Search Results for author:
Found 20 papers, 3 papers with code
CA-BERT: Leveraging Context Awareness for Enhanced Multi-Turn Chat Interaction
Effective communication in automated chat systems hinges on the ability to understand and respond to context.
Draw Like an Artist: Complex Scene Generation with Diffusion Model via Composition, Painting, and Retouching
Recent advances in text-to-image diffusion models have demonstrated impressive capabilities in image quality.
Scalable Knowledge Refactoring using Constrained Optimisation
Our first key idea is to encode the problem with decision variables based on literals rather than rules.
Automatic Dataset Construction (ADC): Sample Collection, Data Curation, and Beyond
Furthermore, we design three benchmark datasets focused on label noise detection, label noise learning, and class-imbalanced learning.
Kalib: Markerless Hand-Eye Calibration with Keypoint Tracking
Hand-eye calibration involves estimating the transformation between the camera and the robot.
PIN: A Knowledge-Intensive Dataset for Paired and Interleaved Multimodal Documents
Recent advancements in Large Multimodal Models (LMMs) have leveraged extensive multimodal datasets to enhance capabilities in complex knowledge-driven tasks.
IntentionQA: A Benchmark for Evaluating Purchase Intention Comprehension Abilities of Language Models in E-commerce
Enhancing Language Models' (LMs) ability to understand purchase intentions in E-commerce scenarios is crucial for their effective assistance in various downstream tasks.
MAP-Neo: Highly Capable and Transparent Bilingual Large Language Model Series
To improve the transparency of LLMs, the research community has formed to open-source truly open LLMs (e. g., Pythia, Amber, OLMo), where more details (e. g., pre-training corpus and training code) are being provided.
A Survey on Human-AI Teaming with Large Pre-Trained Models
In the rapidly evolving landscape of artificial intelligence (AI), the collaboration between human intelligence and AI systems, known as Human-AI (HAI) Teaming, has emerged as a cornerstone for advancing problem-solving and decision-making processes.
Tag-Based Annotation for Avatar Face Creation
Currently, digital avatars can be created manually using human images as reference.
Do humans and machines have the same eyes? Human-machine perceptual differences on image classification
Trained computer vision models are assumed to solve vision tasks by imitating human behavior learned from training labels.
Interpreting Hidden Semantics in the Intermediate Layers of 3D Point Cloud Classification Neural Network
It delivers the class Relevance to the activated neurons in the intermediate layers in a back-propagation manner, and associates the activation of neurons with the input points to visualize the hidden semantics of each layer.
Tag-based annotation creates better avatars
Compared to direct annotation of labels, the proposed method: produces higher annotator agreements, causes machine learning to generates more consistent predictions, and only requires a marginal cost to add new rendering systems.
AgileAvatar: Stylized 3D Avatar Creation via Cascaded Domain Bridging
We propose a novel self-supervised learning framework to create high-quality stylized 3D avatars with a mix of continuous and discrete parameters.
How Much Does Input Data Type Impact Final Face Model Accuracy?
This idealized synthetic analysis is then compared to real results from several methods for constructing 3D faces from 2D photographs.
Can Graph Neural Networks Learn to Solve MaxSAT Problem?
With the rapid development of deep learning techniques, various recent work has tried to apply graph neural networks (GNNs) to solve NP-hard problems such as Boolean Satisfiability (SAT), which shows the potential in bridging the gap between machine learning and symbolic reasoning.
ε-weakened Robustness of Deep Neural Networks
This paper introduces a notation of $\varepsilon$-weakened robustness for analyzing the reliability and stability of deep neural networks (DNNs).
Gated Transformer Networks for Multivariate Time Series Classification
In this work, we explored a simple extension of the current Transformer Networks with gating, named Gated Transformer Networks (GTN) for the multivariate time series classification problem.
DuelGAN: A Duel Between Two Discriminators Stabilizes the GAN Training
In this paper, we introduce DuelGAN, a generative adversarial network (GAN) solution to improve the stability of the generated samples and to mitigate mode collapse.
Ranked #3 on
Image Generation
on Fashion-MNIST
