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Found 18 papers, 11 papers with code
Hierarchical Representation-based Dynamic Reasoning Network for Biomedical Question Answering
It then performs dynamic reasoning based on the hierarchical representations of evidences to solve complex biomedical problems.
Dynamic Multistep Reasoning based on Video Scene Graph for Video Question Answering
Then, it performs multistep reasoning for better answer decision between the representations of the question and the video, and dynamically integrate the reasoning results.
A Multimodal Object-level Contrast Learning Method for Cancer Survival Risk Prediction
In this paper, we propose a new training method, multimodal object-level contrast learning, for cancer survival risk prediction.
Advancing Multi-grained Alignment for Contrastive Language-Audio Pre-training
Recent advances have been witnessed in audio-language joint learning, such as CLAP, that shows much success in multi-modal understanding tasks.
SCMIL: Sparse Context-aware Multiple Instance Learning for Predicting Cancer Survival Probability Distribution in Whole Slide Images
Cancer survival prediction is a challenging task that involves analyzing of the tumor microenvironment within Whole Slide Image (WSI).
Audio-free Prompt Tuning for Language-Audio Models
Contrastive Language-Audio Pretraining (CLAP) is pre-trained to associate audio features with human language, making it a natural zero-shot classifier to recognize unseen sound categories.
Semi-supervised Sound Event Detection with Local and Global Consistency Regularization
Then, the local consistency is adopted to encourage the model to leverage local features for frame-level predictions, and the global consistency is applied to force features to align with global prototypes through a specially designed contrastive loss.
Audio Generation with Multiple Conditional Diffusion Model
To address this issue, we propose a novel model that enhances the controllability of existing pre-trained text-to-audio models by incorporating additional conditions including content (timestamp) and style (pitch contour and energy contour) as supplements to the text.
Leveraging Language Model Capabilities for Sound Event Detection
Large language models reveal deep comprehension and fluent generation in the field of multi-modality.
Compact Twice Fusion Network for Edge Detection
In this work, we propose a Compact Twice Fusion Network (CTFN) to fully integrate multi-scale features while maintaining the compactness of the model.
A Hybrid System of Sound Event Detection Transformer and Frame-wise Model for DCASE 2022 Task 4
For the frame-wise model, the ICT-TOSHIBA system of DCASE 2021 Task 4 is used.
Couple Learning for semi-supervised sound event detection
The recently proposed Mean Teacher method, which exploits large-scale unlabeled data in a self-ensembling manner, has achieved state-of-the-art results in several semi-supervised learning benchmarks.
Sound Event Detection Transformer: An Event-based End-to-End Model for Sound Event Detection
A critical issue with the frame-based model is that it pursues the best frame-level prediction rather than the best event-level prediction.
An End-to-end Approach for Lexical Stress Detection based on Transformer
For this, we train transformer model using feature sequence of audio and their phoneme sequence with lexical stress marks.
Guided Learning Convolution System for DCASE 2019 Task 4
In this paper, we describe in detail the system we submitted to DCASE2019 task 4: sound event detection (SED) in domestic environments.
Guided learning for weakly-labeled semi-supervised sound event detection
Instead of designing a single model by considering a trade-off between the two sub-targets, we design a teacher model aiming at audio tagging to guide a student model aiming at boundary detection to learn using the unlabeled data.
Specialized Decision Surface and Disentangled Feature for Weakly-Supervised Polyphonic Sound Event Detection
In this paper, a special decision surface for the weakly-supervised sound event detection (SED) and a disentangled feature (DF) for the multi-label problem in polyphonic SED are proposed.
Action Recognition Based on Optimal Joint Selection and Discriminative Depth Descriptor
This paper proposes a novel human action recognition using the decision-level fusion of both skeleton and depth sequence.
