15 citations · 64 across the 16 of their papers we have counts for
16 papers
Ground-V: Teaching VLMs to Ground Complex Instructions in Pixels
Yongshuo Zong, Qin Zhang, Dongsheng An +6
This work presents a simple yet effective workflow for automatically scaling instruction-following data to elicit pixel-level grounding capabilities of VLMs under complex instructi…
Open-World Dynamic Prompt and Continual Visual Representation Learning
Youngeun Kim, Jun Fang, Qin Zhang +7
The open world is inherently dynamic, characterized by ever-evolving concepts and distributions. Continual learning (CL) in this dynamic open-world environment presents a significa…
WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation
Jongheon Jeong, Yang Zou, Taewan Kim +3
Visual anomaly classification and segmentation are vital for automating industrial quality inspection. The focus of prior research in the field has been on training custom models f…
A Meta-Learning Approach to Predicting Performance and Data Requirements
Achin Jain, Gurumurthy Swaminathan, Paolo Favaro +8
We propose an approach to estimate the number of samples required for a model to reach a target performance. We find that the power law, the de facto principle to estimate model pe…
ComplETR: Reducing the cost of annotations for object detection in dense scenes with vision transformers
Achin Jain, Kibok Lee, Gurumurthy Swaminathan +4
Annotating bounding boxes for object detection is expensive, time-consuming, and error-prone. In this work, we propose a DETR based framework called ComplETR that is designed to ex…
SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentation
Yang Zou, Jongheon Jeong, Latha Pemula +2
Visual anomaly detection is commonly used in industrial quality inspection. In this paper, we present a new dataset as well as a new self-supervised learning method for ImageNet pr…