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- DRG: Dual Relation Graph for Human-Object Interaction Detection255
The proposed dual relation graph effectively captures discriminative cues from the scene to resolve ambiguity from local predictions and leads to favorable results compared to the state-of-the-art HOI detection algorithms on two large-scale benchmark datasets.
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- Flow-edge Guided Video Completion211
Both visual and quantitative results show that the new flow-based video completion algorithm compares favorably against the state-of-the-art algorithms.
- PSGAN: Pose and Expression Robust Spatial-Aware GAN for Customizable Makeup Transfer172
Pose and expression robust Spatial-aware GAN (PSGAN) is proposed, which not only achieves state-of-the-art results even when large pose and expression differences exist but also is able to perform partial and shade-controllable makeup transfer.
- AdversarialNAS: Adversarial Neural Architecture Search for GANs111
The AdversarialNAS is the first method that can search the architectures of generator and discriminator simultaneously in a differentiable manner and only takes 1 GPU day to search for a superior generative model in the proposed large search space.
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- Towards Vehicle-to-everything Autonomous Driving: A Survey on Collaborative Perception94
A comprehensive review of CP methods for V2X scenarios, bringing a profound and in-depth understanding to the community, and examines the sim-to-real generalization ability of existing CP methods.
- Target-Driven Structured Transformer Planner for Vision-Language Navigation89
This article devise an Imaginary Scene Tokenization mechanism for explicit estimation of the long-term target (even located in unexplored environments) and design a Structured Transformer Planner which elegantly incorporates the explored room layout into a neural attention architecture for structured and global planning.
- NAS-DIP: Learning Deep Image Prior with Neural Architecture Search64
This work proposes to search for neural architectures that capture stronger image priors by leveraging an existing neural architecture search algorithm (using reinforcement learning with a recurrent neural network controller) and designs new search spaces for an upsampling cell and a pattern of cross-scale residual connections.
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- Reinforced Structured State-Evolution for Vision-Language Navigation60
This paper proposes a novel Structured state-Evolution (SEvol) model, which utilises the graph-based feature to represent the navigation state instead of the vector-based state, and devise a Reinforced Layout clues Miner (RLM) to mine and detect the most crucial layout graph for long-term navigation via a customised reinforcement learning strategy.
- NTIRE 2023 Challenge on Light Field Image Super-Resolution: Dataset, Methods and Results52
The first NTIRE challenge on light field (LF) image super-resolution (SR), which aims at super-resolving LF images under the standard bicubic degradation with a magnification factor of 4, develops a new LF dataset called NTIRE-2023 for validation and test, and provides a toolbox called BasicLFSR to facilitate model development.
- PSGAN++: Robust Detail-Preserving Makeup Transfer and Removal45
Experiments demonstrate that PSGAN++ not only achieves state-of-the-art results with fine makeup details even in cases of large pose/expression differences but also can perform partial or degree-controllable makeup transfer.
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- Identifying Vulnerable Households Using Machine Learning27
It is argued that long-term household characteristics, such as dwelling wall composition, are reflective of accumulated household wealth and this supports the idea that some factors determining food security are persistent.
- InteractGAN: Learning to Generate Human-Object Interaction26
This work introduces an Interact-GAN to solve the new Human-Object Interaction Image Generation problem and proposes a Relation-based Spatial Transformer Network (RSTN) to adaptively process the images conditioned on their interaction.
- Eliminating Cross-modal Conflicts in BEV Space for LiDAR-Camera 3D Object Detection25
A novel Eliminating Conflicts Fusion method to explicitly eliminate the extrinsic/inherent conflicts in BEV space and produce improved multi-modal BEV features and achieves state-of-the-art performance in the highly competitive nuScenes 3D object detection dataset.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. 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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