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Found 13 papers, 6 papers with code
MaterialSeg3D: Segmenting Dense Materials from 2D Priors for 3D Assets
Driven by powerful image diffusion models, recent research has achieved the automatic creation of 3D objects from textual or visual guidance.
Knowledge Transfer across Multiple Principal Component Analysis Studies
In the first step, we integrate the shared subspace information across multiple studies by a proposed method named as Grassmannian barycenter, instead of directly performing PCA on the pooled dataset.
CLAPP: Contrastive Language-Audio Pre-training in Passive Underwater Vessel Classification
Existing research on audio classification faces challenges in recognizing attributes of passive underwater vessel scenarios and lacks well-annotated datasets due to data privacy concerns.
MEAOD: Model Extraction Attack against Object Detectors
The widespread use of deep learning technology across various industries has made deep neural network models highly valuable and, as a result, attractive targets for potential attackers.
Geo2SigMap: High-Fidelity RF Signal Mapping Using Geographic Databases
First, we develop an automated framework that seamlessly integrates three open-source tools: OpenStreetMap (geographic databases), Blender (computer graphics), and Sionna (ray tracing), enabling the efficient generation of large-scale 3D building maps and ray tracing models.
Dissecting the Runtime Performance of the Training, Fine-tuning, and Inference of Large Language Models
For end users, our benchmark and findings help better understand different optimization techniques, training and inference frameworks, together with hardware platforms in choosing configurations for deploying LLMs.
CluCDD:Contrastive Dialogue Disentanglement via Clustering
A huge number of multi-participant dialogues happen online every day, which leads to difficulty in understanding the nature of dialogue dynamics for both humans and machines.
Recommend for a Reason: Unlocking the Power of Unsupervised Aspect-Sentiment Co-Extraction
Compliments and concerns in reviews are valuable for understanding users' shopping interests and their opinions with respect to specific aspects of certain items.
Powering Comparative Classification with Sentiment Analysis via Domain Adaptive Knowledge Transfer
We study Comparative Preference Classification (CPC) which aims at predicting whether a preference comparison exists between two entities in a given sentence and, if so, which entity is preferred over the other.
Towards Visual Explainable Active Learning for Zero-Shot Classification
This paper proposes a visual explainable active learning approach with its design and implementation called semantic navigator to solve the above problems.
Interpretable Click-Through Rate Prediction through Hierarchical Attention
For this problem, existing approaches, with shallow or deep architectures, have three major drawbacks.
Learning Gender-Neutral Word Embeddings
Word embedding models have become a fundamental component in a wide range of Natural Language Processing (NLP) applications.
Peeking the Impact of Points of Interests on Didi
Recently, the online car-hailing service, Didi, has emerged as a leader in the sharing economy.
