REVIEW 5 cited by
Supervised Multimodal Bitransformers for Classifying Images and Text
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Supervised Multimodal Bitransformers for Classifying Images and Text
read the original abstract
Self-supervised bidirectional transformer models such as BERT have led to dramatic improvements in a wide variety of textual classification tasks. The modern digital world is increasingly multimodal, however, and textual information is often accompanied by other modalities such as images. We introduce a supervised multimodal bitransformer model that fuses information from text and image encoders, and obtain state-of-the-art performance on various multimodal classification benchmark tasks, outperforming strong baselines, including on hard test sets specifically designed to measure multimodal performance.
Forward citations
Cited by 5 Pith papers
-
HuggingFace's Transformers: State-of-the-art Natural Language Processing
Hugging Face releases an open-source Python library that supplies a unified API and pretrained weights for major Transformer architectures used in natural language processing.
-
EVL-MCoT: Enhanced Vision-Language Multi-CoT for Harmful Meme Detection
EVL-MCoT combines multiple hateful and benign chain-of-thought explanations with prototype-guided vision-language fusion, reporting state-of-the-art harmful meme detection accuracy on HatefulMemes and MultiOFF.
-
LongMoE: Longitudinal Multimodal Learning via Trajectory-Aware Mixture-of-Experts
LongMoE is a multimodal framework combining context-aware imputation, frequency-domain attentional tokenization, trajectory encoding, and context-conditioned sparse MoE routing to jointly handle modality missingness a...
-
Fall into a Pit, Gain in a Wit: Cognitive-Guided Harmful Meme Detection via Misjudgment Risk Pattern Retrieval
PatMD improves harmful meme detection by retrieving misjudgment risk patterns to guide MLLMs, reporting 8.30% average F1 and 7.71% accuracy gains on 6,626 memes across 5 tasks.
-
Connecting online criminal behavior with machine learning: Using authorship attribution to analyze and link potential online traffickers
Machine learning can link potential online traffickers by identifying persistent writing and image patterns in anonymous advertisements despite identity changes.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.