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Works228 from public data
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- Scene graph captioner: Image captioning based on structural visual representation97
This work proposes a novel framework to embed a scene graph into the structural representation, which captures the semantic concepts and the graph topology and develops the scene-graph-driven method to generate the attention graph.
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- Coupled hidden conditional random fields for RGB-D human action recognition87
The extensive experiments show that the proposed model can boost the performance of human action recognition by taking advance of complementary characteristics from both RGB and depth modalities.
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- CAT-DM: Controllable Accelerated Virtual Try-On with Diffusion Model82
A basic diffusion-based virtual try-on network is designed, which utilizes ControlNet to introduce additional control conditions and improves the feature extraction of garment images and demonstrates the superiority of CAT-DM against both GAN-based and diffusion-based methods in producing more real-istic images and accurately reproduced garment patterns.
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- Graph-based characteristic view set extraction and matching for 3D model retrieval68
A novel method for characteristic view extraction and similarity measurement is proposed using the graph clustering method for view grouping and the random-walk algorithm is applied to adaptively update the weight of each view.
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- Hierarchical multi-view context modelling for 3D object classification and retrieval55
A hierarchical multi-view context modelling method for 3D object classification and retrieval that can imitate the human need to look back and forth to identify and compare the discriminative parts of individual 3D objects based on a joint convolutional neural network and bidirectional long short-term memory network.
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- Dual-level Embedding Alignment Network for 2D Image-Based 3D Object Retrieval46
This is the first unsupervised work to jointly realize cross-domain feature learning and distribution alignment in an end-to-end manner for this new task, 2D image-based 3D object retrieval.
- Human Action Recognition Based on Selected Spatio-Temporal Features via Bidirectional LSTM46
This paper proposes a novel framework that can select the discriminative part in the spatial dimension and enrich the modeling action of motion in the temporal dimension using multiple layers of a long short-term memory framework, which can learn compositional representations in space and time.
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 2026-10-14. 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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