Search Results for author:
Found 16 papers, 4 papers with code
IntrinsicAnything: Learning Diffusion Priors for Inverse Rendering Under Unknown Illumination
This paper aims to recover object materials from posed images captured under an unknown static lighting condition.
Dense Training, Sparse Inference: Rethinking Training of Mixture-of-Experts Language Models
Mixture-of-Experts (MoE) language models can reduce computational costs by 2-4$\times$ compared to dense models without sacrificing performance, making them more efficient in computation-bounded scenarios.
EvaSurf: Efficient View-Aware Implicit Textured Surface Reconstruction on Mobile Devices
In our method, we first employ an efficient surface-based model with a multi-view supervision module to ensure accurate mesh reconstruction.
LangNav: Language as a Perceptual Representation for Navigation
We explore the use of language as a perceptual representation for vision-and-language navigation (VLN), with a focus on low-data settings.
HoloAssist: an Egocentric Human Interaction Dataset for Interactive AI Assistants in the Real World
Building an interactive AI assistant that can perceive, reason, and collaborate with humans in the real world has been a long-standing pursuit in the AI community.
Head3D: Complete 3D Head Generation via Tri-plane Feature Distillation
Head generation with diverse identities is an important task in computer vision and computer graphics, widely used in multimedia applications.
Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting
Models are tasked with the prediction of future motion for "scored actors" in each scenario and are provided with track histories that capture object location, heading, velocity, and category.
Towards 3D Face Reconstruction in Perspective Projection: Estimating 6DoF Face Pose from Monocular Image
In 3D face reconstruction, orthogonal projection has been widely employed to substitute perspective projection to simplify the fitting process.
Ranked #3 on
Head Pose Estimation
on BIWI
IA-RED$^2$: Interpretability-Aware Redundancy Reduction for Vision Transformers
The self-attention-based model, transformer, is recently becoming the leading backbone in the field of computer vision.
Ranked #29 on
Efficient ViTs
on ImageNet-1K (with DeiT-S)
Exploring Adversarial Learning for Deep Semi-Supervised Facial Action Unit Recognition
Specifically, the proposed deep semi-supervised AU recognition approach consists of a deep recognition network and a discriminator D. The deep recognition network R learns facial representations from large-scale facial images and AU classifiers from limited ground truth AU labels.
Improved Techniques for Quantizing Deep Networks with Adaptive Bit-Widths
Second, to effectively transfer knowledge, we develop a dynamic block swapping method by randomly replacing the blocks in the lower-precision student network with the corresponding blocks in the higher-precision teacher network.
VA-RED$^2$: Video Adaptive Redundancy Reduction
An inherent property of real-world videos is the high correlation of information across frames which can translate into redundancy in either temporal or spatial feature maps of the models, or both.
Multi-Moments in Time: Learning and Interpreting Models for Multi-Action Video Understanding
Videos capture events that typically contain multiple sequential, and simultaneous, actions even in the span of only a few seconds.
Cross-view Semantic Segmentation for Sensing Surroundings
Our further experiment on a LoCoBot robot shows that our model enables the surrounding sensing capability from 2D image input.
Recurrent Residual Module for Fast Inference in Videos
Deep convolutional neural networks (CNNs) have made impressive progress in many video recognition tasks such as video pose estimation and video object detection.
Deep Facial Action Unit Recognition From Partially Labeled Data
To address this, we propose a deep facial action unit recognition approach learning from partially AU-labeled data.
