Search Results for author:
Found 27 papers, 5 papers with code
Roadside Monocular 3D Detection via 2D Detection Prompting
Our method builds on a key insight that, compared with 3D detectors, a 2D detector is much easier to train and performs significantly better w. r. t detections on the 2D image plane.
The Neglected Tails in Vision-Language Models
We address this by using large language models (LLMs) to count the number of pretraining texts that contain synonyms of these concepts.
Long-Tailed 3D Detection via Multi-Modal Fusion
Contemporary autonomous vehicle (AV) benchmarks have advanced techniques for training 3D detectors, particularly on large-scale multi-modal (LiDAR + RGB) data.
Knowledge Graph Driven Recommendation System Algorithm
We first use a single-layer neural network to merge individual node features in the graph, and then adjust the aggregation weights of neighboring entities by incorporating influence factors.
Prompting Scientific Names for Zero-Shot Species Recognition
We find that common names are more likely to be included in CLIP's training set, and prompting them achieves 2$\sim$5 times higher accuracy on benchmarking datasets of fine-grained species recognition.
Real-Time Progressive Learning: Accumulate Knowledge from Control with Neural-Network-Based Selective Memory
Memory, as the basis of learning, determines the storage, update and forgetting of knowledge and further determines the efficiency of learning.
Trajectory-aware Principal Manifold Framework for Data Augmentation and Image Generation
Many existing methods generate new samples from a parametric distribution, like the Gaussian, with little attention to generate samples along the data manifold in either the input or feature space.
NTIRE 2023 Quality Assessment of Video Enhancement Challenge
61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions.
Hybrid quantum-classical convolutional neural network for phytoplankton classification
The taxonomic composition and abundance of phytoplankton, having direct impact on marine ecosystem dynamic and global environment change, are listed as essential ocean variables.
Quantum Recurrent Neural Networks for Sequential Learning
Quantum neural network (QNN) is one of the promising directions where the near-term noisy intermediate-scale quantum (NISQ) devices could find advantageous applications against classical resources.
CoSign: Exploring Co-occurrence Signals in Skeleton-based Continuous Sign Language Recognition
The co-occurrence signals (e. g., hand shape, facial expression, and lip pattern) play a critical role in Continuous Sign Language Recognition (CSLR).
Selective Memory Recursive Least Squares: Recast Forgetting into Memory in RBF Neural Network Based Real-Time Learning
In radial basis function neural network (RBFNN) based real-time learning tasks, forgetting mechanisms are widely used such that the neural network can keep its sensitivity to new data.
Deep Radial Embedding for Visual Sequence Learning
The blank class of CTC plays a crucial role in the alignment process and is often considered responsible for the peaky behavior of CTC.
Realistic Bokeh Effect Rendering on Mobile GPUs, Mobile AI & AIM 2022 challenge: Report
In this Mobile AI challenge, the target was to develop an efficient end-to-end AI-based bokeh effect rendering approach that can run on modern smartphone GPUs using TensorFlow Lite.
Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Report
Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks.
Alleviating the Sample Selection Bias in Few-shot Learning by Removing Projection to the Centroid
In this paper, we find that this problem usually occurs when the positions of support samples are in the vicinity of task centroid -- the mean of all class centroids in the task.
Q-Net: Query-Informed Few-Shot Medical Image Segmentation
Deep learning has achieved tremendous success in computer vision, while medical image segmentation (MIS) remains a challenge, due to the scarcity of data annotations.
Towards Efficient and Stable K-Asynchronous Federated Learning with Unbounded Stale Gradients on Non-IID Data
Federated learning (FL) is an emerging privacy-preserving paradigm that enables multiple participants collaboratively to train a global model without uploading raw data.
Dual Path Structural Contrastive Embeddings for Learning Novel Objects
Learning novel classes from a very few labeled samples has attracted increasing attention in machine learning areas.
Class-Incremental Few-Shot Object Detection
To tackle this problem, we propose a novel method LEAST, which can transfer with Less forgetting, fEwer training resources, And Stronger Transfer capability.
Asynchronous Federated Learning with Differential Privacy for Edge Intelligence
Formally, we give the first analysis on the model convergence of AFL under DP and propose a multi-stage adjustable private algorithm (MAPA) to improve the trade-off between model utility and privacy by dynamically adjusting both the noise scale and the learning rate.
Impact of Prior Knowledge and Data Correlation on Privacy Leakage: A Unified Analysis
Considering general correlations, a closed-form expression of privacy leakage is derived for continuous data, and a chain rule is presented for discrete data.
Semi-Supervised Classification for oil reservoir
In this paper, we use the semi-supervised learning to solve the problem of ever-increasing amount of unlabelled data available for interpretation.
Learning Robust Features with Incremental Auto-Encoders
In this paper, we propose a new method to learn non-linear robust features by taking advantage of the data manifold structure.
Zero-Shot Learning with Generative Latent Prototype Model
Zero-shot learning, which studies the problem of object classification for categories for which we have no training examples, is gaining increasing attention from community.
Zero-Shot Recognition using Dual Visual-Semantic Mapping Paths
This mapping is learned on training data of seen classes and is expected to have transfer ability to unseen classes.
