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Works172 from public data
- PAD-Net: Multi-tasks Guided Prediction-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing582
This paper proposes a novel multi-task guided prediction-and-distillation network (PAD-Net), which first predicts a set of intermediate auxiliary tasks ranging from low level to high level, and then the predictions from these intermediate Auxiliary tasks are utilized as multi-modal input via the authors' proposed multi- modal distillation modules for the final tasks.
- GS-SLAM: Dense Visual SLAM with 3D Gaussian Splatting571
GS-SLAM is introduced that first utilizes 3D Gaussian representation in the Simultaneous Localization and Mapping (SLAM) system and proposes an adaptive expansion strategy that adds new or deletes noisy 3D Gaussians in order to efficiently reconstruct new observed scene geometry and improve the mapping of pre-viously observed areas.
- Learning Deep Representations of Appearance and Motion for Anomalous Event Detection550
This work proposes Appearance and Motion DeepNet (AMDN) which utilizes deep neural networks to automatically learn feature representations, and introduces a novel double fusion framework, combining both the benefits of traditional early fusion and late fusion strategies.
- Detecting anomalous events in videos by learning deep representations of appearance and motion474
A novel double fusion framework is introduced, combining the benefits of traditional early fusion and late fusion strategies, which is extensively evaluated on publicly available video surveillance datasets including UCSD pedestian, Subway, and Train, showing competitive performance with respect to state of the art approaches.
- Multi-scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation466
This paper addresses the problem of depth estimation from a single still image by designing a novel CNN implementation of mean-field updates for continuous CRFs and demonstrates the effectiveness of the proposed approach and establishes new state of the art results on publicly available datasets.
- Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation348
This paper introduces a novel approach for monocular depth estimation which is competitive with previous methods on the KITTI benchmark and outperforms the state of the art on the NYU Depth V2 dataset.
- Multi-class Token Transformer for Weakly Supervised Semantic Segmentation321
A Multi-class Token Transformer, termed as MCTformer, which uses multiple class tokens to learn interactions between the class tokens and the patch tokens, which can successfully produce class-discriminative object localization maps from the class-to-patch attentions corresponding to different class tokens.
- Delving into Localization Errors for Monocular 3D Object Detection309
This work quantifies the impact introduced by each sub-task and found the ‘localization error’ is the vital factor in restricting monocular 3D detection, and investigates the underlying reasons behind localization errors.
- AttentionGAN: Unpaired Image-to-Image Translation Using Attention-Guided Generative Adversarial Networks292
A new Attention-Guided Generative Adversarial Networks (AttentionGAN) is proposed for the unpaired image-to-image translation task which can identify the most discriminative semantic objects and minimize changes of unwanted parts for semantic manipulation problems without using extra data and models.
- Depth-Aware Generative Adversarial Network for Talking Head Video Generation260
A self-supervised face-depth learning method to automatically recover dense 3D facial geometry (i.e. depth) from the face videos without the requirement of any expensive 3D annotation data is introduced.
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- Multi-Channel Attention Selection GAN With Cascaded Semantic Guidance for Cross-View Image Translation239
A novel approach named Multi-Channel Attention SelectionGAN (SelectionGAN) that makes it possible to generate images of natural scenes in arbitrary viewpoints, based on an image of the scene and a novel semantic map, using a multi-channel attention selection mechanism.
- Learning Cross-Modal Deep Representations for Robust Pedestrian Detection237
The proposed approach outperforms the state-of-the-art on the challenging KAIST multispectral pedestrian dataset and it is competitive with previous methods on the popular Caltech dataset.
- Unsupervised Adversarial Depth Estimation Using Cycled Generative Networks193
A novel unsupervised deep learning approach for predicting depth maps and showing that the depth estimation task can be effectively tackled within an adversarial learning framework is presented.
- Leveraging Auxiliary Tasks with Affinity Learning for Weakly Supervised Semantic Segmentation175
This work proposes a novel weakly supervised multi-task framework termed as AuxSegNet, to leverage saliency detection and multi-label image classification as auxiliary tasks to improve the primary task of semantic segmentation using only image-level ground-truth labels.
- Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation161
This work considers learning the scene generation in a local context, and correspondingly design a local class-specific generative network with semantic maps as a guidance, which separately constructs and learns sub-generators concentrating on the generation of different classes and is able to provide more scene details.
- Dynamic Graph Message Passing Networks157
A dynamic graph message passing network, that significantly reduces the computational complexity compared to related works modelling a fully-connected graph by adaptively sampling nodes in the graph, conditioned on the input, for message passing.
- Attention-Guided Generative Adversarial Networks for Unsupervised Image-to-Image Translation151
This paper proposes a novel Attention-Guided Generative Adversarial Network (AGGAN), which can detect the most discriminative semantic object and minimize changes of unwanted part for semantic manipulation problems without using extra data and models.
- DetCLIPv2: Scalable Open-Vocabulary Object Detection Pre-training via Word-Region Alignment137
An efficient and scalable training framework that incorporates large-scale imagetext pairs to achieve open-vocabulary object detection (OVD) and is trained with a hybrid supervision from detection, grounding and image-text pair data under a unified data formulation.
- Learning Deep Structured Multi-Scale Features using Attention-Gated CRFs for Contour Prediction134
A hierarchical deep model is introduced which produces more rich and complementary representations and the novel Attention-Gated Conditional Random Fields (AG-CRFs) are proposed to refine and robustly fuse the representations learned at different scales.
- Cross-modal Consensus Network for Weakly Supervised Temporal Action Localization115
This work argues that the features extracted from the pre-trained extractors are not specific for WS-TAL task, leading to inevitable redundancy and sub-optimization, and proposes a cross-modal consensus network (CO2-Net) to tackle this problem.
- Monocular Depth Estimation Using Multi-Scale Continuous CRFs as Sequential Deep Networks112
This paper addresses the problem of monocular depth estimation from a single still image by designing a novel CNN implementation of mean-field updates for continuous CRFs and proposes two different variations, one based on a cascade of multiple CRFs, the other on a unified graphical model.
- Video anomaly detection based on a hierarchical activity discovery within spatio-temporal contexts107
A novel approach for video-anomaly detection in crowded and complicated scenes based on a hierarchical activity-pattern discovery framework, comprehensively considering both global and local spatio-temporal contexts is presented.
- Uncertainty-Aware Contrastive Distillation for Incremental Semantic Segmentation106
A novel distillation framework, Uncertainty-aware Contrastive Distillation, that takes into account all the images in a mini-batch, enforcing similarity between features associated to all the pixels from the same classes, and pulling apart those corresponding to pixels from different classes is proposed.
- GestureGAN for Hand Gesture-to-Gesture Translation in the Wild99
Extensive experiments demonstrate that the proposed GestureGAN achieves state-of-the-art performance on the unconstrained hand gesture-to-gesture translation task and the generated images are in high-quality and are photo-realistic, allowing them to be used as data augmentation to improve the performance of a hand gesture classifier.
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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