6 accepted papers
To ensure the robustness of 3D point cloud Deep Neural Network(3D DNN), 3D adversarial attack targeting the inference stage and backdoor attack targeting the training stage are well studied. The success of both attacks usually requires a specified permissions that attacker must have. However, the ob
We present Compound Conditioned ControlNet C3Net a novel generative neural architecture taking conditions from multiple modalities and synthesizing multimodal contents simultaneously (e.g. image text audio). C3Net adapts the ControlNet architecture to jointly train and make inferences on a productio…
We present the content deformation field (CoDeF) as a new type of video representation which consists of a canonical content field aggregating the static contents in the entire video and a temporal deformation field recording the transformations from the canonical image (i.e. rendered from the canon…
This work proposes the quantum-state-based mapping (QSM) for machine learning. QSM uses wave functions that describe microscopic particle systems as mappings. By QSM, original inputs or features extracted by neural networks are processed as quantum states to train wave function parameters. QSM has a…
It is a common paradigm in object detection frameworks to perform multi-scale detection. However, each scale is treated equally during training. In this paper, we carefully study the objective imbalance of multi-scale detector training. We argue that the loss in each scale is neither equally importa…
Feature Pyramid Network (FPN) has become a common detection paradigm by improving multi-scale features with strong semantics. However, most FPN-based methods typically treat each feature map equally and sum the loss without distinction, which might lead to suboptimal overall performance. In this pap…