TextFM is presented, the first language-guided feature matching framework that incorporates domain-invariant semantic information from vision-language models (VLMs) to establish a strong foundation for robust and generalizable feature matching under real-world constraints.
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
This work introduces \method, a Multi-scale Adaptive Vision Encoder, a Multi-scale Adaptive Vision Encoder that uses position-dependent gates to fuse shallow, intermediate, and deep features from a vision Transformer, preserving global semantics while enhancing edges, text, and local structure.
A novel three-stage framework that integrates self-supervised contrastive pre-training with a dual-branch attention-driven architecture and employs an asymmetric deep hashing layer that promotes both high-level semantic consistency and computational efficiency through binary Hamming distance matching is proposed.
A. Nazari, Kambiz Rahbar, Hamidreza Moghassemi et al.· Signal, Image and Video Proc...· 0 citations
Dense vision-language understanding, including object localization, region recognition, and open-vocabulary semantic segmentation, requires associating language concepts with spatially grounded visual regions. CLIP provides a strong foundation for these tasks by learning a shared image-text embedding space from large-s...
Qwen-3D is introduced, a geometry-aware LMM that compresses visual information within the Qwen backbone using multi-view geometric cues, enabling efficient long-horizon visual reasoning over static scenes and incorporates a query-based segmentation decoder that grounds language directly in the underlying 3D scene repre...
Lucy Lin, Ayush Jain, Yifan Liu et al.· 3 citations
Dynamic Uncertainty with Noisy Correspondences (DUNC), a novel framework that incorporates two key components that incorporates Cross-modal Evidential Learning (CEL), which models bidirectional alignment uncertainty using a Dirichlet distribution to capture the confidence in image–text similarity, and Dynamic Robust Lo...
Ze-Qun Xie, Chu-Xin Wang, Si-Hang Cai et al.· ACM Transactions on Informat...· 0 citations
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