171 research outputs found
When Sparsity Meets Dynamic Convolution
Dynamic convolution achieves a substantial performance boost for efficient
CNNs at a cost of increased convolutional weights. Contrastively, mask-based
unstructured pruning obtains a lightweight network by removing redundancy in
the heavy network at risk of performance drop. In this paper, we propose a new
framework to coherently integrate these two paths so that they can complement
each other compensate for the disadvantages. We first design a binary mask
derived from a learnable threshold to prune static kernels, significantly
reducing the parameters and computational cost but achieving higher performance
in Imagenet-1K(0.6\% increase in top-1 accuracy with 0.67G fewer FLOPs). Based
on this learnable mask, we further propose a novel dynamic sparse network
incorporating the dynamic routine mechanism, which exerts much higher accuracy
than baselines ( increase in top-1 accuracy for MobileNetV1 with
sparsity). As a result, our method demonstrates a more efficient dynamic
convolution with sparsity
Anthropogenic emissions of NO x over China: Reconciling the difference of inverse modeling results using GOME-2 and OMI measurements
Abstract Inverse modeling using satellite observations of nitrogen dioxide (NO 2 ) columns has been extensively used to estimate nitrogen oxides (NO x ) emissions in China. Recently, the Global Ozone Monitoring Experiment-2 (GOME-2) and Ozone Monitoring Instrument (OMI) provide independent global NO 2 column measurements on a nearly daily basis at around 9:30 and 13:30 local time across the equator, respectively. Anthropogenic NO x emission estimates by applying previously developed monthly inversion (MI) or daily inversion (DI) methods to these two sets of measurements show substantial differences. We improve the DI method by conducting model simulation, satellite retrieval, and inverse modeling sequentially on a daily basis. After each inversion, we update anthropogenic NO x emissions in the model simulation with the newly obtained a posteriori results. Consequently, the inversion-optimized emissions are used to compute the a priori NO 2 profiles for satellite retrievals. As such, the a priori profiles used in satellite retrievals are now coupled to inverse modeling results. The improved procedure was applied to GOME-2 and OMI NO 2 measurements in 2011. The new daily retrieval-inversion (DRI) method estimates an average NO x emission of 6.9 Tg N/yr over China, and the difference between using GOME-2 and OMI measurements is 0.4 Tg N/yr, which is significantly smaller than the difference of 1.3 Tg N/yr using the previous DI method. Using the more consistent DRI inversion results, we find that anthropogenic NO x emissions tend to be higher in winter and summer than spring (and possibly fall) and the weekday-to-weekend emission ratio tends to increase with NO x emission in China
DriveSceneGen: Generating Diverse and Realistic Driving Scenarios from Scratch
Realistic and diverse traffic scenarios in large quantities are crucial for
the development and validation of autonomous driving systems. However, owing to
numerous difficulties in the data collection process and the reliance on
intensive annotations, real-world datasets lack sufficient quantity and
diversity to support the increasing demand for data. This work introduces
DriveSceneGen, a data-driven driving scenario generation method that learns
from the real-world driving dataset and generates entire dynamic driving
scenarios from scratch. DriveSceneGen is able to generate novel driving
scenarios that align with real-world data distributions with high fidelity and
diversity. Experimental results on 5k generated scenarios highlight the
generation quality, diversity, and scalability compared to real-world datasets.
To the best of our knowledge, DriveSceneGen is the first method that generates
novel driving scenarios involving both static map elements and dynamic traffic
participants from scratch.Comment: 7 pages, 5 figures, 2 table
An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation
Despite making significant progress in multi-modal tasks, current Multi-modal
Large Language Models (MLLMs) encounter the significant challenge of
hallucination, which may lead to harmful consequences. Therefore, evaluating
MLLMs' hallucinations is becoming increasingly important in model improvement
and practical application deployment. Previous works are limited in high
evaluation costs (e.g., relying on humans or advanced LLMs) and insufficient
evaluation dimensions (e.g., types of hallucination and task). In this paper,
we propose an LLM-free multi-dimensional benchmark AMBER, which can be used to
evaluate both generative task and discriminative task including object
existence, object attribute and object relation hallucination. Based on AMBER,
we design a low-cost and efficient evaluation pipeline. Additionally, we
conduct a comprehensive evaluation and detailed analysis of mainstream MLLMs
including GPT-4V(ision), and also give guideline suggestions for mitigating
hallucinations. The data and code of AMBER are available at
https://github.com/junyangwang0410/AMBER.Comment: 11 pages, 4 figure
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