Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks151 from public data
- 472
- Look, Imagine and Match: Improving Textual-Visual Cross-Modal Retrieval with Generative Models420
This work proposes to incorporate generative processes into the cross-modal feature embedding, through which it is able to learn not only the global abstract features but also the local grounded features of image-text pairs.
- 322
- Unpaired Image Captioning via Scene Graph Alignments199
This paper proposes an unsupervised feature alignment method that maps the scene graph features from the image to the sentence modality and can generate quite promising results without using any image-caption training pairs, outperforming existing methods by a wide margin.
- 199
- Stack-Captioning: Coarse-to-Fine Learning for Image Captioning194
A coarse-to-fine multi-stage prediction framework for image captioning, composed of multiple decoders each of which operates on the output of the previous stage, producing increasingly refined image descriptions, which simultaneously solves the well-known exposure bias problem and the loss-evaluation mismatch problem.
- 182
- Graininess-Aware Deep Feature Learning for Pedestrian Detection168
This paper proposes a graininess-aware deep feature learning method for pedestrian detection which encodes fine-grained attention masks into convolutional feature maps, which significantly suppresses background interference and highlights pedestrians.
- Learning Common and Specific Features for RGB-D Semantic Segmentation with Deconvolutional Networks166
The proposed feature transformation network achieves competitive segmentation accuracy on NYU depth dataset V1 and V2 and takes advantage of deconvolutional networks which can predict pixel-wise class labels, and develops a new structure for deconvolved of multiple modalities.
- An Empirical Study of Language CNN for Image Captioning159
This paper introduces a language CNN model which is suitable for statistical language modeling tasks and shows competitive performance in image captioning, and is competitive with the state-of-the-art methods.
- Knowledge Distillation via the Target-aware Transformer158
This work proposes a novel one-to-all spatial matching knowledge distillation approach that surpasses the state-of-the-art methods by a significant margin on various computer vision benchmarks, such as ImageNet, Pascal VOC and COCOStuff10k.
- 154
- 145
- EdgeYOLO: An Edge-Real-Time Object Detector128
An efficient, low-complexity, and anchor-free object detector based on the state-of-the-art YOLO framework is proposed, which can be implemented in real time on edge computing platforms and a hybrid random loss function is designed to improve the detection accuracy of small objects.
- Learning Contextual Dependence With Convolutional Hierarchical Recurrent Neural Networks124
This paper integrates CNNs with HRNNs, and develops end-to-end convolutional hierarchical RNNs (C-HRNNs) for image classification, which not only utilize the discriminative representation power of CNNs, but also utilize the contextual dependence learning ability of the authors' HRnns.
- 102
- Learning Discriminative and Shareable Features for Scene Classification94
The experimental results on three challenging scene image classification datasets indicate that the learned filters can achieve very promising performance, and show great complementary effect to the state-of-the-art ConvNets feature.
- Molecular cloning and identification of a flavanone 3-hydroxylase gene from Lycium chinense, and its overexpression enhances drought stress in tobacco85
It is suggested that LcF3H plays a role in enhancing drought tolerance in L. chinense, and its overexpression increases tolerance to drought stress by improving the antioxidant system in tobacco.
- Online latent semantic hashing for cross-media retrieval81
This paper proposes an online optimization scheme towards the challenging task of learning hash functions efficiently on streaming data points, and the computational complexity and memory cost are much less than the size of training dataset at each round.
- Over-Expression of LcPDS, LcZDS, and LcCRTISO, Genes From Wolfberry for Carotenoid Biosynthesis, Enhanced Carotenoid Accumulation, and Salt Tolerance in Tobacco69
This study indicated that LcPDS, LcZDS, and LcCRTISO have the potential to improve carotenoid content and salt tolerance in higher plant breeding.
- Exemplar based Deep Discriminative and Shareable Feature Learning for scene image classification69
A new local feature learning approach named Deep Discriminative and Shareable Feature Learning (DDSFL), which aims to hierarchically learn feature transformation filter banks to transform raw pixel image patches to features and shows great complementary effect to the state-of-the-art Caffe features.
- The effect of pds gene silencing on chloroplast pigment composition, thylakoid membrane structure and photosynthesis efficiency in tobacco plants69
Observations suggest the exact matching of pigments and proteins is important for energy absorption and transfer in photosynthesis and mismatching of them could cause malfunction of PSII.
- Phytoremediation of DEHP and heavy metals co-contaminated soil by rice assisted with a PGPR consortium: Insights into the regulation of ion homeostasis, improvement of photosynthesis and enrichment of beneficial bacteria in rhizosphere soil68
The combined use of rice and the PGPR consortium could remedy DEHP and heavy metal co-contaminated soil economically and ecologically without simultaneously posing risks for rice consumption.
- 59
- 58
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 2026-10-12. 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.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.