8 accepted papers
Traditional dialogue retrieval aims to select the most appropriate utterance or image from recent dialogue history. However, they often fail to meet users’ actual needs for revisiting semantically coherent content scattered across long-form conversations. To fill this gap, we define the Fine-grained
Vision-language models (VLMs) often struggle with compositional reasoning due to insufficient high-quality image-text data. To tackle this challenge, we propose a novel block-based diffusion approach that automatically generates counterfactual datasets without manual annotation. Our method utilizes…
Current legal frameworks consider AI-generated works eligible for copyright protection when they meet originality requirements and involve substantial human intellectual input. However, systematic legal standards and reliable evaluation methods for AI art copyrights are lacking. Through comprehensiv…
With the rapid advancement of generative models, we can now create highly realistic images. This represents a significant technical breakthrough but also introduces new challenges for copyright protection. Previous methods for detecting copyright infringement in AI-generated images mainly depend on…
The advancement of Generative Adversarial Networks (GANs) and diffusion models significantly enhances the realism of synthetic images, driving progress in image processing and creative design. However, this progress also necessitates the development of effective detection methods, as synthetic image…
Approximately 200 million individuals around the world suffer from varying degrees of visual impairment, making it crucial to leverage AI technology to offer walking assistance for these people.With the recent progress of vision-language models (VLMs), applying VLMs to offer walking guidance has bec…
Graph convolutional networks (GCNs) have been widely used and achieved remarkable results in skeleton-based action recognition. In GCNs, graph topology dominates feature aggregation and therefore is the key to extracting representative features. In this work, we propose a novel Channel-wise Topology…
In this paper, we convert traditional video captioning task into a new paradigm, i.e., Open-book Video Captioning, which generates natural language under the prompts of video-content-relevant sentences, not limited to the video itself. To address the open-book video captioning problem, we propose a…