Search Results for author:
Found 12 papers, 8 papers with code
Towards Effective Collaborative Learning in Long-Tailed Recognition
In this paper, we observe that the knowledge transfer between experts is imbalanced in terms of class distribution, which results in limited performance improvement of the minority classes.
HiFace: High-Fidelity 3D Face Reconstruction by Learning Static and Dynamic Details
3D Morphable Models (3DMMs) demonstrate great potential for reconstructing faithful and animatable 3D facial surfaces from a single image.
Ranked #1 on
3D Face Reconstruction
on REALY (side-view)
SEAM: Searching Transferable Mixed-Precision Quantization Policy through Large Margin Regularization
This general and dataset-independent property makes us search for the MPQ policy over a rather small-scale proxy dataset and then the policy can be directly used to quantize the model trained on a large-scale dataset.
ERA-Solver: Error-Robust Adams Solver for Fast Sampling of Diffusion Probabilistic Models
Different from the traditional predictor based on explicit Adams methods, we leverage a Lagrange interpolation function as the predictor, which is further enhanced with an error-robust strategy to adaptively select the Lagrange bases with lower error in the estimated noise.
Learning Imbalanced Data with Vision Transformers
In this paper, we systematically investigate the ViTs' performance in LTR and propose LiVT to train ViTs from scratch only with LT data.
Ranked #7 on
Long-tail Learning
on CIFAR-10-LT (ρ=10)
HyP$^2$ Loss: Beyond Hypersphere Metric Space for Multi-label Image Retrieval
Image retrieval has become an increasingly appealing technique with broad multimedia application prospects, where deep hashing serves as the dominant branch towards low storage and efficient retrieval.
REALY: Rethinking the Evaluation of 3D Face Reconstruction
The evaluation of 3D face reconstruction results typically relies on a rigid shape alignment between the estimated 3D model and the ground-truth scan.
HHF: Hashing-guided Hinge Function for Deep Hashing Retrieval
Deep hashing has shown promising performance in large-scale image retrieval.
Semantic-Sparse Colorization Network for Deep Exemplar-based Colorization
To address these two problems, we propose Semantic-Sparse Colorization Network (SSCN) to transfer both the global image style and detailed semantic-related colors to the gray-scale image in a coarse-to-fine manner.
Towards Calibrated Model for Long-Tailed Visual Recognition from Prior Perspective
Real-world data universally confronts a severe class-imbalance problem and exhibits a long-tailed distribution, i. e., most labels are associated with limited instances.
Ranked #24 on
Long-tail Learning
on CIFAR-100-LT (ρ=10)
MoDeRNN: Towards Fine-grained Motion Details for Spatiotemporal Predictive Learning
We carefully design Detail Context Block (DCB) to extract fine-grained details and improve the isolated correlation between upper context state and current input state.
CMS-LSTM: Context Embedding and Multi-Scale Spatiotemporal Expression LSTM for Predictive Learning
To tackle the increasing ambiguity during forecasting, we design CMS-LSTM to focus on context correlations and multi-scale spatiotemporal flow with details on fine-grained locals, containing two elaborate designed blocks: Context Embedding (CE) and Spatiotemporal Expression (SE) blocks.
