Search Results for author:
Found 19 papers, 3 papers with code
Audio-Infused Automatic Image Colorization by Exploiting Audio Scene Semantics
Second, the natural co-occurrence of audio and video is utilized to learn the color semantic correlations between audio and visual scenes.
RPG-Palm: Realistic Pseudo-data Generation for Palmprint Recognition
Palmprint recently shows great potential in recognition applications as it is a privacy-friendly and stable biometric.
Enhancing Dynamic Image Advertising with Vision-Language Pre-training
The core of DIA is a query-image matching module performing ad image retrieval and relevance modeling.
Learning In-context Learning for Named Entity Recognition
M}$, and a new entity extractor can be implicitly constructed by applying new instruction and demonstrations to PLMs, i. e., $\mathcal{ (\lambda .
Universal Information Extraction as Unified Semantic Matching
Based on this paradigm, we propose to universally model various IE tasks with Unified Semantic Matching (USM) framework, which introduces three unified token linking operations to model the abilities of structuring and conceptualizing.
Convolutional Long Short-Term Memory (convLSTM) for Spatio-Temporal Forecastings of Saturations and Pressure in the SACROC Field
A machine learning architecture composed of convolutional long short-term memory (convLSTM) is developed to predict spatio-temporal parameters in the SACROC oil field, Texas, USA.
Depth Estimation by Combining Binocular Stereo and Monocular Structured-Light
In order to verify the effectiveness of the proposed system, we build a prototype and collect a test dataset in indoor scenes.
Geometric Synthesis: A Free lunch for Large-scale Palmprint Recognition Model Pretraining
In this paper, by observing that palmar creases are the key information to deep-learning-based palmprint recognition, we propose to synthesize training data by manipulating palmar creases.
Fooling the Eyes of Autonomous Vehicles: Robust Physical Adversarial Examples Against Traffic Sign Recognition Systems
From the view of object detectors, the traffic sign`s position and quality of the video are continuously changing, rendering the digital AEs ineffective in the physical world.
Multi-frame Joint Enhancement for Early Interlaced Videos
Early interlaced videos usually contain multiple and interlacing and complex compression artifacts, which significantly reduce the visual quality.
Ranked #1 on
Video Deinterlacing
on MSU Deinterlacer Benchmark
Bilateral Grid Learning for Stereo Matching Networks
In this paper, we present a novel edge-preserving cost volume upsampling module based on the slicing operation in the learned bilateral grid.
Touchless Palmprint Recognition based on 3D Gabor Template and Block Feature Refinement
To our best knowledge, it is the largest contactless palmprint image benchmark ever collected with regard to the number of individuals and palms.
Deep View Synthesis via Self-Consistent Generative Network
To address the above issues, in this paper, we propose a novel deep generative model, called Self-Consistent Generative Network (SCGN), which synthesizes novel views from the given input views without explicitly exploiting the geometric information.
Rethinking deinterlacing for early interlaced videos
Traditional deinterlacing approaches are mainly focused on early interlacing scanning systems and thus cannot handle the complex and complicated artifacts in real-world early interlaced videos.
Residual-Guided In-Loop Filter Using Convolution Neural Network
The block-based coding structure in the hybrid video coding framework inevitably introduces compression artifacts such as blocking, ringing, etc.
ARNOR: Attention Regularization based Noise Reduction for Distant Supervision Relation Classification
In this paper, we propose ARNOR, a novel Attention Regularization based NOise Reduction framework for distant supervision relation classification.
End-to-End Detection and Re-identification Integrated Net for Person Search
This paper proposes a pedestrian detection and re-identification (re-id) integration net (I-Net) in an end-to-end learning framework.
GUN: Gradual Upsampling Network for Single Image Super-Resolution
The GUN consists of an input layer, multiple upsampling and convolutional layers, and an output layer.
Local Patch Encoding-Based Method for Single Image Super-Resolution
The proposed method consists of a learning stage and a reconstructing stage.
