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Found 20 papers, 12 papers with code
XoFTR: Cross-modal Feature Matching Transformer
We introduce, XoFTR, a cross-modal cross-view method for local feature matching between thermal infrared (TIR) and visible images.
Knowledge Distillation Layer that Lets the Student Decide
Albeit useful especially in the penultimate layer and beyond, its action on student's feature transform is rather implicit, limiting its practice in the intermediate layers.
Generalized Sum Pooling for Metric Learning
GSP improves GAP with two distinct abilities: i) the ability to choose a subset of semantic entities, effectively learning to ignore nuisance information, and ii) learning the weights corresponding to the importance of each entity.
Generalizable Embeddings with Cross-batch Metric Learning
Global average pooling (GAP) is a popular component in deep metric learning (DML) for aggregating features.
MAEVI: Motion Aware Event-Based Video Frame Interpolation
Utilization of event-based cameras is expected to improve the visual quality of video frame interpolation solutions.
Feature Embedding by Template Matching as a ResNet Block
Convolution blocks serve as local feature extractors and are the key to success of the neural networks.
E-VFIA : Event-Based Video Frame Interpolation with Attention
E-VFIA fuses event information with standard video frames by deformable convolutions to generate high quality interpolated frames.
Deep Metric Learning with Chance Constraints
Deep metric learning (DML) aims to minimize empirical expected loss of the pairwise intra-/inter- class proximity violations in the embedding space.
Improved Hard Example Mining Approach for Single Shot Object Detectors
Hard example mining methods generally improve the performance of the object detectors, which suffer from imbalanced training sets.
ASAP DML: Deep Metric Learning with Alternating Sets of Alternating Proxies
Deep metric learning (DML) aims to minimize empirical expected loss of the pairwise intra-/inter- class proximity violations in the embedding image.
Effect of Parameter Optimization on Classical and Learning-based Image Matching Methods
After a fair comparison, the experimental results on HPatches dataset reveal that the performance gap between classical and learning-based methods is not that significant.
DFM: A Performance Baseline for Deep Feature Matching
A novel image matching method is proposed that utilizes learned features extracted by an off-the-shelf deep neural network to obtain a promising performance.
Semi-Automatic Annotation For Visual Object Tracking
For detection, we use an off-the-shelf object detector which is trained iteratively with the annotations generated by the proposed method, and we perform object detection on each frame independently.
Blind Deinterleaving of Signals in Time Series with Self-attention Based Soft Min-cost Flow Learning
We propose an end-to-end learning approach to address deinterleaving of patterns in time series, in particular, radar signals.
Late Temporal Modeling in 3D CNN Architectures with BERT for Action Recognition
In this work, we combine 3D convolution with late temporal modeling for action recognition.
Ranked #1 on
Action Recognition
on UCF 101
Effect of Annotation Errors on Drone Detection with YOLOv3
Following the recent advances in deep networks, object detection and tracking algorithms with deep learning backbones have been improved significantly; however, this rapid development resulted in the necessity of large amounts of annotated labels.
Quadruplet Selection Methods for Deep Embedding Learning
Recognition of objects with subtle differences has been used in many practical applications, such as car model recognition and maritime vessel identification.
Deep Metric Learning with Alternating Projections onto Feasible Sets
The feasible set induced by the constraint set is expressed as the intersection of the relaxed feasible sets which enforce the proximity constraints only for particular samples (a sample from each class) of the training data.
Good Features to Correlate for Visual Tracking
The proposed learning framework enables the network model to be flexible for a custom design.
Ranked #2 on
Visual Object Tracking
on VOT2016
Efficient MRF Energy Propagation for Video Segmentation via Bilateral Filters
Depending on the application, automatic or interactive methods are desired; however, regardless of the application type, efficient computation of video object segmentation is crucial for time-critical applications; specifically, mobile and interactive applications require near real-time efficiencies.
