Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks189 from public data
- Large-scale visual sentiment ontology and detectors using adjective noun pairs862
This work presents a method built upon psychological theories and web mining to automatically construct a large-scale Visual Sentiment Ontology (VSO) consisting of more than 3,000 Adjective Noun Pairs (ANP) and proposes SentiBank, a novel visual concept detector library that can be used to detect the presence of 1,200 ANPs in an image.
- Executing your Commands via Motion Diffusion in Latent Space766
The proposed Motion Latent-based Diffusion model (MLD) could produce vivid motion sequences conforming to the given conditional inputs and substantially reduce the computational overhead in both the training and inference stages.
- An epidermal sEMG tattoo-like patch as a new human–machine interface for patients with loss of voice145
The design of an imperceptible, flexible epidermal sEMG tattoo-like patch with the thickness of less than 10 μm and peeling strength of larger than 1 N cm−1 that exhibits large adhesiveness to complex biological surfaces and is thus capable of s EMG recording for silent speech recognition is demonstrated.
- Cascade EF-GAN: Progressive Facial Expression Editing With Local Focuses119
The introduction of the local focus enables the Cascade EF-GAN to better preserve identity-related features and details around eyes, noses and mouths, which further helps reduce artifacts and blurs within the generated facial images.
- 113
- End-to-End 3D Dense Captioning with Vote2Cap-DETR112
Without bells and whistles, extensive experiments on two commonly used datasets, ScanRefer and Nr3D, demonstrate that the proposed Vote2Cap-DETR surpasses current state-of-the-arts by 11.13% and 7.11% in CIDEr@0.5IoU, respectively.
- 111
- Internet visual media processing: a survey with graphics and vision applications89
Recent research as regards processing of large collections of images and video, including work on analysis, manipulation, and synthesis is surveyed, and possible future directions in this emerging research area are suggested.
- Word Embedding Composition for Data Imbalances in Sentiment and Emotion Classification88
This paper presents an oversampling method based on word embedding compositionality which produces meaningful balanced training data and achieves improved results for both sentiment and emotion classification.
- Landmark recognition with compact BoW histogram and ensemble ELM87
A landmark recognition framework is proposed by employing a novel discriminative feature selection method and the improved extreme learning machine (ELM) algorithm to generate a set of preliminary codewords for landmark images.
- 83
- 77
- BerDiff: Conditional Bernoulli Diffusion Model for Medical Image Segmentation74
The proposed BerDiff uses the Bernoulli noise as the diffusion kernel to enhance the capacity of the diffusion model for binary segmentation tasks, resulting in more accurate segmentation masks and suggests diffusion models could serve as a strong backbone for medical image segmentation.
- Curriculum-Style Local-to-Global Adaptation for Cross-Domain Remote Sensing Image Segmentation74
Experimental results demonstrate that the proposed curriculum-style local-to-global cross-domain adaptation framework can significantly boost the domain adaptability of segmentation networks for VHR RSIs.
- M$^3$Lung-Sys: A Deep Learning System for Multi-Class Lung Pneumonia Screening From CT Imaging71
The quantitative results with plenty of metrics indicate the superiority of the proposed model on both slice- and patient-level classification tasks, and the generated lesion location maps make the system interpretable and more valuable to clinicians.
- Towards a comprehensive computational model foraesthetic assessment of videos69
A novel aesthetic model emphasizing psycho-visual statistics extracted from multiple levels in contrast to earlier approaches that rely only on descriptors suited for image recognition or based on photographic principles that demonstrates strong correlation with human prediction on 1,000 broadcast quality videos released by NHK as an aesthetic evaluation dataset.
- 64
- 63
- ShapeGPT: 3D Shape Generation With a Unified Multi-Modal Language Model62
This article presents ShapeGPT, a shape-included multi-modal framework to leverage strong pre-trained language models to address multiple shape-relevant tasks, and demonstrates that ShapeGPT achieves comparable performance across shape-relevant tasks, including text-to-shape, shape-to-text, shape completion, and shape editing.
- 61
- 60
- A Large-Scale Outdoor Multi-modal Dataset and Benchmark for Novel View Synthesis and Implicit Scene Reconstruction58
A large-scale outdoor multi-modal dataset, OMMO dataset, containing complex objects and scenes with calibrated images, point clouds and prompt annotations is proposed, establishing a new benchmark for several outdoor NeRF-based tasks, such as novel view synthesis, diverse 3D representation, and multi-modal NeRF.
- Vote2Cap-DETR++: Decoupling Localization and Describing for End-to-End 3D Dense Captioning55
This paper proposes Vote2Cap-DETR, a simple-yet-effective transformer framework that decouples the decoding process of caption generation and object localization through parallel decoding, and proposes an advanced version, Vote2Cap-DETR++, which decouples the queries into localization and caption queries to capture task-specific features.
- Uni3D: A Unified Baseline for Multi-Dataset 3D Object Detection55
A Uni3D is presented which leverages a simple data-level correction operation and a designed semantic-level coupling-and-recoupling module to alleviate the unavoidable data- level and taxonomy-level differences, respectively.
- 55
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.