29 citations · 35 across the 12 of their papers we have counts for
8 papers · 1 filter
ZeroBench: An Impossible Visual Benchmark for Contemporary Large Multimodal Models
Jonathan Roberts, Mohammad Reza Taesiri, Ansh Sharma +31
Large Multimodal Models (LMMs) exhibit shortfalls when interpreting images and, by some measures, have poorer spatial cognition than young children or animals. Despite this, they a…
ProMerge: Prompt and Merge for Unsupervised Instance Segmentation
Dylan Li, Gyungin Shin
Unsupervised instance segmentation aims to segment distinct object instances in an image without relying on human-labeled data. This field has recently seen significant advancement…
Tails Tell Tales: Chapter-Wide Manga Transcriptions with Character Names
Ragav Sachdeva, Gyungin Shin, Andrew Zisserman
Enabling engagement of manga by visually impaired individuals presents a significant challenge due to its inherently visual nature. With the goal of fostering accessibility, this p…
Zero-shot Unsupervised Transfer Instance Segmentation
Gyungin Shin, Samuel Albanie, Weidi Xie
Segmentation is a core computer vision competency, with applications spanning a broad range of scientifically and economically valuable domains. To date, however, the prohibitive c…
NamedMask: Distilling Segmenters from Complementary Foundation Models
Gyungin Shin, Weidi Xie, Samuel Albanie
The goal of this work is to segment and name regions of images without access to pixel-level labels during training. To tackle this task, we construct segmenters by distilling the…
ReCo: Retrieve and Co-segment for Zero-shot Transfer
Gyungin Shin, Weidi Xie, Samuel Albanie
Semantic segmentation has a broad range of applications, but its real-world impact has been significantly limited by the prohibitive annotation costs necessary to enable deployment…
Unsupervised Salient Object Detection with Spectral Cluster Voting
Gyungin Shin, Samuel Albanie, Weidi Xie
In this paper, we tackle the challenging task of unsupervised salient object detection (SOD) by leveraging spectral clustering on self-supervised features. We make the following co…
All you need are a few pixels: semantic segmentation with PixelPick
Gyungin Shin, Weidi Xie, Samuel Albanie
A central challenge for the task of semantic segmentation is the prohibitive cost of obtaining dense pixel-level annotations to supervise model training. In this work, we show that…