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Works134 from public data
- 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 Attributes732
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 Captioning683
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.
- 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.
- Memory Matching Networks for One-Shot Image Recognition309
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.
- 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.
- 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.
- Dual Vision Transformer174
This paper proposes a novel Transformer architecture that elegantly exploits the global semantics for self-attention learning, namely Dual Vision Transformer (Dual-ViT).
- Incorporating Copying Mechanism in Image Captioning for Learning Novel Objects155
A new architecture that incorporates copying into the Convolutional Neural Networks plus Recurrent Neural Networks (RNN) image captioning framework, for describing novel objects in captions, and superior results are reported when compared to state-of-the-art deep models.
- HiDream-I1: A High-Efficient Image Generative Foundation Model with Sparse Diffusion Transformer152
HiDream-I1 is introduced, a new open-source image generative foundation model with 17B parameters that achieves state-of-the-art image generation quality within seconds and evolves to form a comprehensive image agent (HiDream-A1) capable of fully interactive image creation and refinement.
- Semantic-Conditional Diffusion Networks for Image Captioning*140
The deeply rooted conventions in learning Transformer-based encoder-decoder are broken, and a new diffusion model based paradigm tailored for image captioning, namely Semantic-Conditional Diffusion Networks (SCD-Net) is proposed.
- Comprehending and Ordering Semantics for Image Captioning128
A new recipe of Transformer-style structure is proposed, namely Comprehending and Ordering Semantics Networks (COS-Net), that novelly unifies an enriched semantic comprehending and a learnable semantic ordering processes into a single architecture.
- SeCo: Exploring Sequence Supervision for Unsupervised Representation Learning126
This paper compositions a trilogy of exploring the basic and generic supervision in the sequence from spatial, spatiotemporal and sequential perspectives, and uniquely regard the signals as the foundation in contrastive learning and derive a particular form named Sequence Contrastive Learning (SeCo).
- Temporal Deformable Convolutional Encoder-Decoder Networks for Video Captioning109
A novel design --- Temporal Deformable Convolutional Encoder-Decoder Networks (dubbed as TDConvED) that fully employ convolutions in both encoder and decoder networks for video captioning and increases CIDEr-D performance from 58.8% to 67.2% on MSVD.
- Exploring Structure-aware Transformer over Interaction Proposals for Human-Object Interaction Detection104
This work designs a novel Transformer-style HOI detector, i.e., Structure-aware Transformer over Interaction Proposals (STIP), for HOI detection that upgrades vanilla Transformer by encoding additionally the holistically semantic structure among interaction proposals as well as the locally spatial structure of human/object within each interaction proposal, so as to strengthen HOI predictions.
- Mocycle-GAN101
A new Motion-guided Cycle GAN, dubbed as Mocycle-GAN, that novelly integrates motion estimation into unpaired video translator that capitalizes on three types of constrains: adversarial constraint discriminating between synthetic and real frame, cycle consistency encouraging an inverse translation on both frame and motion, and motion translation validating the transfer of motion between consecutive frames.
- ObjectFusion: Multi-modal 3D Object Detection with Object-Centric Fusion88
This paper proposes a novel Object-centric Fusion (ObjectFusion) paradigm, which completely gets rid of camera-to-BEV transformation during fusion to align object-centric features across different modalities for 3D object detection.
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 2026-10-16. 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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