9 accepted papers
Although existing backdoor defenses have gained success in mitigating backdoor attacks, they still face substantial challenges. In particular, most of them rely on large amounts of clean data to weaken the backdoor mapping but generally struggle with residual trigger effects, resulting in persistent
Diffusion models have revolutionized image synthesis but raise concerns around content provenance and authenticity. Digital watermarking offers a means of tracing generated media, yet traditional schemes often introduce distributional shifts and degrade visual quality. Recent lossless methods embed…
The growing popularity of large language models has raised concerns regarding the potential to misuse AI-generated text (AIGT). It becomes increasingly critical to establish an excellent AIGT detection method with high generalization and robustness.While, existing methods either focus on model gener…
The proliferation of digital images on the Internet has provided unprecedented convenience, but also poses significant risks of malicious theft and misuse. Digital watermarking has long been researched as an effective tool for copyright protection. However, it often falls short when addressing parti…
To assist or replace human beings in completing various tasks, research on the functional grasp synthesis of dexterous hands with high degree-of-freedom (DoF) is necessary and challenging. The dexterous functional grasp requires not only that the grasp is stable but more importantly facilitates the
Successful grasp is an important and long-standing issue for robots to interact with the real world. Most recent studies have devoted more attention to stable grasp rather than functional grasp, which cannot guarantee task-oriented postgrasp manipulation. To achieve human-like functional grasp, a se
Category-level 6D pose estimation can be better generalized to unseen objects in a category compared with instance-level 6D pose estimation. However, existing category-level 6D pose estimation methods usually require supervised training with a sufficient number of 6D pose annotations of objects whic…
"Grasp pose estimation is an important issue for robots to interact with the real world. However, most of existing methods require exact 3D object models available beforehand or a large amount of grasp annotations for training. To avoid these problems, we propose TransGrasp, a category-level grasp p…
3D object detection is an important scene understanding task in autonomous driving and virtual reality. Approaches based on LiDAR technology have high performance, but LiDAR is expensive. Considering more general scenes, where there is no LiDAR data in the 3D datasets, we propose a 3D object detecti…