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Found 9 papers, 7 papers with code
ZeroBench: An Impossible Visual Benchmark for Contemporary Large Multimodal Models
Large Multimodal Models (LMMs) exhibit major shortfalls when interpreting images and, by some measures, have poorer spatial cognition than small children or animals.
ProMerge: Prompt and Merge for Unsupervised Instance Segmentation
Unsupervised instance segmentation aims to segment distinct object instances in an image without relying on human-labeled data.
Tails Tell Tales: Chapter-Wide Manga Transcriptions with Character Names
Enabling engagement of manga by visually impaired individuals presents a significant challenge due to its inherently visual nature.
arXiVeri: Automatic table verification with GPT
In this paper, we propose to meet this challenge through the novel task of automatic table verification (AutoTV), in which the objective is to verify the accuracy of numerical data in tables by cross-referencing cited sources.
Zero-shot Unsupervised Transfer Instance Segmentation
Segmentation is a core computer vision competency, with applications spanning a broad range of scientifically and economically valuable domains.
NamedMask: Distilling Segmenters from Complementary Foundation Models
Our method, termed NamedMask, begins by using CLIP to construct category-specific archives of images.
ReCo: Retrieve and Co-segment for Zero-shot Transfer
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
In this paper, we tackle the challenging task of unsupervised salient object detection (SOD) by leveraging spectral clustering on self-supervised features.
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Unsupervised Saliency Detection
on ECSSD
All you need are a few pixels: semantic segmentation with PixelPick
A central challenge for the task of semantic segmentation is the prohibitive cost of obtaining dense pixel-level annotations to supervise model training.
