10 accepted papers
Multi-agent systems (MAS) powered by large language models (LLMs) have shown potential in tackling multifaceted problems through advanced understanding and reasoning. However, they struggle to adapt to evolving task dependencies and to handle uncertainties, such as shifting priorities or unpredictab
Spatio-temporal consistency is a critical topic in video generation. A qualified generated video segment must ensure plot plausibility and coherence while maintaining visual consistency of objects and scenes across varying viewpoints. Prior research, especially in open-source projects, primarily foc…
Roadside vision-based 3D object detection is vital in many applications, such as autonomous driving. The mainstream methods enhance the accuracy of distance estimation by converting predicted height distribution into depth distribution. However, predicting object’s height in roadside perception is c…
We propose a multimodal 3D scene editing framework MMEditor to create or modify objects within an extant 3D Gaussian Splatting (3DGS) according to text and image prompts. MMEditor employs a multimodal image editing module to iteratively optimize 3D Gaussians in editing regions for delicate and multi…
Existing change detection (CD) methods often directly fuse the multi-level features from bi-temporal remote sensing images without discriminatively considering each pixel's importance. Despite the demonstrated success, unselectively mixing the features degrades the model's performance to effectively…
The emergence of ChatGPT has once again sparked research in generative artificial intelligence (GAI). While people have been amazed by the generated results, they have also noticed the reasoning potential reflected in the generated textual content. However, this current ability for causal reasoning…
Existing video multi-modal sentiment analysis mainly focuses on the sentiment expression of people within the video, yet often neglects the induced sentiment of viewers while watching the videos. Induced sentiment of viewers is essential for inferring the public response to videos and has broad appl…
Existing deep learning based remote sensing change detection (RSCD) methods only rely on binary ground-truth to guide the network learning while neglecting the useful semantic guidance. As a result, the network can be readily misled by irrelevant category changes, leading to degraded performance and…
It has been found that many real networks, such as power grids and the Internet, are non-robust, i.e., attacking a small set of nodes would cause the paralysis of the entire network. Thus, the Network Enhancement Problem~(NEP), i.e., improving the robustness of a given network by modifying its struc…
Co-salient object detection (CoSOD) aims to discover and segment foreground targets in a group of images with the same semantic category. Existing mainstream approaches often employ convolutional neural networks (CNNs) to learn the semantic-invariant features from a group of images. Despite demonstr…