Multimodal pre-trained models, such as CLIP, are popular for zero-shot classification due to
their open-vocabulary flexibility and high performance. However, vision-language models …

Unsupervised domain adaptation (UDA) involves learning class semantics from labeled
data within a source domain that generalize to an unseen target domain. UDA methods are …

Robots are increasingly envisioned to interact in real-world scenarios, where they must
continuously adapt to new situations. To detect and grasp novel objects, zero-shot pose …

The nature of diversity in real-world environments necessitates neural network models to
expand from closed category settings to accommodate novel emerging categories. In this …

Detecting Human-Object Interactions (HOI) in zero-shot settings, where models must handle
unseen classes, poses significant challenges. Existing methods that rely on aligning visual …

Multi-label image recognition is a fundamental task in computer vision. Recently, Vision-
Language Models (VLMs) have made notable advancements in this area. However …

Existing open-vocabulary object detection (OVD) develops methods for testing unseen
categories by aligning object region embeddings with corresponding VLM features. A recent …

The fast advancement of Large Vision-Language Models (LVLMs) has shown immense
potential. These models are increasingly capable of tackling abstract visual tasks. Geometric …

We present Seg-TTO, a novel framework for zero-shot, open-vocabulary semantic
segmentation (OVSS), designed to excel in specialized domain tasks. While current open …

Open-vocabulary object detection (OVOD) aims at localizing and recognizing visual objects
from novel classes unseen at the training time. Whereas, empirical studies reveal that …