Publications (14)
VeCLIP: Improving CLIP Training via Visual-enriched Captions
Zhengfeng Lai, Haotian Zhang, Bowen Zhang +9
Large-scale web-crawled datasets are fundamental for the success of pre-training vision-language models, such as CLIP. However, the inherent noise and potential irrelevance of web-…
Synth4Seg -- Learning Defect Data Synthesis for Defect Segmentation using Bi-level Optimization
Shancong Mou, Raviteja Vemulapalli, Shiyu Li +8
Defect segmentation is crucial for quality control in advanced manufacturing, yet data scarcity poses challenges for state-of-the-art supervised deep learning. Synthetic defect dat…
SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection
Yue Zhao, Xiyang Hu, Cheng Cheng +11
Outlier detection (OD) is a key machine learning (ML) task for identifying abnormal objects from general samples with numerous high-stake applications including fraud detection and…
PAEDID: Patch Autoencoder Based Deep Image Decomposition For Pixel-level Defective Region Segmentation
Shancong Mou, Meng Cao, Haoping Bai +3
Unsupervised pixel-level defective region segmentation is an important task in image-based anomaly detection for various industrial applications. The state-of-the-art methods have…
MMAU: A Holistic Benchmark of Agent Capabilities Across Diverse Domains
Guoli Yin, Haoping Bai, Shuang Ma +21
Recent advances in large language models (LLMs) have increased the demand for comprehensive benchmarks to evaluate their capabilities as human-like agents. Existing benchmarks, whi…
BatchQuant: Quantized-for-all Architecture Search with Robust Quantizer
Haoping Bai, Meng Cao, Ping Huang +1
As the applications of deep learning models on edge devices increase at an accelerating pace, fast adaptation to various scenarios with varying resource constraints has become a cr…
Direct Large Language Model Alignment Through Self-Rewarding Contrastive Prompt Distillation
Aiwei Liu, Haoping Bai, Zhiyun Lu +5
Aligning large language models (LLMs) with human expectations without human-annotated preference data is an important problem. In this paper, we propose a method to evaluate the re…
RGI: robust GAN-inversion for mask-free image inpainting and unsupervised pixel-wise anomaly detection
Shancong Mou, Xiaoyi Gu, Meng Cao +4
Generative adversarial networks (GANs), trained on a large-scale image dataset, can be a good approximator of the natural image manifold. GAN-inversion, using a pre-trained generat…
MTL-NAS: Task-Agnostic Neural Architecture Search towards General-Purpose Multi-Task Learning
Yuan Gao, Haoping Bai, Zequn Jie +3
We propose to incorporate neural architecture search (NAS) into general-purpose multi-task learning (GP-MTL). Existing NAS methods typically define different search spaces accordin…
TIS-DPO: Token-level Importance Sampling for Direct Preference Optimization With Estimated Weights
Aiwei Liu, Haoping Bai, Zhiyun Lu +9
Direct Preference Optimization (DPO) has been widely adopted for preference alignment of Large Language Models (LLMs) due to its simplicity and effectiveness. However, DPO is deriv…
Analyzing Material Recognition Performance of Thermal Tactile Sensing using a Large Materials Database and a Real Robot
Haoping Bai, Haofeng Chen, Elizabeth Healy +2
In this paper we focus on analyzing the thermal modality of tactile sensing for material recognition using a large materials database. Many factors affect thermal recognition perfo…
Self-supervised Semi-supervised Learning for Data Labeling and Quality Evaluation
Haoping Bai, Meng Cao, Ping Huang +1
As the adoption of deep learning techniques in industrial applications grows with increasing speed and scale, successful deployment of deep learning models often hinges on the avai…
VISION Datasets: A Benchmark for Vision-based InduStrial InspectiON
Haoping Bai, Shancong Mou, Tatiana Likhomanenko +6
Despite progress in vision-based inspection algorithms, real-world industrial challenges -- specifically in data availability, quality, and complex production requirements -- often…
SUOD: Toward Scalable Unsupervised Outlier Detection
Yue Zhao, Xueying Ding, Jianing Yang +1
Outlier detection is a key field of machine learning for identifying abnormal data objects. Due to the high expense of acquiring ground truth, unsupervised models are often chosen…