This paper aims to efficiently enable Large Language Models (LLMs) to use multi-modal
tools. The advanced proprietary LLMs, such as ChatGPT and GPT-4, have shown great …

Mainstream object detectors based on the fully convolutional network has achieved
impressive performance. While most of them still need a hand-designed non-maximum …

Weakly supervised object localization (WSOL) is a challenging problem when given image
category labels but requires to learn object localization models. Optimizing a convolutional …

In this paper, we present a conceptually simple, strong, and efficient framework for panoptic
segmentation, called Panoptic FCN. Our approach aims to represent and predict foreground …

In contrast to the fully supervised methods using pixel-wise mask labels, box-supervised
instance segmentation takes advantage of the simple box annotations, which has recently …

Recently, numerous handcrafted and searched networks have been applied for semantic
segmentation. However, previous works intend to handle inputs with various scales in pre …

Weakly supervised semantic segmentation is receiving great attention due to its low human
annotation cost. In this paper, we aim to tackle bounding box supervised semantic …

Sparsely annotated semantic segmentation (SASS) aims to train a segmentation network
with coarse-grained (ie, point-, scribble-, and block-wise) supervisions, where only a small …

State-of-the-art stereo matching networks have difficulties in generalizing to new unseen
environments due to significant domain differences, such as color, illumination, contrast, and …

Weakly supervised object localization (WSOL) remains an open problem due to the
deficiency of finding object extent information using a classification network. While prior …