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Found 22 papers, 11 papers with code
Res2Net: A New Multi-scale Backbone Architecture
We evaluate the Res2Net block on all these models and demonstrate consistent performance gains over baseline models on widely-used datasets, e. g., CIFAR-100 and ImageNet.
Ranked #2 on
Image Classification
on GasHisSDB
Self-training with progressive augmentation for unsupervised cross-domain person re-identification
In this work, we develop a self-training method with progressive augmentation framework (PAST) to promote the model performance progressively on the target dataset.
Ranked #11 on
Unsupervised Domain Adaptation
on Market to Duke
Diversity Transfer Network for Few-Shot Learning
The learning problem of the sample generation (i. e., diversity transfer) is solved via minimizing an effective meta-classification loss in a single-stage network, instead of the generative loss in previous works.
Part-Guided Attention Learning for Vehicle Instance Retrieval
Finally, we aggregate the global appearance and part features to improve the feature performance further.
Exploit the potential of Multi-column architecture for Crowd Counting
To the best of our knowledge, PSNet is the first work to explicitly address scale limitation and feature similarity in multi-column design.
Semi-Supervised Learning with Meta-Gradient
In this work, we propose a simple yet effective meta-learning algorithm in semi-supervised learning.
A Design Methodology for Efficient Implementation of Deconvolutional Neural Networks on an FPGA
In support of such applications, various FPGA accelerator architectures have been proposed for convolutional neural networks (CNNs) that enable high performance for classification tasks at lower power than CPU and GPU processors.
DNA: Deeply-supervised Nonlinear Aggregation for Salient Object Detection
Recent progress on salient object detection mainly aims at exploiting how to effectively integrate multi-scale convolutional features in convolutional neural networks (CNNs).
Structured Sparsification with Joint Optimization of Group Convolution and Channel Shuffle
Recent advances in convolutional neural networks(CNNs) usually come with the expense of excessive computational overhead and memory footprint.
Learnable Cost Volume Using the Cayley Representation
Cost volume is an essential component of recent deep models for optical flow estimation and is usually constructed by calculating the inner product between two feature vectors.
Transferring Grasp Configurations using Active Learning and Local Replanning
We compute a grasp space for each part of the example object using active learning.
Robotics
AdaSample: Adaptive Sampling of Hard Positives for Descriptor Learning
The effectiveness of the triplet loss heavily relies on the triplet selection, in which a common practice is to first sample intra-class patches (positives) from the dataset for batch construction and then mine in-batch negatives to form triplets.
Adversarial AutoAugment
The augmentation policy network attempts to increase the training loss of a target network through generating adversarial augmentation policies, while the target network can learn more robust features from harder examples to improve the generalization.
Ranked #591 on
Image Classification
on ImageNet
Memorizing Comprehensively to Learn Adaptively: Unsupervised Cross-Domain Person Re-ID with Multi-level Memory
Due to the lack of supervision in the target domain, it is crucial to identify the underlying similarity-and-dissimilarity relationships among the unlabelled samples in the target domain.
DeepDualMapper: A Gated Fusion Network for Automatic Map Extraction using Aerial Images and Trajectories
We propose a deep convolutional neural network called DeepDualMapper which fuses the aerial image and trajectory data in a more seamless manner to extract the digital map.
Dual-discriminator GAN: A GAN way of profile face recognition
A wealth of angle problems occur when facial recognition is performed: At present, the feature extraction network presents eigenvectors with large differences between the frontal face and profile face recognition of the same person in many cases.
Towards Embodied Scene Description
Embodiment is an important characteristic for all intelligent agents (creatures and robots), while existing scene description tasks mainly focus on analyzing images passively and the semantic understanding of the scenario is separated from the interaction between the agent and the environment.
Dependency Aware Filter Pruning
Convolutional neural networks (CNNs) are typically over-parameterized, bringing considerable computational overhead and memory footprint in inference.
Error Bounds for Generalized Group Sparsity
In high-dimensional statistical inference, sparsity regularizations have shown advantages in consistency and convergence rates for coefficient estimation.
Safe Screening Rules for Generalized Double Sparsity Learning
In a high-dimensional setting, sparse model has shown its power in computational and statistical efficiency.
Optimization and Control Computation
Multi-Objective Model-based Reinforcement Learning for Infectious Disease Control
Severe infectious diseases such as the novel coronavirus (COVID-19) pose a huge threat to public health.
