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Found 10 papers, 9 papers with code
Efficient Video Object Segmentation via Modulated Cross-Attention Memory
Recently, transformer-based approaches have shown promising results for semi-supervised video object segmentation.
PALO: A Polyglot Large Multimodal Model for 5B People
PALO offers visual reasoning capabilities in 10 major languages, including English, Chinese, Hindi, Spanish, French, Arabic, Bengali, Russian, Urdu, and Japanese, that span a total of ~5B people (65% of the world population).
Arabic Mini-ClimateGPT : A Climate Change and Sustainability Tailored Arabic LLM
To this end, we propose a light-weight Arabic Mini-ClimateGPT that is built on an open-source LLM and is specifically fine-tuned on a conversational-style instruction tuning curated Arabic dataset Clima500-Instruct with over 500k instructions about climate change and sustainability.
GLaMM: Pixel Grounding Large Multimodal Model
In this work, we present Grounding LMM (GLaMM), the first model that can generate natural language responses seamlessly intertwined with corresponding object segmentation masks.
Learnable Weight Initialization for Volumetric Medical Image Segmentation
Hybrid volumetric medical image segmentation models, combining the advantages of local convolution and global attention, have recently received considerable attention.
XrayGPT: Chest Radiographs Summarization using Medical Vision-Language Models
The latest breakthroughs in large vision-language models, such as Bard and GPT-4, have showcased extraordinary abilities in performing a wide range of tasks.
SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications
Using our proposed efficient additive attention, we build a series of models called "SwiftFormer" which achieves state-of-the-art performance in terms of both accuracy and mobile inference speed.
UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentation
Owing to the success of transformer models, recent works study their applicability in 3D medical segmentation tasks.
EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications
Our EdgeNeXt model with 1. 3M parameters achieves 71. 2% top-1 accuracy on ImageNet-1K, outperforming MobileViT with an absolute gain of 2. 2% with 28% reduction in FLOPs.
Ranked #29 on
Semantic Segmentation
on PASCAL VOC 2012 test
INSTA-YOLO: Real-Time Instance Segmentation
We compare our results to the state-of-the-art models for instance segmentation.
