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Found 12 papers, 1 papers with code
Comp4D: LLM-Guided Compositional 4D Scene Generation
Recent advancements in diffusion models for 2D and 3D content creation have sparked a surge of interest in generating 4D content.
PersonMAE: Person Re-Identification Pre-Training with Masked AutoEncoders
Pre-training is playing an increasingly important role in learning generic feature representation for Person Re-identification (ReID).
Sign Language Translation with Iterative Prototype
Technically, IP-SLT consists of feature extraction, prototype initialization, and iterative prototype refinement.
Ranked #5 on
Sign Language Translation
on CSL-Daily
Exploiting Spatial-Temporal Context for Interacting Hand Reconstruction on Monocular RGB Video
Reconstructing interacting hands from monocular RGB data is a challenging task, as it involves many interfering factors, e. g. self- and mutual occlusion and similar textures.
SignBERT+: Hand-model-aware Self-supervised Pre-training for Sign Language Understanding
In our framework, the hand pose is regarded as a visual token, which is derived from an off-the-shelf detector.
Ranked #1 on
Sign Language Recognition
on WLASL
DIRE for Diffusion-Generated Image Detection
We find that existing detectors struggle to detect images generated by diffusion models, even if we include generated images from a specific diffusion model in their training data.
BEST: BERT Pre-Training for Sign Language Recognition with Coupling Tokenization
In this work, we are dedicated to leveraging the BERT pre-training success and modeling the domain-specific statistics to fertilize the sign language recognition~(SLR) model.
Hand-Object Interaction Image Generation
In this work, we are dedicated to a new task, i. e., hand-object interaction image generation, which aims to conditionally generate the hand-object image under the given hand, object and their interaction status.
SignBERT: Pre-Training of Hand-Model-Aware Representation for Sign Language Recognition
To validate the effectiveness of our method on SLR, we perform extensive experiments on four public benchmark datasets, i. e., NMFs-CSL, SLR500, MSASL and WLASL.
Ranked #1 on
Sign Language Recognition
on WLASL100
(using extra training data)
Model-Aware Gesture-to-Gesture Translation
Then, a transformation flow is calculated based on the correspondence of the source and target topology map.
Boosting Continuous Sign Language Recognition via Cross Modality Augmentation
Continuous sign language recognition (SLR) deals with unaligned video-text pair and uses the word error rate (WER), i. e., edit distance, as the main evaluation metric.
Global-local Enhancement Network for NMFs-aware Sign Language Recognition
Sign language recognition (SLR) is a challenging problem, involving complex manual features, i. e., hand gestures, and fine-grained non-manual features (NMFs), i. e., facial expression, mouth shapes, etc.
