In the field of visual scene understanding, deep neural networks have made impressive
advancements in various core tasks like segmentation, tracking, and detection. However …

As the most fundamental scene understanding tasks, object detection and segmentation
have made tremendous progress in deep learning era. Due to the expensive manual …

The objective of Active Learning is to strategically label a subset of the dataset to maximize
performance within a predetermined labeling budget. In this study, we harness features …

Open-vocabulary learning has emerged as a cutting-edge research area, particularly in light
of the widespread adoption of vision-based foundational models. Its primary objective is to …

The crux of effective out-of-distribution (OOD) detection lies in acquiring a robust in-
distribution (ID) representation, distinct from OOD samples. While previous methods …

A serious issue that harms the performance of zero-shot visual recognition is named
objective misalignment, ie, the learning objective prioritizes improving the recognition …

Abstract Panoramic images, capturing a 360\(^\circ\) field of view (FoV), encompass
omnidirectional spatial information crucial for scene understanding. However, it is not only …

Pre-trained vision-language models, eg, CLIP, have been successfully applied to zero-shot
semantic segmentation. Existing CLIP-based approaches primarily utilize visual features …

The capacity of Large Language Models (LLMs) to comprehend and reason over long
contexts is pivotal for advancements in diverse fields. Yet, they still stuggle with capturing …

The pre-trained vision-language model, exemplified by CLIP, advances zero-shot semantic
segmentation by aligning visual features with class embeddings through a transformer …