Search Results for author:
Found 20 papers, 9 papers with code
Synth4Seg -- Learning Defect Data Synthesis for Defect Segmentation using Bi-level Optimization
Synthetic defect data generation is a popular approach for mitigating data challenges.
Smart Audit System Empowered by LLM
Manufacturing quality audits are pivotal for ensuring high product standards in mass production environments.
TIS-DPO: Token-level Importance Sampling for Direct Preference Optimization With Estimated Weights
In this work, we propose that the optimal data for DPO has equal expected rewards for each token in winning and losing responses, as there is no difference in token importance.
Apple Intelligence Foundation Language Models
We present foundation language models developed to power Apple Intelligence features, including a ~3 billion parameter model designed to run efficiently on devices and a large server-based language model designed for Private Cloud Compute.
MMAU: A Holistic Benchmark of Agent Capabilities Across Diverse Domains
Ultimately, MMAU not only sheds light on the capabilities and limitations of LLM agents but also enhances the interpretability of their performance.
A Survey of Multimodal Large Language Model from A Data-centric Perspective
Multimodal large language models (MLLMs) enhance the capabilities of standard large language models by integrating and processing data from multiple modalities, including text, vision, audio, video, and 3D environments.
ODGEN: Domain-specific Object Detection Data Generation with Diffusion Models
Given a domain-specific object detection dataset, we first fine-tune a pre-trained diffusion model on both cropped foreground objects and entire images to fit target distributions.
Direct Large Language Model Alignment Through Self-Rewarding Contrastive Prompt Distillation
In this paper, we propose a method to evaluate the response preference by using the output probabilities of response pairs under contrastive prompt pairs, which could achieve better performance on LLaMA2-7B and LLaMA2-13B compared to RLAIF.
VeCLIP: Improving CLIP Training via Visual-enriched Captions
For example, VeCLIP achieves up to +25. 2% gain in COCO and Flickr30k retrieval tasks under the 12M setting.
VISION Datasets: A Benchmark for Vision-based InduStrial InspectiON
We introduce the VISION Datasets, a diverse collection of 14 industrial inspection datasets, uniquely poised to meet these challenges.
Efficient ConvBN Blocks for Transfer Learning and Beyond
Subsequently, we propose a novel Tune mode to bridge the gap between Eval mode and Deploy mode.
RGI: robust GAN-inversion for mask-free image inpainting and unsupervised pixel-wise anomaly detection
In this paper, we propose a Robust GAN-inversion (RGI) method with a provable robustness guarantee to achieve image restoration under unknown \textit{gross} corruptions, where a small fraction of pixels are completely corrupted.
DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detection
In this study, we propose an improved model called DeSTSeg, which integrates a pre-trained teacher network, a denoising student encoder-decoder, and a segmentation network into one framework.
Ranked #52 on
Anomaly Detection
on MVTec AD
(using extra training data)
PAEDID: Patch Autoencoder Based Deep Image Decomposition For Pixel-level Defective Region Segmentation
To combine the best of both worlds, we present an unsupervised patch autoencoder based deep image decomposition (PAEDID) method for defective region segmentation.
Synthetic Defect Generation for Display Front-of-Screen Quality Inspection: A Survey
Display front-of-screen (FOS) quality inspection is essential for the mass production of displays in the manufacturing process.
Information Gain Propagation: a new way to Graph Active Learning with Soft Labels
Graph Neural Networks (GNNs) have achieved great success in various tasks, but their performance highly relies on a large number of labeled nodes, which typically requires considerable human effort.
Self-supervised Semi-supervised Learning for Data Labeling and Quality Evaluation
On active learning task, our method achieves 97. 0% Top-1 Accuracy on CIFAR10 with 0. 1% annotated data, and 83. 9% Top-1 Accuracy on CIFAR100 with 10% annotated data.
RIM: Reliable Influence-based Active Learning on Graphs
Message passing is the core of most graph models such as Graph Convolutional Network (GCN) and Label Propagation (LP), which usually require a large number of clean labeled data to smooth out the neighborhood over the graph.
BatchQuant: Quantized-for-all Architecture Search with Robust Quantizer
While single-shot quantized neural architecture search enjoys flexibility in both model architecture and quantization policy, the combined search space comes with many challenges, including instability when training the weight-sharing supernet and difficulty in navigating the exponentially growing search space.
Hardware Aware Neural Architecture Search
Model Optimization
+2
Video Frame Interpolation via Structure-Motion based Iterative Fusion
Inspired by the observation that audiences have different visual preferences on foreground and background objects, we for the first time propose to use saliency masks in the evaluation processes of the task of video frame interpolation.
