← 研踪 PaperTrailCVF · CVPR 2026:cvf:dd02bc96b91287327f17e5212619763c · 记录 #1
Reconstructing CLIP for Open-Vocabulary Dense Perception
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-01 23:53
摘要
Large-scale vision-language models (VLMs) such as CLIP have excelled in zero-shot image classification, yet they struggle to achieve the dense cross-modal alignment required by open-vocabulary dense perception (OVDP). While recent self-distillation methods address this by aligning dense features with the generalizable global semantics, a key question remains: how should such dense features be constructed to achieve optimal alignment? To address this, we propose DenseRC, a principled Dense Representations Construction framework that reconstructs CLIP for OVDP based on two key insights.First, by analyzing the internal semantics encoded in the global cls token, we identify that multi-layer value embeddings serve as an informative basis for dense features. Second, we reveal that spatial aggregation tends to amplify semantic misalignment. Motivated by this, we design a lightweight Head-Selective Gating (HSG) module that adaptively reweights feature heads according to their intrinsic heterogeneity, enabling discriminative and alignment-friendly dense representations construction. Extensive experiments demonstrate that DenseRC delivers consistent and substantial gains across OVDP tasks including object detection and semantic segmentation, setting new state-of-the-art performance on multiple benchmarks.
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记录 #1 · 2026 · 最新观测版本
Reconstructing CLIP for Open-Vocabulary Dense Perception
Large-scale vision-language models (VLMs) such as CLIP have excelled in zero-shot image classification, yet they struggle to achieve the dense cross-modal alignment required by open-vocabulary dense perception (OVDP). While recent self-distillation methods address this by aligning dense features with the generalizable global semantics, a key question remains: how should such dense features be constructed to achieve optimal alignment? To address this, we propose DenseRC, a principled Dense Representations Construction framework that reconstructs CLIP for OVDP based on two key insights.First, by analyzing the internal semantics encoded in the global cls token, we identify that multi-layer value embeddings serve as an informative basis for dense features. Second, we reveal that spatial aggregation tends to amplify semantic misalignment. Motivated by this, we design a lightweight Head-Selective Gating (HSG) module that adaptively reweights feature heads according to their intrinsic heterogeneity, enabling discriminative and alignment-friendly dense representations construction. Extensive experiments demonstrate that DenseRC delivers consistent and substantial gains across OVDP tasks including object detection and semantic segmentation, setting new state-of-the-art performance on multiple benchmarks.
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