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Found 18 papers, 6 papers with code
Deconfounded Reasoning for Multimodal Fake News Detection via Causal Intervention
The rapid growth of social media has led to the widespread dissemination of fake news across multiple content forms, including text, images, audio, and video.
Exploring Modality Disruption in Multimodal Fake News Detection
Compared to unimodal fake news detection, multimodal fake news detection benefits from the increased availability of information across multiple modalities.
Lifelong Learning of Large Language Model based Agents: A Roadmap
This survey is the first to systematically summarize the potential techniques for incorporating lifelong learning into LLM-based agents.
Neural Codec Source Tracing: Toward Comprehensive Attribution in Open-Set Condition
Current research in audio deepfake detection is gradually transitioning from binary classification to multi-class tasks, referred as audio deepfake source tracing task.
LetsTalk: Latent Diffusion Transformer for Talking Video Synthesis
Then we propose a suitable solution according to the modality differences of image, audio, and video generation.
Federated Incremental Named Entity Recognition
In a more realistic scenario, local clients receive new entity types continuously, while new local clients collecting novel data may irregularly join the global FNER training.
Video-to-Audio Generation with Fine-grained Temporal Semantics
With recent advances of AIGC, video generation have gained a surge of research interest in both academia and industry (e. g., Sora).
DPI-TTS: Directional Patch Interaction for Fast-Converging and Style Temporal Modeling in Text-to-Speech
In recent years, speech diffusion models have advanced rapidly.
EzAudio: Enhancing Text-to-Audio Generation with Efficient Diffusion Transformer
Latent diffusion models have shown promising results in text-to-audio (T2A) generation tasks, yet previous models have encountered difficulties in generation quality, computational cost, diffusion sampling, and data preparation.
Towards Diverse and Efficient Audio Captioning via Diffusion Models
We introduce Diffusion-based Audio Captioning (DAC), a non-autoregressive diffusion model tailored for diverse and efficient audio captioning.
Text Prompt is Not Enough: Sound Event Enhanced Prompt Adapter for Target Style Audio Generation
Additionally, the Sound Event Reference Style Transfer Dataset (SERST) is introduced for the proposed target style audio generation task, enabling dual-prompt audio generation using both text and audio references.
Video-to-Audio Generation with Hidden Alignment
In this work, we aim to offer insights into the video-to-audio generation paradigm, focusing on three crucial aspects: vision encoders, auxiliary embeddings, and data augmentation techniques.
HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Model
Accurate hyperspectral image (HSI) interpretation is critical for providing valuable insights into various earth observation-related applications such as urban planning, precision agriculture, and environmental monitoring.
Prompt-guided Precise Audio Editing with Diffusion Models
Audio editing involves the arbitrary manipulation of audio content through precise control.
MM-LLMs: Recent Advances in MultiModal Large Language Models
In the past year, MultiModal Large Language Models (MM-LLMs) have undergone substantial advancements, augmenting off-the-shelf LLMs to support MM inputs or outputs via cost-effective training strategies.
Multi-Task Audio Source Separation
In detail, the proposed model follows a two-stage pipeline, which separates the three types of audio signals and then performs signal compensation separately.
Ranked #1 on
Multi-task Audio Source Seperation
on MTASS
Audio Source Separation
Multi-task Audio Source Seperation
+3
Single-channel Speech Dereverberation via Generative Adversarial Training
In this paper, we propose a single-channel speech dereverberation system (DeReGAT) based on convolutional, bidirectional long short-term memory and deep feed-forward neural network (CBLDNN) with generative adversarial training (GAT).
Scaling Nakamoto Consensus to Thousands of Transactions per Second
The Conflux consensus protocol represents relationships between blocks as a direct acyclic graph and achieves consensus on a total order of the blocks.
Distributed, Parallel, and Cluster Computing
