21 citations · 40 across the 5 of their papers we have counts for
8 papers
I2MVFormer: Large Language Model Generated Multi-View Document Supervision for Zero-Shot Image Classification
Muhammad Ferjad Naeem, Muhammad Gul Zain Ali Khan, Yongqin Xian +4
Recent works have shown that unstructured text (documents) from online sources can serve as useful auxiliary information for zero-shot image classification. However, these methods…
Learning Attention Propagation for Compositional Zero-Shot Learning
Muhammad Gul Zain Ali Khan, Muhammad Ferjad Naeem, Luc Van Gool +3
Compositional zero-shot learning aims to recognize unseen compositions of seen visual primitives of object classes and their states. While all primitives (states and objects) are o…
I2DFormer: Learning Image to Document Attention for Zero-Shot Image Classification
Muhammad Ferjad Naeem, Yongqin Xian, Luc Van Gool +1
Despite the tremendous progress in zero-shot learning(ZSL), the majority of existing methods still rely on human-annotated attributes, which are difficult to annotate and scale. An…
Learning Graph Embeddings for Compositional Zero-shot Learning
Muhammad Ferjad Naeem, Yongqin Xian, Federico Tombari +1
In compositional zero-shot learning, the goal is to recognize unseen compositions (e.g. old dog) of observed visual primitives states (e.g. old, cute) and objects (e.g. car, dog) i…
Open World Compositional Zero-Shot Learning
Massimiliano Mancini, Muhammad Ferjad Naeem, Yongqin Xian +1
Compositional Zero-Shot learning (CZSL) requires to recognize state-object compositions unseen during training. In this work, instead of assuming prior knowledge about the unseen c…
Reliable Fidelity and Diversity Metrics for Generative Models
Muhammad Ferjad Naeem, Seong Joon Oh, Youngjung Uh +2
Devising indicative evaluation metrics for the image generation task remains an open problem. The most widely used metric for measuring the similarity between real and generated im…