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Found 18 papers, 7 papers with code
Talaria: Interactively Optimizing Machine Learning Models for Efficient Inference
On-device machine learning (ML) moves computation from the cloud to personal devices, protecting user privacy and enabling intelligent user experiences.
Toward Accurate Camera-based 3D Object Detection via Cascade Depth Estimation and Calibration
First, a depth estimation (DE) scheme leverages relative depth information to realize the effective feature lifting from 2D to 3D spaces.
The Causal Impact of Credit Lines on Spending Distributions
Consumer credit services offered by e-commerce platforms provide customers with convenient loan access during shopping and have the potential to stimulate sales.
DocStormer: Revitalizing Multi-Degraded Colored Document Images to Pristine PDF
Thus, we propose DocStormer, a novel algorithm designed to restore multi-degraded colored documents to their potential pristine PDF.
SupFusion: Supervised LiDAR-Camera Fusion for 3D Object Detection
In this paper, we propose a novel training strategy called SupFusion, which provides an auxiliary feature level supervision for effective LiDAR-Camera fusion and significantly boosts detection performance.
DeLELSTM: Decomposition-based Linear Explainable LSTM to Capture Instantaneous and Long-term Effects in Time Series
The tensorized LSTM assigns each variable with a unique hidden state making up a matrix $\mathbf{h}_t$, and the standard LSTM models all the variables with a shared hidden state $\mathbf{H}_t$.
Region-Enhanced Feature Learning for Scene Semantic Segmentation
We design a region-based feature enhancement (RFE) module, which consists of a Semantic-Spatial Region Extraction stage and a Region Dependency Modeling stage.
Semantic Human Parsing via Scalable Semantic Transfer over Multiple Label Domains
This paper presents Scalable Semantic Transfer (SST), a novel training paradigm, to explore how to leverage the mutual benefits of the data from different label domains (i. e. various levels of label granularity) to train a powerful human parsing network.
Generalized 3D Self-supervised Learning Framework via Prompted Foreground-Aware Feature Contrast
The second is that we prevent over-discrimination between 3D segments/objects and encourage grouped foreground-to-background distinctions at the segment level with adaptive feature learning in a Siamese correspondence network, which adaptively learns feature correlations within and across point cloud views effectively.
Semantics-Preserving Sketch Embedding for Face Generation
With a semantic feature matching loss for effective semantic supervision, our sketch embedding precisely conveys the semantics in the input sketches to the synthesized images.
Toward Unpaired Multi-modal Medical Image Segmentation via Learning Structured Semantic Consistency
Integrating multi-modal data to promote medical image analysis has recently gained great attention.
Dual Progressive Prototype Network for Generalized Zero-Shot Learning
This enables DPPN to produce visual representations with accurate attribute localization ability, which benefits the semantic-visual alignment and representation transferability.
Text-Aware Single Image Specular Highlight Removal
The core goal is to improve the accuracy of text detection and recognition by removing the highlight from text images.
Task-Independent Knowledge Makes for Transferable Representations for Generalized Zero-Shot Learning
First, DCEN leverages task labels to cluster representations of the same semantic category by cross-modal contrastive learning and exploring semantic-visual complementarity.
Scene text removal via cascaded text stroke detection and erasing
Specifically, we decouple the text removal problem into text stroke detection and stroke removal.
Cross-Modal Pattern-Propagation for RGB-T Tracking
Motivated by our observations on RGB-T data that pattern correlations are high-frequently recurred across modalities also along sequence frames, in this paper, we propose a cross-modal pattern-propagation (CMPP) tracking framework to diffuse instance patterns across RGB-T data on spatial domain as well as temporal domain.
Ranked #11 on
Rgb-T Tracking
on RGBT234
Domain-aware Visual Bias Eliminating for Generalized Zero-Shot Learning
Recent methods focus on learning a unified semantic-aligned visual representation to transfer knowledge between two domains, while ignoring the effect of semantic-free visual representation in alleviating the biased recognition problem.
HouseExpo: A Large-scale 2D Indoor Layout Dataset for Learning-based Algorithms on Mobile Robots
As one of the most promising areas, mobile robots draw much attention these years.
Robotics
