Search Results for author:
Found 10 papers, 3 papers with code
Improving Zero-Shot Chinese-English Code-Switching ASR with kNN-CTC and Gated Monolingual Datastores
To address this, we propose a novel kNN-CTC-based code-switching ASR (CS-ASR) framework that employs dual monolingual datastores and a gated datastore selection mechanism to reduce noise interference.
CKGConv: General Graph Convolution with Continuous Kernels
In this work, we propose a novel and general graph convolution framework by parameterizing the kernels as continuous functions of pseudo-coordinates derived via graph positional encoding.
Rethinking CLIP-based Video Learners in Cross-Domain Open-Vocabulary Action Recognition
To answer this, we establish a CROSS-domain Open-Vocabulary Action recognition benchmark named XOV-Action, and conduct a comprehensive evaluation of five state-of-the-art CLIP-based video learners under various types of domain gaps.
ActionHub: A Large-scale Action Video Description Dataset for Zero-shot Action Recognition
With the proposed ActionHub dataset, we further propose a novel Cross-modality and Cross-action Modeling (CoCo) framework for ZSAR, which consists of a Dual Cross-modality Alignment module and a Cross-action Invariance Mining module.
GeoDeformer: Geometric Deformable Transformer for Action Recognition
Specifically, at the core of GeoDeformer is the Geometric Deformation Predictor, a module designed to identify and quantify potential spatial and temporal geometric deformations within the given video.
Towards Weakly Supervised End-to-end Learning for Long-video Action Recognition
To this end, this work aims to build a weakly supervised end-to-end framework for training recognition models on long videos, with only video-level action category labels.
Ranked #1 on
Long-video Activity Recognition
on Breakfast
PostRainBench: A comprehensive benchmark and a new model for precipitation forecasting
To address these limitations, we introduce the PostRainBench, a comprehensive multi-variable NWP post-processing benchmark consisting of three datasets for NWP post-processing-based precipitation forecasting.
CIF-T: A Novel CIF-based Transducer Architecture for Automatic Speech Recognition
RNN-T models are widely used in ASR, which rely on the RNN-T loss to achieve length alignment between input audio and target sequence.
MADI: Inter-domain Matching and Intra-domain Discrimination for Cross-domain Speech Recognition
Unsupervised domain adaptation (UDA) aims to improve the performance on the unlabeled target domain by transferring knowledge from the source to the target domain.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Graph-Based High-Order Relation Modeling for Long-Term Action Recognition
In this paper, we propose a Graph-based High-order Relation Modeling (GHRM) module to exploit the high-order relations in the long-term actions for long-term action recognition.
Ranked #5 on
Video Classification
on Breakfast
