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Found 9 papers, 5 papers with code
REPS: Reconstruction-based Point Cloud Sampling
Our method outperforms previous approaches in preserving the structural features of the sampled point clouds.
A Geometric Algorithm for Tubular Shape Reconstruction from Skeletal Representation
We introduce a novel approach for the reconstruction of tubular shapes from skeletal representations.
Low Resolution Information Also Matters: Learning Multi-Resolution Representations for Person Re-Identification
As a prevailing task in video surveillance and forensics field, person re-identification (re-ID) aims to match person images captured from non-overlapped cameras.
Deep High-Resolution Representation Learning for Cross-Resolution Person Re-identification
Compared with the state-of-the-art methods, our proposed PS-HRNet improves 3. 4\%, 6. 2\%, 2. 5\%, 1. 1\% and 4. 2\% at Rank-1 on MLR-Market-1501, MLR-CUHK03, MLR-VIPeR, MLR-DukeMTMC-reID, and CAVIAR datasets, respectively.
TIPCB: A Simple but Effective Part-based Convolutional Baseline for Text-based Person Search
Text-based person search is a sub-task in the field of image retrieval, which aims to retrieve target person images according to a given textual description.
Ranked #11 on
Text based Person Retrieval
on CUHK-PEDES
Reference-Aided Part-Aligned Feature Disentangling for Video Person Re-Identification
Due to the inaccurate person detections and pose changes, pedestrian misalignment significantly increases the difficulty of feature extraction and matching.
Hybrid-Attention Guided Network with Multiple Resolution Features for Person Re-Identification
Finally, we reconstruct the feature extractor to ensure that our model can obtain more richer and robust features.
Cascade Convolutional Neural Network for Image Super-Resolution
With the development of the super-resolution convolutional neural network (SRCNN), deep learning technique has been widely applied in the field of image super-resolution.
Ranked #1 on
Image Super-Resolution
on BSD200 - 2x upscaling
StaticGreedy: solving the scalability-accuracy dilemma in influence maximization
We point out that the essential reason of the dilemma is the surprising fact that the submodularity, a key requirement of the objective function for a greedy algorithm to approximate the optimum, is not guaranteed in all conventional greedy algorithms in the literature of influence maximization.
Social and Information Networks Data Structures and Algorithms Physics and Society F.2.2; D.2.8
