Search Results for author:
Found 31 papers, 12 papers with code
Prompt-Guided Generation of Structured Chest X-Ray Report Using a Pre-trained LLM
Our method introduces a prompt-guided approach to generate structured chest X-ray reports using a pre-trained large language model (LLM).
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Recent research, such as BitNet, is paving the way for a new era of 1-bit Large Language Models (LLMs).
SGS-SLAM: Semantic Gaussian Splatting For Neural Dense SLAM
We present SGS-SLAM, the first semantic visual SLAM system based on Gaussian Splatting.
DDN-SLAM: Real-time Dense Dynamic Neural Implicit SLAM
To address dynamic tracking interferences, we propose a feature point segmentation method that combines semantic features with a mixed Gaussian distribution model.
Temporal Adaptive RGBT Tracking with Modality Prompt
RGBT tracking has been widely used in various fields such as robotics, surveillance processing, and autonomous driving.
Ranked #2 on
Rgb-T Tracking
on RGBT210
VOT: Revolutionizing Speaker Verification with Memory and Attention Mechanisms
Speaker verification is to judge the similarity of two unknown voices in an open set, where the ideal speaker embedding should be able to condense discriminant information into a compact utterance-level representation that has small intra-speaker distances and large inter-speaker distances. We propose a novel model named Voice Transformer(VOT) for speaker verification.
BitNet: Scaling 1-bit Transformers for Large Language Models
The increasing size of large language models has posed challenges for deployment and raised concerns about environmental impact due to high energy consumption.
PREFER: Prompt Ensemble Learning via Feedback-Reflect-Refine
Moreover, to enhance stability of the prompt effect evaluation, we propose a novel prompt bagging method involving forward and backward thinking, which is superior to majority voting and is beneficial for both feedback and weight calculation in boosting.
Hyperspectral Target Detection Based on Low-Rank Background Subspace Learning and Graph Laplacian Regularization
Firstly, to obtain a complete and pure background dictionary, we propose a LRR-based background subspace learning method by jointly mining the low-dimensional structure of all pixels.
High-Accuracy Approximation of Evolutionary Pairwise Games on Complex Networks
Previous studies have shown that the topological properties of a complex network, such as heterogeneity and average degree, affect the evolutionary game dynamics on it.
The state-of-the-art 3D anisotropic intracranial hemorrhage segmentation on non-contrast head CT: The INSTANCE challenge
It is intended to resolve the above-mentioned problems and promote the development of both intracranial hemorrhage segmentation and anisotropic data processing.
TorchScale: Transformers at Scale
Large Transformers have achieved state-of-the-art performance across many tasks.
Foundation Transformers
A big convergence of model architectures across language, vision, speech, and multimodal is emerging.
Feature Extractor Stacking for Cross-domain Few-shot Learning
Recently published CDFSL methods generally construct a universal model that combines knowledge of multiple source domains into one feature extractor.
DeepNet: Scaling Transformers to 1,000 Layers
In this paper, we propose a simple yet effective method to stabilize extremely deep Transformers.
Raw Bayer Pattern Image Synthesis for Computer Vision-oriented Image Signal Processing Pipeline Design
It is shown that by adding another transformation, the proposed method is able to synthesize high-quality RAW Bayer images with arbitrary size.
Deep Learning for Depression Recognition with Audiovisual Cues: A Review
Consequently, to improve current medical care, many scholars have used deep learning to extract a representation of depression cues in audio and video for automatic depression detection.
Classification of Smoking and Calling using Deep Learning
Since 2014, very deep convolutional neural networks have been proposed and become the must-have weapon for champions in all kinds of competition.
Carleson measures on convex domains
Following M. Abate and A. Saracco's work on strongly pseudoconvex domains in $\mathbb{C}^n$, we characterize Carleson measures of $A^2(D)$ in bounded convex domains with smooth boundary of finite type.
Complex Variables
AEGCN: An Autoencoder-Constrained Graph Convolutional Network
In extensive experiments on citation networks and other heterogeneous graphs, we demonstrate that adding autoencoder constraints significantly improves the performance of graph convolutional networks.
Recognizing Handwritten Mathematical Expressions as LaTex Sequences Using a Multiscale Robust Neural Network
In this paper, a robust multiscale neural network is proposed to recognize handwritten mathematical expressions and output LaTeX sequences, which can effectively and correctly focus on where each step of output should be concerned and has a positive effect on analyzing the two-dimensional structure of handwritten mathematical expressions and identifying different mathematical symbols in a long expression.
Deep Multiphase Level Set for Scene Parsing
The proposed method consists of three modules, i. e., recurrent FCNs, adaptive multiphase level set, and deeply supervised learning.
An Efficient Target Detection and Recognition Method in Aerial Remote-sensing Images Based on Multiangle Regions-of-Interest
Recently, deep learning technology have been extensively used in the field of image recognition.
Robust Encoder-Decoder Learning Framework towards Offline Handwritten Mathematical Expression Recognition Based on Multi-Scale Deep Neural Network
Offline handwritten mathematical expression recognition is a challenging task, because handwritten mathematical expressions mainly have two problems in the process of recognition.
The Graph-Based Behavior-Aware Recommendation for Interactive News
First, we build an interaction behavior graph for multi-level and multi-category data.
ChestNet: A Deep Neural Network for Classification of Thoracic Diseases on Chest Radiography
Computer-aided techniques may lead to more accurate and more acces-sible diagnosis of thorax diseases on chest radiography.
Non-rigid Object Tracking via Deep Multi-scale Spatial-temporal Discriminative Saliency Maps
Extensive experiments demonstrate that the proposed algorithm achieves competitive performance in both saliency detection and visual tracking, especially outperforming other related trackers on the non-rigid object tracking datasets.
Video Person Re-identification by Temporal Residual Learning
In this paper, we propose a novel feature learning framework for video person re-identification (re-ID).
Learning Uncertain Convolutional Features for Accurate Saliency Detection
In this paper, we propose a novel deep fully convolutional network model for accurate salient object detection.
Ranked #5 on
Saliency Detection
on DUT-OMRON
Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection
In addition, to achieve accurate boundary inference and semantic enhancement, edge-aware feature maps in low-level layers and the predicted results of low resolution features are recursively embedded into the learning framework.
Ranked #20 on
RGB Salient Object Detection
on DUTS-TE
(max F-measure metric)
End-to-End Image Super-Resolution via Deep and Shallow Convolutional Networks
In this paper, we seek an alternative and propose a new image SR method, which jointly learns the feature extraction, upsampling and HR reconstruction modules, yielding a completely end-to-end trainable deep CNN.
