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Found 15 papers, 5 papers with code
Enhancing Video Transformers for Action Understanding with VLM-aided Training
The FTP framework adds four feature processors that focus on specific aspects of human action in videos: action category, action components, action description, and context information.
MSLM-S2ST: A Multitask Speech Language Model for Textless Speech-to-Speech Translation with Speaker Style Preservation
There have been emerging research interest and advances in speech-to-speech translation (S2ST), translating utterances from one language to another.
An Empirical Study of Speech Language Models for Prompt-Conditioned Speech Synthesis
Speech language models (LMs) are promising for high-quality speech synthesis through in-context learning.
TCNet: Continuous Sign Language Recognition from Trajectories and Correlated Regions
A key challenge in continuous sign language recognition (CSLR) is to efficiently capture long-range spatial interactions over time from the video input.
Ranked #3 on
Sign Language Recognition
on CSL-Daily
Olapa-MCoT: Enhancing the Chinese Mathematical Reasoning Capability of LLMs
CoT (Chain-of-Thought) is a way to solve reasoning problems for LLMs .
Compensation Sampling for Improved Convergence in Diffusion Models
We argue that the denoising process is crucially limited by an accumulation of the reconstruction error due to an initial inaccurate reconstruction of the target data.
Ranked #16 on
Image Generation
on CIFAR-10
ASPEN: High-Throughput LoRA Fine-Tuning of Large Language Models with a Single GPU
Transformer-based large language models (LLMs) have demonstrated outstanding performance across diverse domains, particularly when fine-turned for specific domains.
Heuristic-Driven Link-of-Analogy Prompting: Enhancing Large Language Models for Document-Level Event Argument Extraction
In this study, we investigate in-context learning (ICL) in document-level event argument extraction (EAE) to alleviate the dependency on large-scale labeled data for this task.
Efficient Temporal Sentence Grounding in Videos with Multi-Teacher Knowledge Distillation
To tackle this problem, we propose a novel efficient multi-teacher model (EMTM) based on knowledge distillation to transfer diverse knowledge from both heterogeneous and isomorphic networks.
Private Multiparty Perception for Navigation
We introduce a framework for navigating through cluttered environments by connecting multiple cameras together while simultaneously preserving privacy.
Towards High-Quality Neural TTS for Low-Resource Languages by Learning Compact Speech Representations
Moreover, we optimize the training strategy by leveraging more audio to learn MSMCRs better for low-resource languages.
Disentangled Speech Representation Learning for One-Shot Cross-lingual Voice Conversion Using $β$-VAE
We propose an unsupervised learning method to disentangle speech into content representation and speaker identity representation.
Speaker Identity Preservation in Dysarthric Speech Reconstruction by Adversarial Speaker Adaptation
The primary task of ASA fine-tunes the SE with the speech of the target dysarthric speaker to effectively capture identity-related information, and the secondary task applies adversarial training to avoid the incorporation of abnormal speaking patterns into the reconstructed speech, by regularizing the distribution of reconstructed speech to be close to that of reference speech with high quality.
Stabilized Likelihood-based Imitation Learning via Denoising Continuous Normalizing Flow
State-of-the-art imitation learning (IL) approaches, e. g, GAIL, apply adversarial training to minimize the discrepancy between expert and learner behaviors, which is prone to unstable training and mode collapse.
Channel-wise Gated Res2Net: Towards Robust Detection of Synthetic Speech Attacks
This argument motivates the current work that presents a novel, channel-wise gated Res2Net (CG-Res2Net), which modifies Res2Net to enable a channel-wise gating mechanism in the connection between feature groups.
