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A survey on self-supervised learning: Algorithms, applications, and future trends
Deep supervised learning algorithms typically require a large volume of labeled data to
achieve satisfactory performance. However, the process of collecting and labeling such data …
achieve satisfactory performance. However, the process of collecting and labeling such data …
From google gemini to openai q*(q-star): A survey of resha** the generative artificial intelligence (ai) research landscape
This comprehensive survey explored the evolving landscape of generative Artificial
Intelligence (AI), with a specific focus on the transformative impacts of Mixture of Experts …
Intelligence (AI), with a specific focus on the transformative impacts of Mixture of Experts …
Point transformer v3: Simpler faster stronger
This paper is not motivated to seek innovation within the attention mechanism. Instead it
focuses on overcoming the existing trade-offs between accuracy and efficiency within the …
focuses on overcoming the existing trade-offs between accuracy and efficiency within the …
Scaling language-image pre-training via masking
Abstract We present Fast Language-Image Pre-training (FLIP), a simple and more efficient
method for training CLIP. Our method randomly masks out and removes a large portion of …
method for training CLIP. Our method randomly masks out and removes a large portion of …
Ulip: Learning a unified representation of language, images, and point clouds for 3d understanding
The recognition capabilities of current state-of-the-art 3D models are limited by datasets with
a small number of annotated data and a pre-defined set of categories. In its 2D counterpart …
a small number of annotated data and a pre-defined set of categories. In its 2D counterpart …
Mvimgnet: A large-scale dataset of multi-view images
Being data-driven is one of the most iconic properties of deep learning algorithms. The birth
of ImageNet drives a remarkable trend of" learning from large-scale data" in computer vision …
of ImageNet drives a remarkable trend of" learning from large-scale data" in computer vision …
Contrast with reconstruct: Contrastive 3d representation learning guided by generative pretraining
Mainstream 3D representation learning approaches are built upon contrastive or generative
modeling pretext tasks, where great improvements in performance on various downstream …
modeling pretext tasks, where great improvements in performance on various downstream …
3d-vista: Pre-trained transformer for 3d vision and text alignment
Abstract 3D vision-language grounding (3D-VL) is an emerging field that aims to connect the
3D physical world with natural language, which is crucial for achieving embodied …
3D physical world with natural language, which is crucial for achieving embodied …
Transformer-based visual segmentation: A survey
Visual segmentation seeks to partition images, video frames, or point clouds into multiple
segments or groups. This technique has numerous real-world applications, such as …
segments or groups. This technique has numerous real-world applications, such as …
Ulip-2: Towards scalable multimodal pre-training for 3d understanding
Recent advancements in multimodal pre-training have shown promising efficacy in 3D
representation learning by aligning multimodal features across 3D shapes their 2D …
representation learning by aligning multimodal features across 3D shapes their 2D …