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Found 30 papers, 17 papers with code
MorpheuS: Neural Dynamic 360° Surface Reconstruction from Monocular RGB-D Video
Thanks to the expressiveness of neural representations, prior works can accurately capture the motion and achieve high-fidelity reconstruction of the target object.
SeMLaPS: Real-time Semantic Mapping with Latent Prior Networks and Quasi-Planar Segmentation
We present a new methodology for real-time semantic mapping from RGB-D sequences that combines a 2D neural network and a 3D network based on a SLAM system with 3D occupancy mapping.
First Place Solution to the CVPR'2023 AQTC Challenge: A Function-Interaction Centric Approach with Spatiotemporal Visual-Language Alignment
Affordance-Centric Question-driven Task Completion (AQTC) has been proposed to acquire knowledge from videos to furnish users with comprehensive and systematic instructions.
Co-SLAM: Joint Coordinate and Sparse Parametric Encodings for Neural Real-Time SLAM
We present Co-SLAM, a neural RGB-D SLAM system based on a hybrid representation, that performs robust camera tracking and high-fidelity surface reconstruction in real time.
MalIoT: Scalable and Real-time Malware Traffic Detection for IoT Networks
The machine learning approach is vital in Internet of Things (IoT) malware traffic detection due to its ability to keep pace with the ever-evolving nature of malware.
FCC: Feature Clusters Compression for Long-Tailed Visual Recognition
Through representation learning, DNNs can map BFs into dense clusters in feature space, while the features of minority classes often show sparse clusters.
CTT-Net: A Multi-view Cross-token Transformer for Cataract Postoperative Visual Acuity Prediction
Clinically, to assess the necessity of cataract surgery, accurately predicting postoperative VA before surgery by analyzing multi-view optical coherence tomography (OCT) images is crucially needed.
Visual Subtitle Feature Enhanced Video Outline Generation
Furthermore, based on the similarity between video outlines and textual outlines, we use a large number of articles with chapter headings to pretrain our model.
GO-Surf: Neural Feature Grid Optimization for Fast, High-Fidelity RGB-D Surface Reconstruction
We present GO-Surf, a direct feature grid optimization method for accurate and fast surface reconstruction from RGB-D sequences.
Weighted Concordance Index Loss-based Multimodal Survival Modeling for Radiation Encephalopathy Assessment in Nasopharyngeal Carcinoma Radiotherapy
We design a deep multimodal survival network (MSN) with two feature extractors to learn discriminative features from multimodal data.
Siamese Encoder-based Spatial-Temporal Mixer for Growth Trend Prediction of Lung Nodules on CT Scans
In the management of lung nodules, we are desirable to predict nodule evolution in terms of its diameter variation on Computed Tomography (CT) scans and then provide a follow-up recommendation according to the predicted result of the growing trend of the nodule.
Controllable Video Captioning with an Exemplar Sentence
In this paper, we investigate a novel and challenging task, namely controllable video captioning with an exemplar sentence.
DSP-SLAM: Object Oriented SLAM with Deep Shape Priors
We propose DSP-SLAM, an object-oriented SLAM system that builds a rich and accurate joint map of dense 3D models for foreground objects, and sparse landmark points to represent the background.
Integrating Semantics and Neighborhood Information with Graph-Driven Generative Models for Document Retrieval
With the need of fast retrieval speed and small memory footprint, document hashing has been playing a crucial role in large-scale information retrieval.
Actor-Action Video Classification CSC 249/449 Spring 2020 Challenge Report
This technical report summarizes submissions and compiles from Actor-Action video classification challenge held as a final project in CSC 249/449 Machine Vision course (Spring 2020) at University of Rochester
Recurrent Exposure Generation for Low-Light Face Detection
REG produces progressively and efficiently intermediate images corresponding to various exposure settings, and such pseudo-exposures are then fused by MED to detect faces across different lighting conditions.
Deep Bilateral Retinex for Low-Light Image Enhancement
Low-light images, i. e. the images captured in low-light conditions, suffer from very poor visibility caused by low contrast, color distortion and significant measurement noise.
STH: Spatio-Temporal Hybrid Convolution for Efficient Action Recognition
Such a design enables efficient spatio-temporal modeling and maintains a small model scale.
Weakly-Supervised Multi-Level Attentional Reconstruction Network for Grounding Textual Queries in Videos
The task of temporally grounding textual queries in videos is to localize one video segment that semantically corresponds to the given query.
Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis
Cancer diagnosis, prognosis, and therapeutic response predictions are based on morphological information from histology slides and molecular profiles from genomic data.
Semantic Conditioned Dynamic Modulation for Temporal Sentence Grounding in Videos
Temporal sentence grounding in videos aims to detect and localize one target video segment, which semantically corresponds to a given sentence.
Weakly Supervised Prostate TMA Classification via Graph Convolutional Networks
In prostate cancer, the Gleason score is a grading system used to measure the aggressiveness of prostate cancer from the spatial organization of cells and the distribution of glands.
Semi-Supervised Histology Classification using Deep Multiple Instance Learning and Contrastive Predictive Coding
Convolutional neural networks can be trained to perform histology slide classification using weak annotations with multiple instance learning (MIL).
Temporally Grounding Language Queries in Videos by Contextual Boundary-aware Prediction
The task of temporally grounding language queries in videos is to temporally localize the best matched video segment corresponding to a given language (sentence).
Controllable Video Captioning with POS Sequence Guidance Based on Gated Fusion Network
In this paper, we propose to guide the video caption generation with Part-of-Speech (POS) information, based on a gated fusion of multiple representations of input videos.
Generating an Overview Report over Many Documents
To overcome this obstacle, we present NDORGS (Numerous Documents' Overview Report Generation Scheme) that integrates text filtering, keyword scoring, single-document summarization (SDS), topic modeling, MDS, and title generation to generate a coherent, well-structured ORPT.
An anomaly prediction framework for financial IT systems using hybrid machine learning methods
To improve the efficiency and accuracy of system failure detection and thereby reduce the impact of system failures on financial services, we propose a novel machine learning-based framework to predict the occurrence of system exceptions and failures in a financial software system.
Non-local NetVLAD Encoding for Video Classification
This paper describes our solution for the 2$^\text{nd}$ YouTube-8M video understanding challenge organized by Google AI.
Bidirectional Attentive Fusion with Context Gating for Dense Video Captioning
We propose a bidirectional proposal method that effectively exploits both past and future contexts to make proposal predictions.
