It is argued, and empirically observe, that value-level alignment saturates in this setting: whether through concept-guided response matching or a learnable projection, the supervision target remains segmentation-shaped, so bridging activations does not resolve the underlying mismatch.
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...
This work injects two complementary semantic priors into Visual prompt tuning, a cascaded scheme that integrates both priors throughout ViT adaptation, and proposes a cascaded scheme that integrates both priors throughout ViT adaptation.
Xi Xiao, Xing-Jian Li, Cheng Han et al.· Trans. Mach. Learn. Res.· 0 citations
The Semantic Localization-Enhanced Teacher (SLE-T), a semantically compatible knowledge-distillation framework built around a lightweight SLE Adapter for DINOv2, achieves state-of-the-art performance and ablation studies confirm the importance of teacher-student semantic compatibility.
Qifeng Zhang, Ting Xiang, Ze-Yu Bai et al.· 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
The Cross-Modal Dual-Path Generator (CM-DPG), a model for robust open-vocabulary SGG that enhances object-level semantic representations through joint visual-textual encoding and improves relational reasoning using complementary visual and geometric cues, is proposed.
Ming-Hao Zou, Qingtian Zeng, Shangkun Liu et al.· 0 citations
Fine-tuning pre-trained DNNs with Relational Knowledge Distillation, an established relational transfer method, brings DNNs close enough to humans to be aligned at the individual-object level on this test set, and indicates that relational transfer from humans brings the global structure of pre-trained DNNs close enoug...
Yuria Shimizu, Soh Takahashi, Takato Horii et al.· 0 citations
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