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Found 10 papers, 2 papers with code
Unimodal and Crossmodal Refinement Network for Multimodal Sequence Fusion
Specifically, to improve unimodal representations, a unimodal refinement module is designed to refine modality-specific learning via iteratively updating the distribution with transformer-based attention layers.
Enhancing Lip Reading with Multi-Scale Video and Multi-Encoder
Automatic lip-reading (ALR) aims to automatically transcribe spoken content from a speaker's silent lip motion captured in video.
Exploiting Low-level Representations for Ultra-Fast Road Segmentation
The spatial detail branch is firstly designed to extract low-level feature representation for the road by the first stage of ResNet-18.
X-SepFormer: End-to-end Speaker Extraction Network with Explicit Optimization on Speaker Confusion
To mitigate the imperative SC issue, we reformulate the training objective and propose two novel loss schemes that explore the metric of reconstruction improvement performance defined at small chunk-level and leverage the metric associated distribution information.
Improving Target Speaker Extraction with Sparse LDA-transformed Speaker Embeddings
As a practical alternative of speech separation, target speaker extraction (TSE) aims to extract the speech from the desired speaker using additional speaker cue extracted from the speaker.
CGI-Stereo: Accurate and Real-Time Stereo Matching via Context and Geometry Interaction
In this paper, we propose CGI-Stereo, a novel neural network architecture that can concurrently achieve real-time performance, competitive accuracy, and strong generalization ability.
Joint Speech Activity and Overlap Detection with Multi-Exit Architecture
Overlapped speech detection (OSD) is critical for speech applications in scenario of multi-party conversion.
Speech Enhancement with Perceptually-motivated Optimization and Dual Transformations
To validate the effectiveness of our proposed model, extensive experiments are conducted on the DNS2020 dataset.
Breast Cancer Molecular Subtypes Prediction on Pathological Images with Discriminative Patch Selecting and Multi-Instance Learning
A weakly supervised learning framework based on discriminative patch selecting and multi-instance learning was proposed for breast cancer molecular subtype prediction from H&E WSIs.
Container Orchestration on HPC Systems
Containerisation demonstrates its efficiency in application deployment in cloud computing.
Distributed, Parallel, and Cluster Computing
