When we look around and perform complex tasks how we see and selectively process what
we see is crucial. However the lack of this visual search mechanism in current multimodal …

Recent advances in vision-language models (VLMs) have led to improved performance on
tasks such as visual question answering and image captioning. Consequently, these models …

In this paper we tackle the problem of Generalized Category Discovery (GCD). Specifically,
given a dataset with labelled and unlabelled images, the task is to cluster all images in the …

Object models are gradually progressing from predicting just category labels to providing
detailed descriptions of object instances. This motivates the need for large datasets which …

Instruction tuning, a new learning paradigm that fine-tunes pre-trained language models on
tasks specified through instructions, has shown promising zero-shot performance on various …

Vision and Language (VL) models have demonstrated remarkable zero-shot performance in
a variety of tasks. However, some aspects of complex language understanding still remain a …

Large-scale pre-trained Vision & Language (VL) models have shown remarkable
performance in many applications, enabling replacing a fixed set of supported classes with …

Vision and Language (VL) models offer an effective method for aligning representation
spaces of images and text allowing for numerous applications such as cross-modal retrieval …

In this paper, we consider the problem of simultaneously detecting objects and inferring their
visual attributes in an image, even for those with no manual annotations provided at the …

Composed image retrieval (CIR) task takes a composed query of image and text aiming to
search relative images for both conditions. Conventional CIR approaches need a training …