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Found 10 papers, 4 papers with code
WavTokenizer: an Efficient Acoustic Discrete Codec Tokenizer for Audio Language Modeling
Despite the reduced number of tokens, WavTokenizer achieves state-of-the-art reconstruction quality with outstanding UTMOS scores and inherently contains richer semantic information.
RIMformer: An End-to-End Transformer for FMCW Radar Interference Mitigation
Frequency-modulated continuous-wave (FMCW) radar plays a pivotal role in the field of remote sensing.
OmniBind: Large-scale Omni Multimodal Representation via Binding Spaces
Given the foundational role of multimodal joint representation in understanding and generation pipelines, high-quality omni joint representations would be a step toward co-processing more diverse multimodal information.
FreeBind: Free Lunch in Unified Multimodal Space via Knowledge Fusion
In this work, we propose FreeBind, an idea that treats multimodal representation spaces as basic units, and freely augments pre-trained unified space by integrating knowledge from extra expert spaces via "space bonds".
ProMamba: Prompt-Mamba for polyp segmentation
To address these issues, we propose a segmentation model based on Prompt-Mamba, which incorporates the latest Vision-Mamba and prompt technologies.
A Segmentation Foundation Model for Diverse-type Tumors
Large pre-trained models with their numerous model parameters and extensive training datasets have shown excellent performance in various tasks.
Zero-knowledge Proof Meets Machine Learning in Verifiability: A Survey
However, in practice, due to various challenges such as limited computational resources and data privacy concerns, users in need of models often cannot train machine learning models locally.
Extending Multi-modal Contrastive Representations
Inspired by recent C-MCR, this paper proposes Extending Multimodal Contrastive Representation (Ex-MCR), a training-efficient and paired-data-free method to flexibly learn unified contrastive representation space for more than three modalities by integrating the knowledge of existing MCR spaces.
Chat-3D: Data-efficiently Tuning Large Language Model for Universal Dialogue of 3D Scenes
This paper presents Chat-3D, which combines the 3D visual perceptual ability of pre-trained 3D representations and the impressive reasoning and conversation capabilities of advanced LLMs to achieve the first universal dialogue systems for 3D scenes.
Connecting Multi-modal Contrastive Representations
This paper proposes a novel training-efficient method for learning MCR without paired data called Connecting Multi-modal Contrastive Representations (C-MCR).
