How well does CLIP understand texture?

1 code implementation • 22 Mar 2022 • Chenyun Wu, Subhransu Maji

We investigate how well CLIP understands texture in natural images described by natural language.

Material Classification Zero-Shot Learning

Describing Textures using Natural Language

no code implementations • ECCV 2020 • Chenyun Wu, Mikayla Timm, Subhransu Maji

Textures in natural images can be characterized by color, shape, periodicity of elements within them, and other attributes that can be described using natural language.

PhraseCut: Language-based Image Segmentation in the Wild

1 code implementation • CVPR 2020 • Chenyun Wu, Zhe Lin, Scott Cohen, Trung Bui, Subhransu Maji

We consider the problem of segmenting image regions given a natural language phrase, and study it on a novel dataset of 77, 262 images and 345, 486 phrase-region pairs.

Attribute Image Segmentation +2

Visualizing and Describing Fine-grained Categories as Textures

no code implementations • 2 Jul 2019 • Tsung-Yu Lin, Mikayla Timm, Chenyun Wu, Subhransu Maji

We analyze how categories from recent FGVC challenges can be described by their textural content.

Reasoning about Fine-grained Attribute Phrases using Reference Games

no code implementations • ICCV 2017 • Jong-Chyi Su, Chenyun Wu, Huaizu Jiang, Subhransu Maji

We collect a large dataset of such phrases by asking annotators to describe several visual differences between a pair of instances within a category.

Attribute Image Retrieval +1