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Found 15 papers, 6 papers with code
CFPL-FAS: Class Free Prompt Learning for Generalizable Face Anti-spoofing
Specifically, we propose a novel Class Free Prompt Learning (CFPL) paradigm for DG FAS, which utilizes two lightweight transformers, namely Content Q-Former (CQF) and Style Q-Former (SQF), to learn the different semantic prompts conditioned on content and style features by using a set of learnable query vectors, respectively.
Distilling Temporal Knowledge with Masked Feature Reconstruction for 3D Object Detection
Ultimately, we utilize this reconstruction target to reconstruct the student features.
PMMTalk: Speech-Driven 3D Facial Animation from Complementary Pseudo Multi-modal Features
The framework entails three modules: PMMTalk encoder, cross-modal alignment module, and PMMTalk decoder.
Long-Range Grouping Transformer for Multi-View 3D Reconstruction
The tokens in each group are sampled from all views and can provide macro representation for the resided view.
Ranked #1 on
3D Object Reconstruction
on Data3D−R2N2
Gloss-free Sign Language Translation: Improving from Visual-Language Pretraining
Many previous methods employ an intermediate representation, i. e., gloss sequences, to facilitate SLT, thus transforming it into a two-stage task of sign language recognition (SLR) followed by sign language translation (SLT).
Ranked #2 on
Gloss-free Sign Language Translation
on PHOENIX14T
Gloss-free Sign Language Translation
Self-Supervised Learning
+3
FM-ViT: Flexible Modal Vision Transformers for Face Anti-Spoofing
The availability of handy multi-modal (i. e., RGB-D) sensors has brought about a surge of face anti-spoofing research.
MA-ViT: Modality-Agnostic Vision Transformers for Face Anti-Spoofing
The existing multi-modal face anti-spoofing (FAS) frameworks are designed based on two strategies: halfway and late fusion.
UMIFormer: Mining the Correlations between Similar Tokens for Multi-View 3D Reconstruction
We empirically demonstrate on ShapeNet and confirm that our decoupled learning method is adaptable for unstructured multiple images.
Ranked #2 on
3D Object Reconstruction
on Data3D−R2N2
A Unified Multimodal De- and Re-coupling Framework for RGB-D Motion Recognition
Although improving motion recognition to some extent, these methods still face sub-optimal situations in the following aspects: (i) Data augmentation, i. e., the scale of the RGB-D datasets is still limited, and few efforts have been made to explore novel data augmentation strategies for videos; (ii) Optimization mechanism, i. e., the tightly space-time-entangled network structure brings more challenges to spatiotemporal information modeling; And (iii) cross-modal knowledge fusion, i. e., the high similarity between multimodal representations caused to insufficient late fusion.
Ranked #3 on
Action Recognition
on NTU RGB+D
GARNet: Global-Aware Multi-View 3D Reconstruction Network and the Cost-Performance Tradeoff
Deep learning technology has made great progress in multi-view 3D reconstruction tasks.
Effective Vision Transformer Training: A Data-Centric Perspective
To achieve these two purposes, we propose a novel data-centric ViT training framework to dynamically measure the ``difficulty'' of training samples and generate ``effective'' samples for models at different training stages.
Decoupling and Recoupling Spatiotemporal Representation for RGB-D-based Motion Recognition
Decoupling spatiotemporal representation refers to decomposing the spatial and temporal features into dimension-independent factors.
Ranked #1 on
Hand Gesture Recognition
on NVGesture
Contrastive Context-Aware Learning for 3D High-Fidelity Mask Face Presentation Attack Detection
To bridge the gap to real-world applications, we introduce a largescale High-Fidelity Mask dataset, namely CASIA-SURF HiFiMask (briefly HiFiMask).
MxPool: Multiplex Pooling for Hierarchical Graph Representation Learning
This motivates us to use a multiplex structure in a diverse way and utilize a priori properties of graphs to guide the learning.
CASIA-SURF: A Large-scale Multi-modal Benchmark for Face Anti-spoofing
To facilitate face anti-spoofing research, we introduce a large-scale multi-modal dataset, namely CASIA-SURF, which is the largest publicly available dataset for face anti-spoofing in terms of both subjects and modalities.
