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Found 17 papers, 2 papers with code
PNP: Robust Learning From Noisy Labels by Probabilistic Noise Prediction
Label noise has been a practical challenge in deep learning due to the strong capability of deep neural networks in fitting all training data.
Expansion-Squeeze-Excitation Fusion Network for Elderly Activity Recognition
This work focuses on the task of elderly activity recognition, which is a challenging task due to the existence of individual actions and human-object interactions in elderly activities.
Interactive Fusion of Multi-level Features for Compositional Activity Recognition
To understand a complex action, multiple sources of information, including appearance, positional, and semantic features, need to be integrated.
Data-driven Meta-set Based Fine-Grained Visual Classification
To this end, we propose a data-driven meta-set based approach to deal with noisy web images for fine-grained recognition.
Social Adaptive Module for Weakly-supervised Group Activity Recognition
This paper presents a new task named weakly-supervised group activity recognition (GAR) which differs from conventional GAR tasks in that only video-level labels are available, yet the important persons within each frame are not provided even in the training data.
Spatiotemporal Co-attention Recurrent Neural Networks for Human-Skeleton Motion Prediction
To this end, we propose a novel Skeleton-joint Co-attention Recurrent Neural Networks (SC-RNN) to capture the spatial coherence among joints, and the temporal evolution among skeletons simultaneously on a skeleton-joint co-attention feature map in spatiotemporal space.
Region-Manipulated Fusion Networks for Pancreatitis Recognition
Specifically, to effectively highlight the imperceptible lesion regions, a novel region-manipulated scheme in RMFN is proposed to force the lesion regions while weaken the non-lesion regions by ceaselessly aggregating the multi-scale local information onto feature maps.
Hierarchical Long Short-Term Concurrent Memory for Human Interaction Recognition
In a Co-LSTM unit, each sub-memory unit stores individual motion information, while this Co-LSTM unit selectively integrates and stores inter-related motion information between multiple interacting persons from multiple sub-memory units via the cell gate and co-memory cell, respectively.
Ranked #1 on
Human Interaction Recognition
on UT
Deep Ordinal Hashing with Spatial Attention
However, most existing deep hashing methods directly learn the hash functions by encoding the global semantic information, while ignoring the local spatial information of images.
Social Anchor-Unit Graph Regularized Tensor Completion for Large-Scale Image Retagging
Recent approaches simultaneously explore visual, user and tag information to improve the performance of image retagging by constructing and exploring an image-tag-user graph.
Face Aging with Contextual Generative Adversarial Nets
The age discriminative network guides the synthesized face to fit the real conditional distribution.
Personalized Age Progression with Bi-level Aging Dictionary Learning
Basically, for each age group, we learn an aging dictionary to reveal its aging characteristics (e. g., wrinkles), where the dictionary bases corresponding to the same index yet from two neighboring aging dictionaries form a particular aging pattern cross these two age groups, and a linear combination of all these patterns expresses a particular personalized aging process.
Concurrence-Aware Long Short-Term Sub-Memories for Person-Person Action Recognition
To this end, we propose a novel Concurrence-Aware Long Short-Term Sub-Memories (Co-LSTSM) to model the long-term inter-related dynamics between two interacting people on the bounding boxes covering people.
Ranked #2 on
Human Interaction Recognition
on BIT
Recurrent Face Aging
Modeling the aging process of human face is important for cross-age face verification and recognition.
Instance-Aware Hashing for Multi-Label Image Retrieval
The instance-aware representations not only bring advantages to semantic hashing, but also can be used in category-aware hashing, in which an image is represented by multiple pieces of hash codes and each piece of code corresponds to a category.
Task-Driven Feature Pooling for Image Classification
Feature pooling is an important strategy to achieve high performance in image classification.
Personalized Age Progression with Aging Dictionary
Second, it is challenging or even impossible to collect faces of all age groups for a particular subject, yet much easier and more practical to get face pairs from neighboring age groups.
