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Found 3 papers, 2 papers with code
SeCG: Semantic-Enhanced 3D Visual Grounding via Cross-modal Graph Attention
3D visual grounding aims to automatically locate the 3D region of the specified object given the corresponding textual description.
SurgicalPart-SAM: Part-to-Whole Collaborative Prompting for Surgical Instrument Segmentation
Specifically, we achieve this by proposing (1) Collaborative Prompts that describe instrument structures via collaborating category-level and part-level texts; (2) Cross-Modal Prompt Encoder that encodes text prompts jointly with visual embeddings into discriminative part-level representations; and (3) Part-to-Whole Adaptive Fusion and Hierarchical Decoding that adaptively fuse the part-level representations into a whole for accurate instrument segmentation in surgical scenarios.
Self-supervised Multi-view Stereo via Effective Co-Segmentation and Data-Augmentation
Recent studies have witnessed that self-supervised methods based on view synthesis obtain clear progress on multi-view stereo (MVS).
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