Search Results for author:
Found 19 papers, 9 papers with code
WavRAG: Audio-Integrated Retrieval Augmented Generation for Spoken Dialogue Models
Specifically, we propose the WavRetriever to facilitate the retrieval from a text-audio hybrid knowledge base, and further enhance the in-context capabilities of spoken dialogue models through the integration of chain-of-thought reasoning.
Speech Watermarking with Discrete Intermediate Representations
In this paper, we propose DiscreteWM, a novel speech watermarking framework that injects watermarks into the discrete intermediate representations of speech.
WavChat: A Survey of Spoken Dialogue Models
Recent advancements in spoken dialogue models, exemplified by systems like GPT-4o, have captured significant attention in the speech domain.
Improving Text-guided Object Inpainting with Semantic Pre-inpainting
In this paper, we propose to decompose the typical single-stage object inpainting into two cascaded processes: 1) semantic pre-inpainting that infers the semantic features of desired objects in a multi-modal feature space; 2) high-fieldity object generation in diffusion latent space that pivots on such inpainted semantic features.
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.
OS-FPI: A Coarse-to-Fine One-Stream Network for UAV Geo-Localization
Furthermore, its performance in meter-level localization accuracy is impressive, with 182. 62% improvement in 3-meter accuracy, 164. 17% improvement in 5-meter accuracy, and 137. 43% improvement in 10-meter accuracy.
Skywork: A More Open Bilingual Foundation Model
In this technical report, we present Skywork-13B, a family of large language models (LLMs) trained on a corpus of over 3. 2 trillion tokens drawn from both English and Chinese texts.
SkyMath: Technical Report
Large language models (LLMs) have shown great potential to solve varieties of natural language processing (NLP) tasks, including mathematical reasoning.
Hybrid Augmented Automated Graph Contrastive Learning
Graph augmentations are essential for graph contrastive learning.
Tightening Discretization-based MILP Models for the Pooling Problem using Upper Bounds on Bilinear Terms
Discretization-based methods have been proposed for solving nonconvex optimization problems with bilinear terms such as the pooling problem.
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
Deep Learning-based Predictive Control of Battery Management for Frequency Regulation
The design procedure of the proposed scheme consists of two sequential processes: (1) the SL process, in which we first run a simulation with an MPC embedding a low-fidelity battery model to generate a training data set, and then, based on the generated data set, we optimize a DNN-approximated policy using SL algorithms; and (2) the RL process, in which we utilize RL algorithms to improve the performance of the DNN-approximated policy by balancing short-term economic incentives and long-term battery degradation.
Tackling Catastrophic Forgetting and Background Shift in Continual Semantic Segmentation
classes predicted by the old model to deal with background shift and avoid catastrophic forgetting of the old classes.
Ranked #7 on
Overlapped 15-1
on PASCAL VOC 2012
Class Incremental Learning
Continual Semantic Segmentation
+5
PLOP: Learning without Forgetting for Continual Semantic Segmentation
classes predicted by the old model to deal with background shift and avoid catastrophic forgetting of the old classes.
Ranked #1 on
Domain 11-5
on Cityscapes val
Class Incremental Learning
Continual Semantic Segmentation
+16
NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results
This paper reviews the NTIRE 2020 challenge on perceptual extreme super-resolution with focus on proposed solutions and results.
Guiding Variational Response Generator to Exploit Persona
Leveraging persona information of users in Neural Response Generators (NRG) to perform personalized conversations has been considered as an attractive and important topic in the research of conversational agents over the past few years.
REVE: Regularizing Deep Learning with Variational Entropy Bound
Studies on generalization performance of machine learning algorithms under the scope of information theory suggest that compressed representations can guarantee good generalization, inspiring many compression-based regularization methods.
MemeFaceGenerator: Adversarial Synthesis of Chinese Meme-face from Natural Sentences
Chinese meme-face is a special kind of internet subculture widely spread in Chinese Social Community Networks.
SEMEDA: Enhancing Segmentation Precision with Semantic Edge Aware Loss
As a result, the predictions outputted by such networks usually struggle to accurately capture the object boundaries and exhibit holes inside the objects.
