Search Results for author:
Found 11 papers, 6 papers with code
Factorized Learning Assisted with Large Language Model for Gloss-free Sign Language Translation
Although some approaches work towards gloss-free SLT through jointly training the visual encoder and translation network, these efforts still suffer from poor performance and inefficient use of the powerful Large Language Model (LLM).
Ranked #1 on
Gloss-free Sign Language Translation
on CSL-Daily
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.
Multi-stage Factorized Spatio-Temporal Representation for RGB-D Action and Gesture Recognition
RGB-D action and gesture recognition remain an interesting topic in human-centered scene understanding, primarily due to the multiple granularities and large variation in human motion.
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
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
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
Regional Attention with Architecture-Rebuilt 3D Network for RGB-D Gesture Recognition
Meanwhile, a more adaptive architecture-searched network structure can also perform better than the block-fixed ones like Resnet since it increases the diversity of features in different stages of the network better.
DSAM: A Distance Shrinking with Angular Marginalizing Loss for High Performance Vehicle Re-identificatio
To obtain a high-performance vehicle ReID model, we present a novel Distance Shrinking with Angular Marginalizing (DSAM) loss function to perform hybrid learning in both the Original Feature Space (OFS) and the Feature Angular Space (FAS) using the local verification and the global identification information.
Searching Multi-Rate and Multi-Modal Temporal Enhanced Networks for Gesture Recognition
Gesture recognition has attracted considerable attention owing to its great potential in applications.
Cross-ethnicity Face Anti-spoofing Recognition Challenge: A Review
Although ethnic bias has been verified to severely affect the performance of face recognition systems, it still remains an open research problem in face anti-spoofing.
