Image segmentation is a key task in computer vision and image processing with important
applications such as scene understanding, medical image analysis, robotic perception …

Neural architecture search (NAS) has attracted increasing attention. In recent years,
individual search methods have been replaced by weight-sharing search methods for higher …

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 …

Prevalent semantic segmentation solutions, despite their different network designs (FCN
based or attention based) and mask decoding strategies (parametric softmax based or pixel …

We present SegFormer, a simple, efficient yet powerful semantic segmentation framework
which unifies Transformers with lightweight multilayer perceptron (MLP) decoders …

Dynamic neural network is an emerging research topic in deep learning. Compared to static
models which have fixed computational graphs and parameters at the inference stage …

Rotated object detection aims to identify and locate objects in images with arbitrary
orientation. In this scenario, the oriented directions of objects vary considerably across …

We present a causal inference framework to improve Weakly-Supervised Semantic
Segmentation (WSSS). Specifically, we aim to generate better pixel-level pseudo-masks by …

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

Image-text retrieval is a fundamental and crucial branch in information retrieval. Although
much progress has been made in bridging vision and language, it remains challenging …