Zero-shot image captioning (IC) without well-paired image-text data can be divided into two categories, training-free and text-only-training. Generally, these two types of methods realize zero-shot IC by integrating pre-trained vision-language models like CLIP for image-text similarity evaluation and a pre-trained language model (LM) for caption generation. The main difference be-tween them is whether using a textual corpus to train the LM. Though achieving attractive performance w.r.t. some metrics, existing methods often exhibit some com-mon drawbacks. Training-free methods tend to produce hallucinations, while text-only-training often lose gener-alization capability. To move forward, in this paper, we propose a novel Memory-Augmented zero-shot image Captioning framework (MeaCap). Specifically, equipped with a textual memory, we introduce a retrieve-then-filter module to get key concepts that are highly related to the image. By deploying our proposed memory-augmented visual-related fusion score in a keywords-to-sentence LM, MeaCap can generate concept-centered captions that keep high consistency with the image with fewer hallucinations and more world-knowledge. The framework of Mea-Cap achieves the state-of-the-art performance on a se-ries of zero-shot IC settings. Our code is available at https://github.com/joeyzOz/MeaCap.

Fast large-scale network scanning is an important way to understand internet service configurations and security in real time, among which stateless scan is representative. Existing stateless scanners can perform single-packet scans for internet-wide network measurements but are limited to host discovery or port scanning. To obtain further information over TCP, slower stateful scanners must be used in conjunction which spend more time and memory because of connection state maintenance. This paper proposes a novel stateless scanning method, which can establish TCP connections and obtain further responses in a completely stateless manner. Based on this method, we implement a stateless scanner named ZBanner. Experiments show that ZBanner performs better than current state-of-the-art solutions in terms of scan rate and memory usage. ZBanner achieves a scan rate at least three times faster than current tools for generic ports and over 90 times faster for open ports while keeping a minimum and stable memory usage.