Search Results for author:
Found 15 papers, 6 papers with code
Prior-free Balanced Replay: Uncertainty-guided Reservoir Sampling for Long-Tailed Continual Learning
Even in the era of large models, one of the well-known issues in continual learning (CL) is catastrophic forgetting, which is significantly challenging when the continual data stream exhibits a long-tailed distribution, termed as Long-Tailed Continual Learning (LTCL).
TRRG: Towards Truthful Radiology Report Generation With Cross-modal Disease Clue Enhanced Large Language Model
This significantly enhances the report generation capability and clinical effectiveness of multi-modal large language models in the field of radiology reportgeneration.
DAAD: Dynamic Analysis and Adaptive Discriminator for Fake News Detection
In current web environment, fake news spreads rapidly across online social networks, posing serious threats to society.
Segment Anything for Videos: A Systematic Survey
To address this gap, this work conducts a systematic review on SAM for videos in the era of foundation models.
FusionMamba: Dynamic Feature Enhancement for Multimodal Image Fusion with Mamba
In this paper, we propose FusionMamba, a novel dynamic feature enhancement method for multimodal image fusion with Mamba.
Animal3D: A Comprehensive Dataset of 3D Animal Pose and Shape
Animal3D consists of 3379 images collected from 40 mammal species, high-quality annotations of 26 keypoints, and importantly the pose and shape parameters of the SMAL model.
Ranked #1 on
Animal Pose Estimation
on Animal3D
Hyperbolic Face Anti-Spoofing
To further improve generalization, we conduct hyperbolic contrastive learning for the bonafide only while relaxing the constraints on diverse spoofing attacks.
Visual Prompt Flexible-Modal Face Anti-Spoofing
Recently, vision transformer based multimodal learning methods have been proposed to improve the robustness of face anti-spoofing (FAS) systems.
A Comprehensive Survey on Segment Anything Model for Vision and Beyond
As the first to comprehensively review the progress of segmenting anything task for vision and beyond based on the foundation model of SAM, this work focuses on its applications to various tasks and data types by discussing its historical development, recent progress, and profound impact on broad applications.
Rehearsal-Free Domain Continual Face Anti-Spoofing: Generalize More and Forget Less
To alleviate the forgetting of previous domains without using previous data, we propose the Proxy Prototype Contrastive Regularization (PPCR) to constrain the continual learning with previous domain knowledge from the proxy prototypes.
Generalized Few-Shot Continual Learning with Contrastive Mixture of Adapters
The goal of Few-Shot Continual Learning (FSCL) is to incrementally learn novel tasks with limited labeled samples and preserve previous capabilities simultaneously, while current FSCL methods are all for the class-incremental purpose.
Rethinking Vision Transformer and Masked Autoencoder in Multimodal Face Anti-Spoofing
In this paper, we investigate three key factors (i. e., inputs, pre-training, and finetuning) in ViT for multimodal FAS with RGB, Infrared (IR), and Depth.
PhysFormer++: Facial Video-based Physiological Measurement with SlowFast Temporal Difference Transformer
As key modules in PhysFormer, the temporal difference transformers first enhance the quasi-periodic rPPG features with temporal difference guided global attention, and then refine the local spatio-temporal representation against interference.
Uncertainty-Aware Distillation for Semi-Supervised Few-Shot Class-Incremental Learning
Given a model well-trained with a large-scale base dataset, Few-Shot Class-Incremental Learning (FSCIL) aims at incrementally learning novel classes from a few labeled samples by avoiding overfitting, without catastrophically forgetting all encountered classes previously.
class-incremental learning
Few-Shot Class-Incremental Learning
+2
Rethinking Few-Shot Class-Incremental Learning with Open-Set Hypothesis in Hyperbolic Geometry
Few-Shot Class-Incremental Learning (FSCIL) aims at incrementally learning novel classes from a few labeled samples by avoiding the overfitting and catastrophic forgetting simultaneously.
class-incremental learning
Few-Shot Class-Incremental Learning
+3
