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Found 8 papers, 7 papers with code
RAP-SAM: Towards Real-Time All-Purpose Segment Anything
Segment Anything Model (SAM) is one remarkable model that can achieve generalized segmentation.
An Open and Comprehensive Pipeline for Unified Object Grounding and Detection
Grounding-DINO is a state-of-the-art open-set detection model that tackles multiple vision tasks including Open-Vocabulary Detection (OVD), Phrase Grounding (PG), and Referring Expression Comprehension (REC).
DST-Det: Simple Dynamic Self-Training for Open-Vocabulary Object Detection
We refer to this approach as the self-training strategy, which enhances recall and accuracy for novel classes without requiring extra annotations, datasets, and re-training.
Towards Open Vocabulary Learning: A Survey
To our knowledge, this is the first comprehensive literature review of open vocabulary learning.
PanopticPartFormer++: A Unified and Decoupled View for Panoptic Part Segmentation
Third, inspired by Mask2Former, based on our meta-architecture, we propose Panoptic-PartFormer++ and design a new part-whole cross-attention scheme to boost part segmentation qualities further.
Panoptic-PartFormer: Learning a Unified Model for Panoptic Part Segmentation
To the best of our knowledge, we are the first to solve the PPS problem via \textit{a unified and end-to-end transformer model.
Fashionformer: A simple, Effective and Unified Baseline for Human Fashion Segmentation and Recognition
This focus on joint human fashion segmentation and attribute recognition.
Multi-Agent Deep Reinforcement Learning enabled Computation Resource Allocation in a Vehicular Cloud Network
To support the ever increasing computational needs in such a vehicular network, the distributed virtual cloud network (VCN) is formed, based on which a computational resource sharing scheme through offloading among nearby vehicles is proposed.
