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Found 23 papers, 8 papers with code
Adaptive Stereo Depth Estimation with Multi-Spectral Images Across All Lighting Conditions
Depth estimation under adverse conditions remains a significant challenge.
MCGS: Multiview Consistency Enhancement for Sparse-View 3D Gaussian Radiance Fields
These modular, plug-and-play strategies enhance robustness to sparse input views, accelerate rendering, and reduce memory consumption, making MCGS a practical and efficient framework for 3D Gaussian Splatting.
PVContext: Hybrid Context Model for Point Cloud Compression
Efficient storage of large-scale point cloud data has become increasingly challenging due to advancements in scanning technology.
Spatial Annealing for Efficient Few-shot Neural Rendering
Although FreeNeRF has introduced an efficient frequency annealing strategy, its operation on frequency positional encoding is incompatible with the efficient hybrid representations.
Whistle: Data-Efficient Multilingual and Crosslingual Speech Recognition via Weakly Phonetic Supervision
We construct a common experimental setup based on the CommonVoice dataset, called CV-Lang10, with 10 seen languages and 2 unseen languages.
DINO-SD: Champion Solution for ICRA 2024 RoboDepth Challenge
However, given that most of the data in the autonomous driving dataset is collected in daytime scenarios, this leads to poor depth model performance in the face of out-of-distribution(OoD) data.
The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition
In the realm of autonomous driving, robust perception under out-of-distribution conditions is paramount for the safe deployment of vehicles.
Mesh Denoising Transformer
Next, we propose a dual-stream structure consisting of a Geometric Encoder branch and a Spatial Encoder branch, which jointly encode local geometry details and spatial information to fully explore multimodal information for mesh denoising.
REPS: Reconstruction-based Point Cloud Sampling
Our method outperforms previous approaches in preserving the structural features of the sampled point clouds.
Mitigating Bias for Question Answering Models by Tracking Bias Influence
Based on the intuition that a model would lean to be more biased if it learns from a biased example, we measure the bias level of a query instance by observing its influence on another instance.
DiNADO: Norm-Disentangled Neurally-Decomposed Oracles for Controlling Language Models
NeurAlly-Decomposed Oracle (NADO) is a powerful approach for controllable generation with large language models.
Unsupervised Melody-to-Lyric Generation
Automatic melody-to-lyric generation is a task in which song lyrics are generated to go with a given melody.
Exploring Energy-based Language Models with Different Architectures and Training Methods for Speech Recognition
Energy-based language models (ELMs) parameterize an unnormalized distribution for natural sentences and are radically different from popular autoregressive language models (ALMs).
Unsupervised Melody-Guided Lyrics Generation
At inference time, we leverage the crucial alignments between melody and lyrics and compile the given melody into constraints to guide the generation process.
A Learning-based Adaptive Compliance Method for Symmetric Bi-manual Manipulation
However, traditional methods have viewed motion planning and compliant control as two separate modules, which can lead to conflicts with the simultaneous change of the desired trajectory and impedance parameters in the presence of external forces and disturbances.
Self-Supervised Arbitrary-Scale Point Clouds Upsampling via Implicit Neural Representation
Most existing methods either take the end-to-end supervised learning based manner, where large amounts of pairs of sparse input and dense ground-truth are exploited as supervision information; or treat up-scaling of different scale factors as independent tasks, and have to build multiple networks to handle upsampling with varying factors.
SME: ReRAM-based Sparse-Multiplication-Engine to Squeeze-Out Bit Sparsity of Neural Network
However, it is challenging for crossbar architecture to exploit the sparsity in the DNN.
Improving Neural Network Efficiency via Post-Training Quantization With Adaptive Floating-Point
Model quantization has emerged as a mandatory technique for efficient inference with advanced Deep Neural Networks (DNN).
Simple Question Answering with Subgraph Ranking and Joint-Scoring
Using this framework as a starting point, we focus on two aspects: improving subgraph selection through a novel ranking method and leveraging the subject--relation dependency by proposing a joint scoring CNN model with a novel loss function that enforces the well-order of scores.
Hierarchical Routing Mixture of Experts
Addressing these problems, we propose a binary tree-structured hierarchical routing mixture of experts (HRME) model that has classifiers as non-leaf node experts and simple regression models as leaf node experts.
Neural Regression Trees
Regression-via-Classification (RvC) is the process of converting a regression problem to a classification one.
Neural Regression Tree
Regression-via-Classification (RvC) is the process of converting a regression problem to a classification one.
Speaker identification from the sound of the human breath
The goal of this paper is to demonstrate that breath sounds are indeed bio-signatures that can be used to identify speakers.
