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Found 16 papers, 4 papers with code
Fine-Grained Urban Flow Inference with Multi-scale Representation Learning
In order to effectively learn multi-scale information across time and space, we propose an effective fine-grained urban flow inference model called UrbanMSR, which uses self-supervised contrastive learning to obtain dynamic multi-scale representations of neighborhood-level and city-level geographic information, and fuses multi-scale representations to improve fine-grained accuracy.
Diverse Teacher-Students for Deep Safe Semi-Supervised Learning under Class Mismatch
Semi-supervised learning can significantly boost model performance by leveraging unlabeled data, particularly when labeled data is scarce.
3DBench: A Scalable 3D Benchmark and Instruction-Tuning Dataset
Evaluating the performance of Multi-modal Large Language Models (MLLMs), integrating both point cloud and language, presents significant challenges.
CLIP-driven Outliers Synthesis for few-shot OOD detection
Few-shot OOD detection focuses on recognizing out-of-distribution (OOD) images that belong to classes unseen during training, with the use of only a small number of labeled in-distribution (ID) images.
CodeS: Natural Language to Code Repository via Multi-Layer Sketch
For feedback-based evaluation, we develop a VSCode plugin for CodeS and engage 30 participants in conducting empirical studies.
Urban Region Embedding via Multi-View Contrastive Prediction
Recently, learning urban region representations utilizing multi-modal data (information views) has become increasingly popular, for deep understanding of the distributions of various socioeconomic features in cities.
3DAxiesPrompts: Unleashing the 3D Spatial Task Capabilities of GPT-4V
In this work, we present a new visual prompting method called 3DAxiesPrompts (3DAP) to unleash the capabilities of GPT-4V in performing 3D spatial tasks.
Point Cloud Pre-training with Diffusion Models
This generator aggregates the features extracted by the backbone and employs them as the condition to guide the point-to-point recovery from the noisy point cloud, thereby assisting the backbone in capturing both local and global geometric priors as well as the global point density distribution of the object.
Latent Evolution Model for Change Point Detection in Time-varying Networks
Our method focuses on learning the low-dimensional representations of networks and capturing the evolving patterns of these learned latent representations simultaneously.
Exploring Linear Feature Disentanglement For Neural Networks
Non-linear activation functions, e. g., Sigmoid, ReLU, and Tanh, have achieved great success in neural networks (NNs).
Series Photo Selection via Multi-view Graph Learning
Series photo selection (SPS) is an important branch of the image aesthetics quality assessment, which focuses on finding the best one from a series of nearly identical photos.
Exploring Domain-Invariant Parameters for Source Free Domain Adaptation
In contrast, we provide a fascinating insight: rather than attempting to learn domain-invariant representations, it is better to explore the domain-invariant parameters of the source model.
Field-wise Learning for Multi-field Categorical Data
We present a model that utilizes linear models with variance and low-rank constraints, to help it generalize better and reduce the number of parameters.
Potential Passenger Flow Prediction: A Novel Study for Urban Transportation Development
In this paper, this specific problem is termed as potential passenger flow (PPF) prediction, which is a novel and important study connected with urban computing and intelligent transportation systems.
Sample Adaptive Multiple Kernel Learning for Failure Prediction of Railway Points
In this paper, we formulate our prediction task as a multiple kernel learning problem with missing kernels.
Inferring the Importance of Product Appearance: A Step Towards the Screenless Revolution
In this paper, we aim to infer the significance of every item's appearance in consumer decision making and identify the group of items that are suitable for screenless shopping.
