This paper introduces two core modules designed to strengthen the feature representation and the semantic association between text and video, respectively and introduces semantic reconstruction feedback to serve as an auxiliary supervision alongside the primary global-local text-video alignment task.
A new prototype-based hierarchical alignment network (PHA-Net) to align individual/local/global level representations across modalities and introduces multiple modality-shared prototypes as the bridge to efficiently optimize text and video representations for cross-modal alignment.
Xiaolun Jing, Kezhao Yin, Xin-Xing Yang et al.· Neurocomputing· 0 citations
This paper studies the problem of text-video retrieval, where the goal is to learn accurate cross-modal alignment between videos and text. This problem is challenging because of the matching ambiguity caused by the inherent gap between the heterogeneous video and text modalities. In particular, the differences in the i...
Dong-Lin Zhang, Zheng-Hao Rao, Xing Xu et al.· IEEE Transactions on Image P...· 0 citations
Experiments on four text-video retrieval benchmarks show that MARS achieves state-of-the-art results in both direct similarity-based retrieval and reranking settings, and a hard-negative-aware slot specialization objective that encourages the slots to capture discriminative matching cues is introduced.
Uicheol Jung, Juyoung Hong, Geuntaek Lim et al.· 0 citations
Experimental results on two publicly available datasets demonstrate that the pro-posed DASE model achieves significant performance improvements in image-text matching tasks compared to baseline methods, validating its effectiveness and superiority.
Xinlin Zhao· Poster Volume 0008 The 2026...· 0 citations
Results show that adaptively orchestrating foundation-model capabilities can combine scalable retrieval with fine-grained reasoning without task-specific training, and are shown to be state-of-the-art among training-free approaches.
Dmitry Demidov, Muhammad Zaigham Zaheer, Omkar Thawakar et al.· 0 citations
This work proposes Semantic-Conditioned Temporal Structure Distillation, SCTSD, which preserves the standard dual-encoder retrieval pipeline while learning temporal structural information induced by text conditions during training.
Wei-Jie Luo, Xiao-Hang Guan, Xi-Chang Cai et al.· Multimedia Systems· 0 citations
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