Deep Pre-Alignment uses a small VLM perceiver instead of ViT to pre-align visual features with LLM text space, yielding 1.9-3.0 point gains on multimodal benchmarks and 32.9% less language forgetting.
by Andrea Vedaldi et al
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Hybrid knowledge graph embeddings fused with vision transformer features outperform standard techniques on abstract concept classification by integrating situated perceptual knowledge from a new cultural image resource.
A literature survey on abstract concept recognition in videos that catalogs prior tasks and datasets while advocating for foundation models and reuse of decades of community experience.
citing papers explorer
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Deep Pre-Alignment for VLMs
Deep Pre-Alignment uses a small VLM perceiver instead of ViT to pre-align visual features with LLM text space, yielding 1.9-3.0 point gains on multimodal benchmarks and 32.9% less language forgetting.
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Stitching Gaps: Fusing Situated Perceptual Knowledge with Vision Transformers for High-Level Image Classification
Hybrid knowledge graph embeddings fused with vision transformer features outperform standard techniques on abstract concept classification by integrating situated perceptual knowledge from a new cultural image resource.
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Looking Beyond the Obvious: A Survey on Abstract Concept Recognition for Video Understanding
A literature survey on abstract concept recognition in videos that catalogs prior tasks and datasets while advocating for foundation models and reuse of decades of community experience.