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Data science
You’ve been invited to add the folder “Data science”, which includes 15 chats.
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Click-Calib: A Robust Extrinsic Calibration Method for Surround-View Systems
Surround-View System (SVS) is an essential component in Advanced Driver Assistance System (ADAS) and requires precise calibrations.
Paper: https://arxiv.org/pdf/2501.01557v2.pdf
Code: https://github.com/lwangvaleo/click_calib
Dataset: WoodScape
@Machine_learn
Surround-View System (SVS) is an essential component in Advanced Driver Assistance System (ADAS) and requires precise calibrations.
Paper: https://arxiv.org/pdf/2501.01557v2.pdf
Code: https://github.com/lwangvaleo/click_calib
Dataset: WoodScape
@Machine_learn
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📄 Deep Generative Models for Therapeutic Peptide Discovery: A Comprehensive Review
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📎 Study the paper
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Artificial Intelligence
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📄A Survey of Genetic Programming Applications in Modern Biological Research
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📃 Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development
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JanusFlow: Harmonizing Autoregression and Rectified Flow for Unified Multimodal Understanding and Generation
We present JanusFlow, a powerful framework that unifies image understanding and generation in a single model. JanusFlow introduces a minimalist architecture that integrates autoregressive language models with rectified flow, a state-of-the-art method in generative modeling. Our key finding demonstrates that rectified flow can be straightforwardly trained within the large language model framework, eliminating the need for complex architectural modifications. To further improve the performance of our unified model, we adopt two key strategies: (i) decoupling the understanding and generation encoders, and (ii) aligning their representations during unified training. Extensive experiments show that JanusFlow achieves comparable or superior performance to specialized models in their respective domains, while significantly outperforming existing unified approaches across standard benchmarks. This work represents a step toward more efficient and versatile vision-language models.
Paper: https://arxiv.org/pdf/2411.07975v1.pdf
Code: https://github.com/deepseek-ai/janus
Datasets: GQA MMBench MM-Vet SEED-Bench
@Machine_learn
We present JanusFlow, a powerful framework that unifies image understanding and generation in a single model. JanusFlow introduces a minimalist architecture that integrates autoregressive language models with rectified flow, a state-of-the-art method in generative modeling. Our key finding demonstrates that rectified flow can be straightforwardly trained within the large language model framework, eliminating the need for complex architectural modifications. To further improve the performance of our unified model, we adopt two key strategies: (i) decoupling the understanding and generation encoders, and (ii) aligning their representations during unified training. Extensive experiments show that JanusFlow achieves comparable or superior performance to specialized models in their respective domains, while significantly outperforming existing unified approaches across standard benchmarks. This work represents a step toward more efficient and versatile vision-language models.
Paper: https://arxiv.org/pdf/2411.07975v1.pdf
Code: https://github.com/deepseek-ai/janus
Datasets: GQA MMBench MM-Vet SEED-Bench
@Machine_learn
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