65 citations · 113 across the 20 of their papers we have counts for
20 papers
Cisco Time Series Model Technical Report
Liang Gou, Archit Khare, Praneet Pabolu +10
We introduce the Cisco Time Series Model, a univariate zero-shot forecaster. This time series foundation model is the result of a general architectural innovation to a time series…
VISTA: A Visual Analytics Framework to Enhance Foundation Model-Generated Data Labels
Xiwei Xuan, Xiaoqi Wang, Wenbin He +4
The advances in multi-modal foundation models (FMs) (e.g., CLIP and LLaVA) have facilitated the auto-labeling of large-scale datasets, enhancing model performance in challenging do…
VISLIX: An XAI Framework for Validating Vision Models with Slice Discovery and Analysis
Xinyuan Yan, Xiwei Xuan, Jorge Piazentin Ono +6
Real-world machine learning models require rigorous evaluation before deployment, especially in safety-critical domains like autonomous driving and surveillance. The evaluation of…
MAG-V: A Multi-Agent Framework for Synthetic Data Generation and Verification
Saptarshi Sengupta, Harsh Vashistha, Kristal Curtis +4
Extending the capabilities of Large Language Models (LLMs) with functions or tools for environment interaction has led to the emergence of the agent paradigm. In industry, training…
InFiConD: Interactive No-code Fine-tuning with Concept-based Knowledge Distillation
Jinbin Huang, Wenbin He, Liang Gou +2
The emergence of large-scale pre-trained models has heightened their application in various downstream tasks, yet deployment is a challenge in environments with limited computation…
USE: Universal Segment Embeddings for Open-Vocabulary Image Segmentation
Xiaoqi Wang, Wenbin He, Xiwei Xuan +8
The open-vocabulary image segmentation task involves partitioning images into semantically meaningful segments and classifying them with flexible text-defined categories. The recen…
Investigating Interaction Modes and User Agency in Human-LLM Collaboration for Domain-Specific Data Analysis
Jiajing Guo, Vikram Mohanty, Jorge Piazentin Ono +3
Despite demonstrating robust capabilities in performing tasks related to general-domain data-operation tasks, Large Language Models (LLMs) may exhibit shortcomings when applied to…
A streamlined Approach to Multimodal Few-Shot Class Incremental Learning for Fine-Grained Datasets
Thang Doan, Sima Behpour, Xin Li +3
Few-shot Class-Incremental Learning (FSCIL) poses the challenge of retaining prior knowledge while learning from limited new data streams, all without overfitting. The rise of Visi…
AttributionScanner: A Visual Analytics System for Model Validation with Metadata-Free Slice Finding
Xiwei Xuan, Jorge Piazentin Ono, Liang Gou +2
Data slice finding is an emerging technique for validating machine learning (ML) models by identifying and analyzing subgroups in a dataset that exhibit poor performance, often cha…
InterVLS: Interactive Model Understanding and Improvement with Vision-Language Surrogates
Jinbin Huang, Wenbin He, Liang Gou +2
Deep learning models are widely used in critical applications, highlighting the need for pre-deployment model understanding and improvement. Visual concept-based methods, while inc…
GradOrth: A Simple yet Efficient Out-of-Distribution Detection with Orthogonal Projection of Gradients
Sima Behpour, Thang Doan, Xin Li +3
Detecting out-of-distribution (OOD) data is crucial for ensuring the safe deployment of machine learning models in real-world applications. However, existing OOD detection approach…
UP-DP: Unsupervised Prompt Learning for Data Pre-Selection with Vision-Language Models
Xin Li, Sima Behpour, Thang Doan +3
In this study, we investigate the task of data pre-selection, which aims to select instances for labeling from an unlabeled dataset through a single pass, thereby optimizing perfor…
Hyp-OW: Exploiting Hierarchical Structure Learning with Hyperbolic Distance Enhances Open World Object Detection
Thang Doan, Xin Li, Sima Behpour +3
Open World Object Detection (OWOD) is a challenging and realistic task that extends beyond the scope of standard Object Detection task. It involves detecting both known and unknown…
CLIP-S: Language-Guided Self-Supervised Semantic Segmentation
Wenbin He, Suphanut Jamonnak, Liang Gou +1
Existing semantic segmentation approaches are often limited by costly pixel-wise annotations and predefined classes. In this work, we present CLIP-S that leverages self-supervi…
Self-supervised Semantic Segmentation Grounded in Visual Concepts
Wenbin He, William Surmeier, Arvind Kumar Shekar +2
Unsupervised semantic segmentation requires assigning a label to every pixel without any human annotations. Despite recent advances in self-supervised representation learning for i…
Novelty-based Generalization Evaluation for Traffic Light Detection
Arvind Kumar Shekar, Laureen Lake, Liang Gou +1
The advent of Convolutional Neural Networks (CNNs) has led to their application in several domains. One noteworthy application is the perception system for autonomous driving that…
VATLD: A Visual Analytics System to Assess, Understand and Improve Traffic Light Detection
Liang Gou, Lincan Zou, Nanxiang Li +4
Traffic light detection is crucial for environment perception and decision-making in autonomous driving. State-of-the-art detectors are built upon deep Convolutional Neural Network…
Towards a Flexible Embedding Learning Framework
Chin-Chia Michael Yeh, Dhruv Gelda, Zhongfang Zhuang +3
Representation learning is a fundamental building block for analyzing entities in a database. While the existing embedding learning methods are effective in various data mining pro…
Dynamic Graph Representation Learning via Self-Attention Networks
Aravind Sankar, Yanhong Wu, Liang Gou +2
Learning latent representations of nodes in graphs is an important and ubiquitous task with widespread applications such as link prediction, node classification, and graph visualiz…
Raising Awareness of Conveyed Personality In Social Media Traces
Bin Xu, Liang Gou, Anbang Xu +2
Users' persistent social media contents like posts on Facebook Timeline are presented as an "exhibition" about the person to others, and managing these exhibitional contents for im…