A new cross-cultural benchmark shows vision-language models infer structured cultural metadata from images inconsistently, with fragmented signals and large performance gaps across regions and metadata types.
ViLBERT : Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks
7 Pith papers cite this work, alongside 1,675 external citations. Polarity classification is still indexing.
7
Pith papers citing it
1,675
external citations · external index
representative citing papers
Gated Multi-modal Fusion reaches 0.82 macro F1 on HARMES, beating the concatenation baseline of 0.76 by 6 points under leave-one-participant-out evaluation.
KG-TRACE fuses genomic features with RotatE KG embeddings via an epistemic trust gate for AMR prediction, reporting 0.976 AUROC on isoniazid resistance in the CRyPTIC cohort plus 92.5% symbolic coverage via a new Biological Grounding Ratio metric.
Contrastive pre-training on unsupervised data at scale creates text and code embeddings that set new state-of-the-art results on classification and semantic search benchmarks.
VisualBERT is a Transformer model that implicitly aligns text and image regions through self-attention and achieves competitive or superior results on VQA, VCR, NLVR2, and Flickr30K after pre-training on captions.
The work establishes an evaluation framework for personality induction and switching in MLLMs, reporting improved captioning but impaired VQA performance plus balancing and residual effects during multi-trait and dynamic conditions.
Introduces GRIT, LTMI, and a hierarchical attention framework claiming performance gains on image captioning, visual dialog, and ALFRED instruction following.
citing papers explorer
-
Appear2Meaning: A Cross-Cultural Benchmark for Structured Cultural Metadata Inference from Images
A new cross-cultural benchmark shows vision-language models infer structured cultural metadata from images inconsistently, with fragmented signals and large performance gaps across regions and metadata types.
-
A Comparison of Fusion Techniques for Multi-Modal Human Activity Recognition on the HARMES Dataset
Gated Multi-modal Fusion reaches 0.82 macro F1 on HARMES, beating the concatenation baseline of 0.76 by 6 points under leave-one-participant-out evaluation.
-
KG-TRACE: A Neuro-Symbolic Framework for Mechanistic Grounding in Antimicrobial Resistance Prediction
KG-TRACE fuses genomic features with RotatE KG embeddings via an epistemic trust gate for AMR prediction, reporting 0.976 AUROC on isoniazid resistance in the CRyPTIC cohort plus 92.5% symbolic coverage via a new Biological Grounding Ratio metric.
-
Text and Code Embeddings by Contrastive Pre-Training
Contrastive pre-training on unsupervised data at scale creates text and code embeddings that set new state-of-the-art results on classification and semantic search benchmarks.
-
VisualBERT: A Simple and Performant Baseline for Vision and Language
VisualBERT is a Transformer model that implicitly aligns text and image regions through self-attention and achieves competitive or superior results on VQA, VCR, NLVR2, and Flickr30K after pre-training on captions.
-
Modeling Complex Behaviors: Multi-Personality Composition and Dynamic Switching in Vision-Language Models
The work establishes an evaluation framework for personality induction and switching in MLLMs, reporting improved captioning but impaired VQA performance plus balancing and residual effects during multi-trait and dynamic conditions.
-
Machine Intelligence that Understands Visual and Linguistic Information and Interacts with Humans and Environments
Introduces GRIT, LTMI, and a hierarchical attention framework claiming performance gains on image captioning, visual dialog, and ALFRED instruction following.