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- Generalized Few-Shot Point Cloud Segmentation Via Geometric Words20
This work proposes the geometric words to represent geometric components shared between the base and novel classes, and incorporates them into a novel geometric-aware semantic representation to facilitate better generalization to the new classes without forgetting the old ones.
- Sketch-a-Segmenter: Sketch-Based Photo Segmenter Generation19
It is shown, for the first time, that it is possible to generate a photo-segmentation model of a novel category using just a single sketch and furthermore exploit the unique fine-grained characteristics of sketch to produce more detailed segmentation.
- Rethink Cross-Modal Fusion in Weakly-Supervised Audio-Visual Video Parsing14
Cross-audio prediction consistency is proposed to suppress the impact of irrelevant audio information on visual event prediction and the messenger-guided mid-fusion transformer to reduce the uncorrelated cross-modal context in the fusion.
- CLAIR: CLIP-Aided Weakly Supervised Zero-Shot Cross-Domain Image Retrieval1
This paper proposes CLAIR to refine the noisy pseudo-labels with a confidence score from the similarity between the CLIP text and image features, and designs inter-instance and inter-cluster contrastive losses to encode images into a class-aware latent space, and an inter-domain contrastive loss to alleviate domain discrepancies.
- Sketch-based Video Object Segmentation: Benchmark and Analysis1
Experimental results show sketch is more effective yet annotation-efficient than other references, such as photo masks, language and scribble, and what the most effective design for incorporating a sketch reference is.
- SKVOS: Sketch-Based Video Object Segmentation with a Large-Scale Benchmark–
This paper proposes sketch-based video object segmentation (SKVOS), a novel task that segments objects consistently across video frames using human-drawn sketches as queries using the Temporal Relation Module and Sketch-Anchored Contrastive Learning.
Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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