TL;DR
SemBridge is introduced, ontology-guided zero-shot fashion segmentation pipeline that employs a large language model (LLM) as a semantic bridge to convert free-form user prompts into structured fashion concepts and synonym-enriched detection queries to improve detection accuracy.
Recent unified open-vocabulary detection (OVD) supports heterogeneous prompts, including text queries, visual exemplars, and their combinations, but often rely on increasingly complex designs such as heavy cross-modal fusion, staged training, and iterative annotation pipelines. We revisit whether such complexity is nec...
Open-vocabulary semantic segmentation aims to establish dense pixel-wise correspondences between images and open-ended semantic categories. While most existing methods build upon CLIP's image-text alignment capability, they require substantial architectural modifications to adapt image-level understanding to pixel-leve...
Pose-Star++ is proposed, a training-free, plug-and-play framework that introduces two core innovations: an LVLM-based Understanding Module that shifts from word- to sentence-level semantic-visual comprehension, eliminating cumbersome instruction pre-parsing and enabling robust understanding of complex natural language.
Experimental results show that the proposed open-vocabulary object detection framework performs excellently on multiple benchmark datasets such as LVIS and COCO-O, demonstrating stronger adaptability to complex scenarios.
OVIP-SG is presented, a unified framework for instance-preserving semantic mapping, functional scene partitioning, and language-guided small, fine-grained object retrieval that outperforms ConceptGraphs under a unified evaluation protocol on Replica.
Open-vocabulary 3D Gaussian segmentation typically binds semantic features to the per-Gaussian state via per-scene gradient training (1-4 h) or the more recent closed-form distillation. Both commit each primitive to a single feature; therefore, boundary Gaussians spanning multiple 2D instances are forced into a hard de...
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