The virtual-to-real paradigm, ie, training models on virtual data and then applying them to
solve real-world problems, has attracted more and more attention from various domains by …

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

We study the zero-shot Composed Image Retrieval (ZS-CIR) task which is to retrieve the
target image given a reference image and a description without training on the triplet …

Open-vocabulary object detection aims to provide object detectors trained on a fixed set of
object categories with the generalizability to detect objects described by arbitrary text …

The goal of Camouflaged object detection (COD) is to detect objects that are visually
embedded in their surroundings. Existing COD methods only focus on detecting …

Open World Object Detection (OWOD), simulating the real dynamic world where knowledge
grows continuously, attempts to detect both known and unknown classes and incrementally …

Dynamic environments in factories, surgical robotics, and warehouses increasingly involve
humans, machines, robots, and various other objects such as tools, fixtures, conveyors, and …

Prompt tuning with large-scale pretrained vision-language models empowers open-
vocabulary predictions trained on limited base categories, eg, object classification and …

Two-stage object detectors generate object proposals and classify them to detect objects in
images. These proposals often do not perfectly contain the objects but overlap with them in …

Zero-shot object detection aims to localize and recognize objects of unseen classes. Most of
existing works face two problems: the low recall of RPN in unseen classes and the confusion …