11 accepted papers
3D part amodal segmentation--decomposing a 3D shape into complete, semantically meaningful parts, even when occluded--is a challenging but crucial task for 3D content creation and understanding. Existing 3D part segmentation methods only identify visible surface patches, limiting their utility. Insp…
Dexterous grasp generation is a predominant task that enables robots to perform human-level manipulation. However, a dexterous hand always maintains high-dimensional DoF and actuation space, making existing approaches that rely on holistic latent representations difficult to produce high-quality and
The pre-trained models contain multitudinous knowledge from huge amount of data. However, when applying these models to downstream tasks, they may mis-locate to wrong knowledge distribution due to a lack of domain or contextual knowledge. To address the distribution bias between the pre-trained mode…
Recent advances in cross-technology communication (CTC) enable heterogeneous wireless devices (e.g., WiFi, Zig-Bee, and BLE) operating in the ISM band to communicate and understand each other. However, due to the limitation of standards and devices, existing CTC techniques need to design specific sc…
Recent advancements in 3D reconstruction from single images have been driven by the evolution of generative models. Prominent among these are methods based on Score Distillation Sampling (SDS) and the adaptation of diffusion models in the 3D domain. Despite their progress these techniques often face…
Recent advances in 3D point cloud completion adopt unsupervised deep learning-based methods, which does not rely on labeled data and improves generalization ability. However, existing methods tend to focus more on the generation overall shape rather than detailed structure. To explore unsupervised 3…
Knowledge distillation is an effective model compression method to improve the performance of a lightweight student model by transferring the knowledge of a well-performed teacher model, which has been widely adopted in many computer vision tasks, including face recognition (FR). The current FR dist…
"Face recognition (FR) has witnessed remarkable progress with the surge of deep learning. Current FR evaluation protocols usually adopt different thresholds to calculate the True Accept Rate (TAR) under a pre-defined False Accept Rate (FAR) for different datasets. How- ever, in practice, when the FR…
To simultaneously translate a source language into multiple different target languages is one of the most common scenarios of multilingual translation. However, existing methods cannot make full use of translation model information during decoding, such as intra-lingual and inter-lingual future info…
This work presents an efficient yet effective online Knowledge Distillation method via Collaborative Learning, termed KDCL, which is able to consistently improve the generalization ability of deep neural networks (DNNs) that have different learning capacities. Unlike existing two-stage knowledge dis…
This paper proposes the dynamic recursive neural network (DRNN), which simplifies the duplicated building blocks in deep neural network. Different from forwarding through different blocks sequentially in previous networks, we demonstrate that the DRNN can achieve better performance with fewer blocks…