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Claim this profileWorks169 from public data
- Improving person re-identification by attribute and identity learning888
An attribute-person recognition (APR) network is proposed, a multi-task network which learns a re-ID embedding and at the same time predicts pedestrian attributes, and demonstrates that by learning a more discriminative representation, APR achieves competitive re-IDs performance compared with the state-of-the-art methods.
- BEATs: Audio Pre-Training with Acoustic Tokenizers758
BEATs, an iterative audio pre-training framework to learn Bidirectional Encoder representation from Audio Transformers, where an acoustic tokenizer and an audio SSL model are optimized by iterations, demonstrates that acoustic tokenizers can generate discrete labels with rich audio semantics and audio SSL models achieve state-of-the-art results across various audio classification benchmarks.
- A survey on LLM-based multi-agent systems: workflow, infrastructure, and challenges699
A comprehensive survey of LLM-based multi-agent systems is presented, offering a systematic review of these studies, and a general structure encompassing five key components: profile, perception, self-action, mutual interaction, and evolution is synthesized.
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- Gated Channel Transformation for Visual Recognition343
A generally applicable transformation unit for visual recognition with deep convolutional neural networks that explicitly models channel relationships with explainable control variables and is applicable to operator-level without much increase of additional parameters.
- Unsupervised Person Re-Identification via Softened Similarity Learning307
The iterative training mechanism is followed but clustering is discarded, since it incurs loss from hard quantization, yet its only product, image-level similarity, can be easily replaced by pairwise computation and a softened classification task.
- Auto-ReID: Searching for a Part-Aware ConvNet for Person Re-Identification285
This work proposes a retrieval-based search algorithm over a specifically designed reID search space, named Auto-ReID, which enables the automated approach to find an efficient and effective CNN architecture for reID.
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- Automated retinopathy of prematurity screening using deep neural networks171
The developed DeepROP is potential to be an efficient and effective system for automated ROP screening and can directly learn ROP features from big data.
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- Symbiotic Attention with Privileged Information for Egocentric Action Recognition69
This paper proposes a novel Symbiotic Attention framework leveraging Privileged information (SAP) for egocentric video recognition and introduces a novel symbiotic attention (SA) to enable effective communication that achieves the state-of-the-art on two large-scale egOCentric video datasets.
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- Visually-Prompted Language Model for Fine-Grained Scene Graph Generation in an Open World63
A Cross-modal prediCate boosting (CaCao) framework, where a visually-prompted language model is learned to generate diverse fine-grained predicates in a low-resource way for open-world predicate scene graph generation (Epic).
- Decoupled Novel Object Captioner63
The Decoupled Novel Object Captioner (DNOC) framework is proposed that can fully decouple the language sequence model from the object descriptions and the experimental results on the held-out MSCOCO dataset demonstrate the ability of DNOC in describing novel concepts.
- Automated detection of kidney abnormalities using multi-feature fusion convolutional neural networks62
An automated architecture to detect various kidney abnormalities is proposed that works on abdominal ultrasound images using convolutional neural networks and is combined with a weighted ensemble method to improve performance.
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- Automatic coronary artery segmentation and diagnosis of stenosis by deep learning based on computed tomographic coronary angiography59
A deep learning model that can automatically segment the coronary artery quickly and accurately and can deliver a diagnosis of ≥ 50% coronary artery stenosis is developed and used in the image postprocessing of coronary computed tomography angiography to achieve a quick and accurate diagnosis of CAD.
- Vision + X: A Survey on Multimodal Learning in the Light of Data52
This paper analyzes the commonness and uniqueness of each data format mainly ranging from vision, audio, text, and motions, and presents the methodological advancements categorized by the combination of data modalities, such as Vision+Text, with slightly inclined emphasis on the visual data.
- Audio classification using attention-augmented convolutional neural network52
A task-independent model is proposed, called FreqCNN, to automaticly extract distinctive features from each frequency band by using convolutional kernels and an attention mechanism is introduced to systematically enhance the features from certain frequency bands.
- Topic Augmented Neural Response Generation with a Joint Attention Mechanism.50
A topic augmented joint attention based Seq2Seq (TAJA-Seq 2Seq) model that simulates how people behave in conversation and can generate well-focused and informative responses with the help of topic information is proposed.
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- Cascaded Revision Network for Novel Object Captioning42
This paper proposes a novel network structure, Cascaded Revision Network, which aims at relieving the problem of inaccurate captioning by equipping the model with out-of-domain knowledge and can accurately describe images with unseen objects.
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- Neural Melody Composition from Lyrics39
A melody composition model that generates lyrics-conditional melody as well as the exact alignment between the generated melody and the given lyrics simultaneously is developed based on the sequence-to-sequence framework.
- Computer-Aided Clinical Skin Disease Diagnosis Using CNN and Object Detection Models38
The results show that object detection can help improve the accuracy of some skin disease classes and an object detection based approach by introducing bounding boxes into the Skin -10 dataset is presented.
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- Multi-query Video Retrieval25
This paper shows that multi-query retrieval task effectively mitigates the dataset noise introduced by imperfect annotations and better correlates with human judgement on evaluating retrieval abilities of current models, and investigates several methods which leverage multiple queries at training time.
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- Dual-feature Fusion Attention Network for Small Object Segmentation16
A dual feature fusion attention network (DFF-Net) to improve the segmentation accuracy of small objects and has fewer parameters, faster inference, and lower model complexity, and achieves better accuracy than more state-of-the-art methods.
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- DETER: Detecting Edited Regions for Deterring Generative Manipulations4
DETER is introduced, a large-scale dataset for DETEcting edited image Regions and deterring modern advanced generative manipulations, and it is confirmed that human deep fake detection rate on DETER is 20.4% lower than on other fake datasets.
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- Early gastric mucosa-associated lymphoid tissue lymphoma treated by endoscopic submucosal dissection2
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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 2026-10-13. 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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