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Found 52 papers, 20 papers with code
Unifying Appearance Codes and Bilateral Grids for Driving Scene Gaussian Splatting
In this paper, we propose a novel multi-scale bilateral grid that unifies appearance codes and bilateral grids.
Challenger: Affordable Adversarial Driving Video Generation
Generating photorealistic driving videos has seen significant progress recently, but current methods largely focus on ordinary, non-adversarial scenarios.
Communication-Efficient Diffusion Denoising Parallelization via Reuse-then-Predict Mechanism
Diffusion models have emerged as a powerful class of generative models across various modalities, including image, video, and audio synthesis.
UAV-Enabled Joint Sensing, Communication, Powering and Backhaul Transmission in Maritime Monitoring Networks
This paper addresses the challenge of energy-constrained maritime monitoring networks by proposing an unmanned aerial vehicle (UAV)-enabled integrated sensing, communication, powering and backhaul transmission scheme with a tailored time-division duplex frame structure.
DiST-4D: Disentangled Spatiotemporal Diffusion with Metric Depth for 4D Driving Scene Generation
To address this, we propose DiST-4D, the first disentangled spatiotemporal diffusion framework for 4D driving scene generation, which leverages metric depth as the core geometric representation.
MuDG: Taming Multi-modal Diffusion with Gaussian Splatting for Urban Scene Reconstruction
Recent breakthroughs in radiance fields have significantly advanced 3D scene reconstruction and novel view synthesis (NVS) in autonomous driving.
Joint Beamforming and Compressed Sensing for Uplink Grant-Free Access
Based on this, we further develop a joint adaptive beamforming and subspace pursuit (JABF-SP) algorithm for the multiuser detection and data recovery, with a novel sparsity level decision method without the accurate knowledge of the noise level.
U-NIAH: Unified RAG and LLM Evaluation for Long Context Needle-In-A-Haystack
Recent advancements in Large Language Models (LLMs) have expanded their context windows to unprecedented lengths, sparking debates about the necessity of Retrieval-Augmented Generation (RAG).
Recent Advances in Discrete Speech Tokens: A Review
The rapid advancement of speech generation technologies in the era of large language models (LLMs) has established discrete speech tokens as a foundational paradigm for speech representation.
LangSurf: Language-Embedded Surface Gaussians for 3D Scene Understanding
To this end, we propose a Language-Embedded Surface Field (LangSurf), which accurately aligns the 3D language fields with the surface of objects, facilitating precise 2D and 3D segmentation with text query, widely expanding the downstream tasks such as removal and editing.
Can Large Language Models Understand You Better? An MBTI Personality Detection Dataset Aligned with Population Traits
In this paper, we optimize the task by constructing MBTIBench, the first manually annotated high-quality MBTI personality detection dataset with soft labels, under the guidance of psychologists.
OccScene: Semantic Occupancy-based Cross-task Mutual Learning for 3D Scene Generation
Within OccScene, the perception module can be effectively improved with customized and diverse generated scenes, while the perception priors in return enhance the generation performance for mutual benefits.
Hierarchical Context Alignment with Disentangled Geometric and Temporal Modeling for Semantic Occupancy Prediction
Camera-based 3D Semantic Occupancy Prediction (SOP) is crucial for understanding complex 3D scenes from limited 2D image observations.
3D Semantic Occupancy Prediction
LIDAR Semantic Segmentation
+2
UniScene: Unified Occupancy-centric Driving Scene Generation
UniScene employs a progressive generation process that decomposes the complex task of scene generation into two hierarchical steps: (a) first generating semantic occupancy from a customized scene layout as a meta scene representation rich in both semantic and geometric information, and then (b) conditioned on occupancy, generating video and LiDAR data, respectively, with two novel transfer strategies of Gaussian-based Joint Rendering and Prior-guided Sparse Modeling.
Fast and High-Quality Auto-Regressive Speech Synthesis via Speculative Decoding
The auto-regressive architecture, like GPTs, is widely used in modern Text-to-Speech (TTS) systems.
R-CoT: Reverse Chain-of-Thought Problem Generation for Geometric Reasoning in Large Multimodal Models
Current geometric data generation approaches, which apply preset templates to generate geometric data or use Large Language Models (LLMs) to rephrase questions and answers (Q&A), unavoidably limit data accuracy and diversity.
UAV-Enabled Integrated Sensing and Communication in Maritime Emergency Networks
Once the UAV passes the initial operating position, the UAV's trajectory and resource allocation are optimized during the mission period to maximize the end-to-end communication rate under the constraint of minimum sensing QoS.
TAPTRv2: Attention-based Position Update Improves Tracking Any Point
In this paper, we present TAPTRv2, a Transformer-based approach built upon TAPTR for solving the Tracking Any Point (TAP) task.
Hierarchical Temporal Context Learning for Camera-based Semantic Scene Completion
To address this problem, we present HTCL, a novel Hierarchical Temporal Context Learning paradigm for improving camera-based semantic scene completion.
Ranked #11 on
3D Semantic Scene Completion
on SemanticKITTI
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.
