Aug 2026· The Visual Computer· Vol 42· 0 citations· 52 references
TL;DR
A Low-Complexity Cross-Modal Alignment via Projection (LCAP) network is proposed, which introduces Projective Token Compression (PTC), which leverages Mish activation and adaptive average pooling to reduce feature redundancy while enhancing discriminative information, and Positional Spatial Enhancement (PSE), which explicitly injects positional cues into the compressed representations and strengthens spatial structure.
Multimodal deep learning integrates heterogeneous data sources such as images and text to enable machines to understand complex real-world contexts. Although recent vision-language models have achieved significant progress, most existing approaches rely on rigid fusion strategies that combine modalities either at early...
Unnati A. Patel, Sanskruti Patel, J. Nanavati et al.· International Conference on...· 0 citations
The findings indicate that text-directed visual filtering and an explicit division between reusable and task-specific information are both consequential for grounded summary generation.
Zhiang Chen· AI and Data Science Journal· 0 citations
This study proposes a dual-stream architecture that exploits complementary Transformer-based and convolutional visual representations for Vietnamese VQA, and shows improvements over the corresponding single-stream convolutional baselines, supporting the complementary role of the two visual representations.
Huy Tran, V. Nguyen· Asian Journal of Research in...· 0 citations
The Robust and Fine-grained training framework for CLIP-based vision-language models (RoFLIP) is proposed, enhancing both the robustness and granularity of vision-language alignment and underscore RoFLIP’s compositional reasoning and generalization abilities.
Yiwei Sun, Chuan-Bin Liu, Shan-Cheng Fang et al.· International Journal of Com...· 0 citations
ReVA is proposed, a region-aware VQA model that employs a frozen CLIP ViT-L/14 Vision Transformer (ViT) and a Qwen2.5-7B-Instruct large language model (LLM) connected through a dual bridge that aligns both whole-image and region-level representations with the LLM's embedding space.
The AP-LCA approach introduces a novel local cross-modal alignment strategy that utilizes image cropping and similarity-based semantic contribution assessment to precisely map fine-grained descriptions to relevant local image regions.
Si-Ying Wu, Song Wu· International Conference on...· 0 citations
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