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Found 32 papers, 14 papers with code
Extreme Video Compression with Pre-trained Diffusion Models
The results showcase the potential of exploiting the temporal relations in video data using generative models.
Closed-Loop Unsupervised Representation Disentanglement with $β$-VAE Distillation and Diffusion Probabilistic Feedback
Representation disentanglement may help AI fundamentally understand the real world and thus benefit both discrimination and generation tasks.
Self-Supervised Dynamic Hypergraph Recommendation based on Hyper-Relational Knowledge Graph
However, the long-tail distribution of entities leads to sparsity in supervision signals, which weakens the quality of item representation when utilizing KG enhancement.
One at a Time: Progressive Multi-step Volumetric Probability Learning for Reliable 3D Scene Perception
Numerous studies have investigated the pivotal role of reliable 3D volume representation in scene perception tasks, such as multi-view stereo (MVS) and semantic scene completion (SSC).
EMoG: Synthesizing Emotive Co-speech 3D Gesture with Diffusion Model
In this paper, we present a novel framework (EMoG) to tackle the above challenges with denoising diffusion models: 1) To alleviate the one-to-many problem, we incorporate emotion clues to guide the generation process, making the generation much easier; 2) To model joint correlation, we propose to decompose the difficult gesture generation into two sub-problems: joint correlation modeling and temporal dynamics modeling.
ICDAR 2023 Competition on Structured Text Extraction from Visually-Rich Document Images
It is hoped that this competition will attract many researchers in the field of CV and NLP, and bring some new thoughts to the field of Document AI.
NaviNeRF: NeRF-based 3D Representation Disentanglement by Latent Semantic Navigation
They are complementary -- the outer navigation is to identify global-view semantic directions, and the inner refinement dedicates to fine-grained attributes.
MixPro: Simple yet Effective Data Augmentation for Prompt-based Learning
Prompt-based learning has shown considerable promise in reformulating various downstream tasks as cloze problems by combining original input with a predetermined template.
A Two-Stage Framework with Self-Supervised Distillation For Cross-Domain Text Classification
Cross-domain text classification aims to adapt models to a target domain that lacks labeled data.
Bridging Stereo Geometry and BEV Representation with Reliable Mutual Interaction for Semantic Scene Completion
However, due to the inherent representation gap between stereo geometry and BEV features, it is non-trivial to bridge them for dense prediction task of SSC.
Semantic-Guided Generative Image Augmentation Method with Diffusion Models for Image Classification
In contrast, generative methods bring more image diversity in the augmented images but may not preserve semantic consistency, thus incorrectly changing the essential semantics of the original image.
Knowledge Enhancement for Contrastive Multi-Behavior Recommendation
In particular, we design the multi-behavior learning module to extract users' personalized behavior information for user-embedding enhancement, and utilize knowledge graph in the knowledge enhancement module to derive more robust knowledge-aware representations for items.
VideoDubber: Machine Translation with Speech-Aware Length Control for Video Dubbing
In this paper, we propose a machine translation system tailored for the task of video dubbing, which directly considers the speech duration of each token in translation, to match the length of source and target speech.
Multi-rate adaptive transform coding for video compression
Contemporary lossy image and video coding standards rely on transform coding, the process through which pixels are mapped to an alternative representation to facilitate efficient data compression.
MetaPrompting: Learning to Learn Better Prompts
Prompting method is regarded as one of the crucial progress for few-shot nature language processing.
When Counting Meets HMER: Counting-Aware Network for Handwritten Mathematical Expression Recognition
Recently, most handwritten mathematical expression recognition (HMER) methods adopt the encoder-decoder networks, which directly predict the markup sequences from formula images with the attention mechanism.
Heterogeneous graph neural network for power allocation in multicarrier-division duplex cell-free massive MIMO systems
In-band full duplex cell-free (CF) systems suffer from severe self-interference and cross-link interference, especially when CF systems are operated in distributed way.
Inverse is Better! Fast and Accurate Prompt for Few-shot Slot Tagging
Such inverse prompting only requires a one-turn prediction for each slot type and greatly speeds up the prediction.
AdaSpeech 4: Adaptive Text to Speech in Zero-Shot Scenarios
We model the speaker characteristics systematically to improve the generalization on new speakers.
Decoupling Visual-Semantic Feature Learning for Robust Scene Text Recognition
As a result, the learning of semantic features is prone to have a bias on the limited vocabulary of the training set, which is called vocabulary reliance.
DelightfulTTS: The Microsoft Speech Synthesis System for Blizzard Challenge 2021
The goal of this challenge is to synthesize natural and high-quality speech from text, and we approach this goal in two perspectives: The first is to directly model and generate waveform in 48 kHz sampling rate, which brings higher perception quality than previous systems with 16 kHz or 24 kHz sampling rate; The second is to model the variation information in speech through a systematic design, which improves the prosody and naturalness.
Data Augmentation Approaches in Natural Language Processing: A Survey
One of the main focuses of the DA methods is to improve the diversity of training data, thereby helping the model to better generalize to unseen testing data.
Follow Your Path: a Progressive Method for Knowledge Distillation
However, it has been observed that a converged heavy teacher model is strongly constrained for learning a compact student network and could make the optimization subject to poor local optima.
AdaSpeech 3: Adaptive Text to Speech for Spontaneous Style
While recent text to speech (TTS) models perform very well in synthesizing reading-style (e. g., audiobook) speech, it is still challenging to synthesize spontaneous-style speech (e. g., podcast or conversation), mainly because of two reasons: 1) the lack of training data for spontaneous speech; 2) the difficulty in modeling the filled pauses (um and uh) and diverse rhythms in spontaneous speech.
AdaSpeech 2: Adaptive Text to Speech with Untranscribed Data
In adaptation, we use untranscribed speech data for speech reconstruction and only fine-tune the TTS decoder.
AdaSpeech: Adaptive Text to Speech for Custom Voice
2) To better trade off the adaptation parameters and voice quality, we introduce conditional layer normalization in the mel-spectrogram decoder of AdaSpeech, and fine-tune this part in addition to speaker embedding for adaptation.
Learning from deep model via exploring local targets
However, it has been observed that a converged heavy teacher model is strongly constrained for learning a compact student network and could make the optimization subject to poor local optima.
On the Sentence Embeddings from Pre-trained Language Models
Pre-trained contextual representations like BERT have achieved great success in natural language processing.
Ranked #16 on
Semantic Textual Similarity
on STS16
A Surprisingly Effective Fix for Deep Latent Variable Modeling of Text
In this paper, we investigate a simple fix for posterior collapse which yields surprisingly effective results.
An Adversarial Approach to High-Quality, Sentiment-Controlled Neural Dialogue Generation
In this work, we propose a method for neural dialogue response generation that allows not only generating semantically reasonable responses according to the dialogue history, but also explicitly controlling the sentiment of the response via sentiment labels.
Multi-Perspective Fusion Network for Commonsense Reading Comprehension
From the experimental results, we can conclude that the difference fusion is comparable with union fusion, and the similarity fusion needs to be activated by the union fusion.
Stochastic WaveNet: A Generative Latent Variable Model for Sequential Data
In this paper, we combine the ideas from both stochastic latent variables and dilated convolutions, and propose a new architecture to model sequential data, termed as Stochastic WaveNet, where stochastic latent variables are injected into the WaveNet structure.
