interaction reasoning for enhancing vision-language pre-training in image-text retrieval
inferred← citeshttps://doi.org/10.1016/j.eswa.2024.125592openalex-snapshot
Self-supervised incomplete cross-modal hashing retrieval
inferred← citeshttps://doi.org/10.1016/j.neucom.2023.126287openalex-snapshot
Deep image captioning: A review of methods, trends and future challenges
inferred← citeshttps://doi.org/10.1109/iccvw60793.2023.00396openalex-snapshot
Black-Box Attacks on Image Activity Prediction and its Natural Language Explanations
inferred← citeshttps://doi.org/10.1109/wi-iat62293.2024.00102openalex-snapshot
Enhancing and Accelerating Image-Text Retrieval with Knowledge Graphs and FAISS
inferred← citeshttps://doi.org/10.1145/3576927openalex-snapshot
NumCap: A Number-controlled Multi-caption Image Captioning Network
inferred← citeshttps://doi.org/10.1145/3587097openalex-snapshot
A 2 SC: Adversarial Attacks on Subspace Clustering
inferred← citeshttps://doi.org/10.1145/3587252openalex-snapshot
Video Captioning by Learning from Global Sentence and Looking Ahead
inferred← citeshttps://doi.org/10.1145/3588572openalex-snapshot
Semantic Enhanced Video Captioning with Multi-feature Fusion
inferred← citeshttps://doi.org/10.1145/3604284openalex-snapshot
Zero-shot Scene Graph Generation via Triplet Calibration and Reduction
inferred← citeshttps://doi.org/10.1145/3638558openalex-snapshot
Exploring Visual Relationships via Transformer-based Graphs for Enhanced Image Captioning
inferred← citeshttps://doi.org/10.1177/03611981251356511openalex-snapshot
Intent-Driven Controllable Adversarial Attacks against Multi-Object Trackers for Intelligent Transportation Systems
inferred← citeshttps://doi.org/10.3390/app14083312openalex-snapshot
Segment Shards: Cross-Prompt Adversarial Attacks against the Segment Anything Model
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