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 …
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 …
Openshape: Scaling up 3d shape representation towards open-world understanding
We introduce OpenShape, a method for learning multi-modal joint representations of text,
image, and point clouds. We adopt the commonly used multi-modal contrastive learning …
image, and point clouds. We adopt the commonly used multi-modal contrastive learning …
Dreamllm: Synergistic multimodal comprehension and creation
This paper presents DreamLLM, a learning framework that first achieves versatile
Multimodal Large Language Models (MLLMs) empowered with frequently overlooked …
Multimodal Large Language Models (MLLMs) empowered with frequently overlooked …
Pointgpt: Auto-regressively generative pre-training from point clouds
G Chen, M Wang, Y Yang, K Yu… - Advances in Neural …, 2024 - proceedings.neurips.cc
Large language models (LLMs) based on the generative pre-training transformer (GPT)
have demonstrated remarkable effectiveness across a diverse range of downstream tasks …
have demonstrated remarkable effectiveness across a diverse range of downstream tasks …
Point-bind & point-llm: Aligning point cloud with multi-modality for 3d understanding, generation, and instruction following
We introduce Point-Bind, a 3D multi-modality model aligning point clouds with 2D image,
language, audio, and video. Guided by ImageBind, we construct a joint embedding space …
language, audio, and video. Guided by ImageBind, we construct a joint embedding space …
[HTML][HTML] DILF: Differentiable rendering-based multi-view Image–Language Fusion for zero-shot 3D shape understanding
Zero-shot 3D shape understanding aims to recognize “unseen” 3D categories that are not
present in training data. Recently, Contrastive Language–Image Pre-training (CLIP) has …
present in training data. Recently, Contrastive Language–Image Pre-training (CLIP) has …
Clip-fo3d: Learning free open-world 3d scene representations from 2d dense clip
Training a 3D scene understanding model requires complicated human annotations, which
are laborious to collect and result in a model only encoding close-set object semantics. In …
are laborious to collect and result in a model only encoding close-set object semantics. In …
Pointmamba: A simple state space model for point cloud analysis
Transformers have become one of the foundational architectures in point cloud analysis
tasks due to their excellent global modeling ability. However, the attention mechanism has …
tasks due to their excellent global modeling ability. However, the attention mechanism has …
Uni3d: Exploring unified 3d representation at scale
Scaling up representations for images or text has been extensively investigated in the past
few years and has led to revolutions in learning vision and language. However, scalable …
few years and has led to revolutions in learning vision and language. However, scalable …