Jiajun Song

Orcid: 0009-0005-5439-0176

According to our database1, Jiajun Song authored at least 15 papers between 2021 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Links

On csauthors.net:

Bibliography

2024
Multi-state Ingredient Recognition via Adaptive Multi-centric Network.
IEEE Trans. Ind. Informatics, April, 2024

Upconversion-Powered Photoelectrochemical Bioanalysis for DNA Sensing.
Sensors, February, 2024

Towards Food Image Retrieval via Generalization-Oriented Sampling and Loss Function Design.
ACM Trans. Multim. Comput. Commun. Appl., January, 2024

A 102.1-dB SNDR oversampling merge-mismatch-error-shaping SAR ADC in 180 nm CMOS.
Microelectron. J., January, 2024

Synthesizing Knowledge-Enhanced Features for Real-World Zero-Shot Food Detection.
IEEE Trans. Image Process., 2024

2023
Transcriptional correlates of frequency-dependent brain functional activity associated with symptom severity in degenerative cervical myelopathy.
NeuroImage, December, 2023

A Time-Domain Reconfigurable Second-Order Noise Shaping ADC With Single Fan-Out Gated Delay Cells.
IEEE Trans. Very Large Scale Integr. Syst., June, 2023

A Reconfigurable 12-to-18-Bit Dynamic Zoom ADC With Pole-Optimized Technique.
IEEE Trans. Circuits Syst. I Regul. Pap., May, 2023

Ingredient Prediction via Context Learning Network With Class-Adaptive Asymmetric Loss.
IEEE Trans. Image Process., 2023

Uncovering hidden geometry in Transformers via disentangling position and context.
CoRR, 2023

SeeDS: Semantic Separable Diffusion Synthesizer for Zero-shot Food Detection.
Proceedings of the 31st ACM International Conference on Multimedia, 2023

DAGC: Data-Aware Adaptive Gradient Compression.
Proceedings of the IEEE INFOCOM 2023, 2023

2022
A Noise-robust Locality Transformer for Fine-grained Food Image Retrieval.
Proceedings of the 5th IEEE International Conference on Multimedia Information Processing and Retrieval, 2022

Rethinking the Optimization of Average Precision: Only Penalizing Negative Instances before Positive Ones Is Enough.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021
Rethinking Ranking-based Loss Functions: Only Penalizing Negative Instances before Positive Ones is Enough.
CoRR, 2021


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