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- 2,618
- Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks1,874
This paper devise multiple variants of bottleneck building blocks in a residual learning framework by simulating 3 x3 x 3 convolutions with 1 × 3 × 3 convolutional filters on spatial domain (equivalent to 2D CNN) plus 3 × 1 × 1 convolutions to construct temporal connections on adjacent feature maps in time.
- Exploring Visual Relationship for Image Captioning933
This paper introduces a new design to explore the connections between objects for image captioning under the umbrella of attention-based encoder-decoder framework that novelly integrates both semantic and spatial object relationships into image encoder.
- Contextual Transformer Networks for Visual Recognition749
A novel Transformer-style module, i.e., Contextual Transformer (CoT) block, for visual recognition that fully capitalizes on the contextual information among input keys to guide the learning of dynamic attention matrix and thus strengthens the capacity of visual representation.
- Boosting Image Captioning with Attributes733
This paper presents Long Short-Term Memory with Attributes (LSTM-A) - a novel architecture that integrates attributes into the successful Convolutional Neural Networks plus Recurrent Neural Networks (RNNs) image captioning framework, by training them in an end-to-end manner.
- X-Linear Attention Networks for Image Captioning684
A unified attention block --- X-Linear attention block, that fully employs bilinear pooling to selectively capitalize on visual information or perform multi-modal reasoning is introduced.
- Jointly Modeling Embedding and Translation to Bridge Video and Language563
A novel unified framework, named Long Short-Term Memory with visual-semantic Embedding (LSTM-E), which can simultaneously explore the learning of LSTM and visual- semantic embedding and outperforms several state-of-the-art techniques in predicting Subject-Verb-Object (SVO) triplets.
- Transferrable Prototypical Networks for Unsupervised Domain Adaptation398
This paper presents Transferrable Prototypical Networks (TPN), which is end-to-end trained by jointly minimizing the distance across the prototypes on three types of data and KL-divergence of score distributions output by each pair of the prototypes.
- Fully Convolutional Adaptation Networks for Semantic Segmentation375
FCAN is presented, a novel deep architecture for semantic segmentation which combines Appearance Adaptation Networks (AAN) and Representation Adaptation networks (RAN), which learns a transformation from one domain to the other in the pixel space and RAN is optimized in an adversarial learning manner to maximally fool the domain discriminator with the learnt source and target representations.
- Gaussian Temporal Awareness Networks for Action Localization369
Gaussian Temporal Awareness Networks (GTAN) is presented --- a new architecture that novelly integrates the exploitation of temporal structure into an one-stage action localization framework and achieves superior results when comparing to state-of-the-art approaches.
- Video Captioning with Transferred Semantic Attributes364
This paper presents Long Short-Term Memory with Transferred Semantic Attributes (LSTM-TSA), a novel deep architecture that incorporates the transferred semantic attributes learnt from images and videos into the CNN plus RNN framework, by training them in an end-to-end manner.
- Exploring Object Relation in Mean Teacher for Cross-Domain Detection355
This work presents Mean Teacher with Object Relations (MTOR) that novelly remolds Mean Teacher under the backbone of Faster R-CNN by integrating the object relations into the measure of consistency cost between teacher and student modules.
- 330
- Memory Matching Networks for One-Shot Image Recognition310
Memory Matching Networks (MM-Net) is presented - a novel deep architecture that explores the training procedure, following the philosophy that training and test conditions must match, and improves one-shot accuracy on Omniglot and miniImageNet.
- 283
- Relation Distillation Networks for Video Object Detection238
Relation Distillation Networks is presented --- a new architecture that novelly aggregates and propagates object relation to augment object features for detection and achieves superior results when comparing to state-of-the-art methods.
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- Hierarchy Parsing for Image Captioning206
A new design to model a hierarchy from instance level, region level and region level to the whole image to delve into a thorough image understanding for captioning and is appealing in view that it is pluggable to any neural captioning models.
- Jointly Localizing and Describing Events for Dense Video Captioning199
This paper presents a novel framework for dense video captioning that unifies the localization of temporal event proposals and sentence generation of each proposal, by jointly training them in an end-to-end manner.
- Learning Spatio-Temporal Representation With Local and Global Diffusion198
A novel framework to boost the spatio-temporal representation learning by Local and Global Diffusion (LGD) is presented, which construct a novel neural network architecture that learns the local and global representations in parallel.
- Multi-Scale Triplet CNN for Person Re-Identification197
A multi-scale triplet convolutional neural network which captures visual appearance of a person at various scales is proposed, addressing the problem of small training set in person re-identification.
- To Create What You Tell180
A novel Temporal GANs conditioning on Captions, namely TGANs-C, in which the input to the generator network is a concatenation of a latent noise vector and caption embedding, and then is transformed into a frame sequence with 3D spatio-temporal convolutions.
- Learning Multimodal Attention LSTM Networks for Video Captioning175
This work presents a novel deep framework to boost video captioning by learning Multimodal Attention Long-Short Term Memory networks (MA-LSTM), and designs a novel child-sum fusion unit in the MA-L STM to effectively combine different encoded modalities to the initial decoding states.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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