Search Results for author:
Found 31 papers, 7 papers with code
Disentangled Graph Neural Networks for Session-based Recommendation
The session embedding is then generated by aggregating the item embeddings with attention weights of each item's factors.
Weakly Supervised Temporal Sentence Grounding With Gaussian-Based Contrastive Proposal Learning
Moreover, they train their model to distinguish positive visual-language pairs from negative ones randomly collected from other videos, ignoring the highly confusing video segments within the same video.
Fine-Grained Image Analysis with Deep Learning: A Survey
Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition, and underpins a diverse set of real-world applications.
A New Benchmark and Approach for Fine-grained Cross-media Retrieval
To the best of our knowledge, it is the first benchmark with 4 media types for fine-grained cross-media retrieval.
Object-aware Aggregation with Bidirectional Temporal Graph for Video Captioning
The main novelties and advantages are: (1) Bidirectional temporal graph: A bidirectional temporal graph is constructed along and reversely along the temporal order, which provides complementary ways to capture the temporal trajectories for each salient object.
Text-to-image Synthesis via Symmetrical Distillation Networks
For addressing these problems, we exploit the excellent capability of generic discriminative models (e. g. VGG19), which can guide the training process of a new generative model on multiple levels to bridge the two gaps.
Better and Faster: Knowledge Transfer from Multiple Self-supervised Learning Tasks via Graph Distillation for Video Classification
Video representation learning is a vital problem for classification task.
Visual Data Synthesis via GAN for Zero-Shot Video Classification
First, we propose multi-level semantic inference to boost video feature synthesis, which captures the discriminative information implied in joint visual-semantic distribution via feature-level and label-level semantic inference.
Cross-media Multi-level Alignment with Relation Attention Network
First, we propose visual-language relation attention model to explore both fine-grained patches and their relations of different media types.
Deep Cross-media Knowledge Transfer
For achieving the goal, this paper proposes deep cross-media knowledge transfer (DCKT) approach, which transfers knowledge from a large-scale cross-media dataset to promote the model training on another small-scale cross-media dataset.
Multimedia
SCH-GAN: Semi-supervised Cross-modal Hashing by Generative Adversarial Network
(2) Ignore the rich information contained in the large amount of unlabeled data across different modalities, especially the margin examples that are easily to be incorrectly retrieved, which can help to model the correlations.
Deep Reinforcement Learning for Image Hashing
Inspired by the sequential decision ability of deep reinforcement learning, we propose a new Deep Reinforcement Learning approach for Image Hashing (DRLIH).
Unsupervised Generative Adversarial Cross-modal Hashing
To address the above problem, in this paper we propose an Unsupervised Generative Adversarial Cross-modal Hashing approach (UGACH), which makes full use of GAN's ability for unsupervised representation learning to exploit the underlying manifold structure of cross-modal data.
Two-stream Collaborative Learning with Spatial-Temporal Attention for Video Classification
Recently, researchers generally adopt the deep networks to capture the static and motion information \textbf{\emph{separately}}, which mainly has two limitations: (1) Ignoring the coexistence relationship between spatial and temporal attention, while they should be jointly modelled as the spatial and temporal evolutions of video, thus discriminative video features can be extracted.
CM-GANs: Cross-modal Generative Adversarial Networks for Common Representation Learning
They can not only exploit cross-modal correlation for learning common representation, but also preserve reconstruction information for capturing semantic consistency within each modality.
Fast Fine-grained Image Classification via Weakly Supervised Discriminative Localization
Therefore, we propose a weakly supervised discriminative localization approach (WSDL) for fast fine-grained image classification to address the two limitations at the same time, and its main advantages are: (1) n-pathway end-to-end discriminative localization network is designed to improve classification speed, which simultaneously localizes multiple different discriminative regions for one image to boost classification accuracy, and shares full-image convolutional features generated by region proposal network to accelerate the process of generating region proposals as well as reduce the computation of convolutional operation.
Fine-grained Discriminative Localization via Saliency-guided Faster R-CNN
Existing methods generally adopt a two-stage learning framework: The first stage is to localize the discriminative regions of objects, and the second is to encode the discriminative features for training classifiers.
Fine-grained Visual-textual Representation Learning
As is known to all, when we describe the object of an image via textual descriptions, we mainly focus on the pivotal characteristics, and rarely pay attention to common characteristics as well as the background areas.
Modality-specific Cross-modal Similarity Measurement with Recurrent Attention Network
Effectively measuring the similarity between different modalities of data is the key of cross-modal retrieval.
MHTN: Modal-adversarial Hybrid Transfer Network for Cross-modal Retrieval
Transfer learning is for relieving the problem of insufficient training data, but it mainly focuses on knowledge transfer only from large-scale datasets as single-modal source domain to single-modal target domain.
Fine-Grained Image Classification via Combining Vision and Language
Most existing fine-grained image classification methods generally learn part detection models to obtain the semantic parts for better classification accuracy.
Cross-modal Common Representation Learning by Hybrid Transfer Network
Knowledge in source domain cannot be directly transferred to both two different modalities in target domain, and the inherent cross-modal correlation contained in target domain provides key hints for cross-modal retrieval which should be preserved during transfer process.
Cross-media Similarity Metric Learning with Unified Deep Networks
Motivated by the strong ability of deep neural network in feature representation and comparison functions learning, we propose the Unified Network for Cross-media Similarity Metric (UNCSM) to associate cross-media shared representation learning with distance metric in a unified framework.
Fine-graind Image Classification via Combining Vision and Language
Most existing fine-grained image classification methods generally learn part detection models to obtain the semantic parts for better classification accuracy.
Object-Part Attention Model for Fine-grained Image Classification
Both are jointly employed to exploit the subtle and local differences for distinguishing the subcategories.
Saliency-guided video classification via adaptively weighted learning
Then two streams of 3D CNN are trained individually for raw frames and optical flow on salient areas, and another 2D CNN is trained for raw frames on non-salient areas.
Cross-modal Deep Metric Learning with Multi-task Regularization
The quadruplet ranking loss can model the semantically similar and dissimilar constraints to preserve cross-modal relative similarity ranking information.
Query-adaptive Image Retrieval by Deep Weighted Hashing
On the other hand, different hash bits actually contribute to the image retrieval differently, and treating them equally greatly affects the retrieval accuracy of image.
SSDH: Semi-supervised Deep Hashing for Large Scale Image Retrieval
(2) A semi-supervised deep hashing network is designed to extensively exploit both labeled and unlabeled data, in which we propose an online graph construction method to benefit from the evolving deep features during training to better capture semantic neighbors.
The Application of Two-level Attention Models in Deep Convolutional Neural Network for Fine-grained Image Classification
Our pipeline integrates three types of attention: the bottom-up attention that propose candidate patches, the object-level top-down attention that selects relevant patches to a certain object, and the part-level top-down attention that localizes discriminative parts.
Exhaustive and Efficient Constraint Propagation: A Semi-Supervised Learning Perspective and Its Applications
This paper presents a novel pairwise constraint propagation approach by decomposing the challenging constraint propagation problem into a set of independent semi-supervised learning subproblems which can be solved in quadratic time using label propagation based on k-nearest neighbor graphs.
