Although perception systems have made remarkable advancements in recent years they still
rely on explicit human instruction or pre-defined categories to identify the target objects …

Open-vocabulary models are a promising new paradigm for image classification. Unlike
traditional classification models, open-vocabulary models classify among any arbitrary set of …

In this paper, we introduce Semantic-SAM, a universal image segmentation model to enable
segment and recognize anything at any desired granularity. Our model offers two key …

Recently, significant progress has been made in masked image modeling to catch up to
masked language modeling. However, unlike words in NLP, the lack of semantic …

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 …

Multimodal large language models (MLLMs) have recently achieved impressive general-
purpose vision-language capabilities through visual instruction tuning. However current …

Object detection has been expanded from a limited number of categories to open
vocabulary. Moving forward, a complete intelligent vision system requires understanding …

Interpretable methods based on prototypical patches recognize various components in an
image in order to explain their reasoning to humans. However, existing prototype-based …

The great success of deep learning heavily relies on increasingly larger training data, which
comes at a price of huge computational and infrastructural costs. This poses crucial …

Accurately estimating the 3D pose and shape is an essential step towards understanding
animal behavior, and can potentially benefit many downstream applications, such as wildlife …