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Found 19 papers, 11 papers with code
LucidDreamer: Towards High-Fidelity Text-to-3D Generation via Interval Score Matching
The recent advancements in text-to-3D generation mark a significant milestone in generative models, unlocking new possibilities for creating imaginative 3D assets across various real-world scenarios.
RC-MVSNet: Unsupervised Multi-View Stereo with Neural Rendering
Finding accurate correspondences among different views is the Achilles' heel of unsupervised Multi-View Stereo (MVS).
Towards Generalizable Multi-Camera 3D Object Detection via Perspective Debiasing
In our approach, we render diverse view maps from BEV features and rectify the perspective bias of these maps, leveraging implicit foreground volumes to bridge the camera and BEV planes.
Scaling Multi-Camera 3D Object Detection through Weak-to-Strong Eliciting
Finally, for MC3D-Det joint training, the elaborate dataset merge strategy is designed to solve the problem of inconsistent camera numbers and camera parameters.
Resolve Domain Conflicts for Generalizable Remote Physiological Measurement
Remote photoplethysmography (rPPG) technology has become increasingly popular due to its non-invasive monitoring of various physiological indicators, making it widely applicable in multimedia interaction, healthcare, and emotion analysis.
DecoupleNet: Decoupled Network for Domain Adaptive Semantic Segmentation
Unsupervised domain adaptation in semantic segmentation has been raised to alleviate the reliance on expensive pixel-wise annotations.
Denoising Diffusion Step-aware Models
Denoising Diffusion Probabilistic Models (DDPMs) have garnered popularity for data generation across various domains.
Neuron Structure Modeling for Generalizable Remote Physiological Measurement
We show that most domain generalization methods do not work well in this problem, as domain labels are ambiguous in complicated environmental changes.
Out-of-domain GAN inversion via Invertibility Decomposition for Photo-Realistic Human Face Manipulation
In this paper, we propose a novel framework that enhances the fidelity of human face inversion by designing a new module to decompose the input images to ID and OOD partitions with invertibility masks.
GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective Computing
In conclusion, this paper provides valuable insights into the potential applications and challenges of MLLMs in human-centric computing.
Advancing Generalizable Remote Physiological Measurement through the Integration of Explicit and Implicit Prior Knowledge
Remote photoplethysmography (rPPG) is a promising technology that captures physiological signals from face videos, with potential applications in medical health, emotional computing, and biosecurity recognition.
PointINS: Point-based Instance Segmentation
In this paper, we explore the mask representation in instance segmentation with Point-of-Interest (PoI) features.
Technology Report : Smartphone-Based Pedestrian Dead Reckoning Integrated with Data-Fusion-Adopted Visible Light Positioning
Pedestrian dead-reckoning (PDR) is a potential indoor localization technology that obtains location estimation with the inertial measurement unit (IMU).
Label Name is Mantra: Unifying Point Cloud Segmentation across Heterogeneous Datasets
In this paper, we propose a principled approach that supports learning from heterogeneous datasets with different label sets.
Addressing Variable Dependency in GNN-based SAT Solving
Boolean satisfiability problem (SAT) is fundamental to many applications.
Defect Spectrum: A Granular Look of Large-Scale Defect Datasets with Rich Semantics
Defect inspection is paramount within the closed-loop manufacturing system.
Self-similarity Prior Distillation for Unsupervised Remote Physiological Measurement
In this paper, we propose a Self-Similarity Prior Distillation (SSPD) framework for unsupervised rPPG estimation, which capitalizes on the intrinsic self-similarity of cardiac activities.
Motion Inversion for Video Customization
In this research, we present a novel approach to motion customization in video generation, addressing the widespread gap in the thorough exploration of motion representation within video generative models.
Towards Faster Training of Diffusion Models: An Inspiration of A Consistency Phenomenon
In this paper, we start with a consistency phenomenon of DMs: we observe that DMs with different initializations or even different architectures can produce very similar outputs given the same noise inputs, which is rare in other generative models.
