Search Results for author:
Found 11 papers, 5 papers with code
FACT: Feature Adaptive Continual-learning Tracker for Multiple Object Tracking
Specifically, we develop the feature adaptive continual-learning (FAC) module, a neural network that can be trained online to learn features adaptively using all past tracking information during tracking.
Dual Branch Network Towards Accurate Printed Mathematical Expression Recognition
Over the past years, Printed Mathematical Expression Recognition (PMER) has progressed rapidly.
POAR: Towards Open Vocabulary Pedestrian Attribute Recognition
Our key idea is to formulate the POAR problem as an image-text search problem.
GKEAL: Gaussian Kernel Embedded Analytic Learning for Few-Shot Class Incremental Task
In this paper, we approach the FSCIL by adopting analytic learning, a technique that converts network training into linear problems.
ACIL: Analytic Class-Incremental Learning with Absolute Memorization and Privacy Protection
Class-incremental learning (CIL) learns a classification model with training data of different classes arising progressively.
Region-aware Attention for Image Inpainting
Recent attention-based image inpainting methods have made inspiring progress by modeling long-range dependencies within a single image.
Multi-Teacher Knowledge Distillation for Incremental Implicitly-Refined Classification
However, distilling knowledge from two teacher models could result in the student model making some redundant predictions.
Structure-aware Image Inpainting with Two Parallel Streams
Recent works in image inpainting have shown that structural information plays an important role in recovering visually pleasing results.
Online Hashing with Similarity Learning
In the proposed framework, the hash functions are fixed and a parametric similarity function for the binary codes is learnt online to adapt to the streaming data.
Fast Search on Binary Codes by Weighted Hamming Distance
In this paper, a fast search algorithm is proposed to perform the non-exhaustive search for $K$ nearest binary codes by weighted Hamming distance.
Efficient Querying from Weighted Binary Codes
In our method, based on the multi-index hash tables, two algorithms, the table bucket finding algorithm and the table merging algorithm, are proposed to select the nearest weighted binary codes of the query in a non-exhaustive and accurate way.
