Sensor Fusion by Spatial Encoding for Autonomous Driving no code implementations • 17 Aug 2023 • Quoc-Vinh Lai-Dang, Jihui Lee, Bumgeun Park, Dongsoo Har Sensor fusion is critical to perception systems for task domains such as autonomous driving and robotics. Autonomous Driving Sensor ...
Brotli is a generic-purpose lossless compression algorithm that compresses data using a combination of a modern variant of the LZ77 algorithm, Huffman coding and 2nd order context modeling, with a compression ratio comparable to the best currently available general-purpose compression methods. It is ...
This approach seeks to develop an effective way to model CNN with different architectures by devising a new encoding strategy. There is a strong correlation between the performance of a CNN and its depth; therefore, the CBL block is automatically repeated using GAs, and then the fixed FC part...
Change static view to avoid setting the Content-Encoding response header to an encoding guessed using Python's mimetypes module. This was causing clients to decode the content of gzipped files when downloading them. The client would end up with a foo.txt.gz file on disk that was already dec...
In addition to using the time series of inertial signals as input features, we propose a new method to convert the time series of inertial signals into images with varying image sizes, as well as suppress the impact of the noise via feature extraction and feature selection. Moreover, we gene...
MiniCPM-V 2.0 can be efficiently deployed on most GPU cards and personal computers, and even on end devices such as mobile phones. For visual encoding, we compress the image representations into much fewer tokens via a perceiver resampler. This allows MiniCPM-V 2.0 to operate with favorable ...
MiniCPM-V 2.0 is the first end-side LMM aligned via multimodal RLHF for trustworthy behavior (using the recent RLHF-V [CVPR'24] series technique). This allows the model to match GPT-4V in preventing hallucinations on Object HalBench. 🌟 High-Resolution Images at Any Aspect Raito. Mini...
, andeven on end devices such as mobile phones. For visual encoding, we compress the image representations into much fewer tokens via a perceiver resampler. This allows MiniCPM-V 2.0 to operate withfavorable memory cost and speed during inference even when dealing with high-resolution images....
MiniCPM-V 2.0 can be efficiently deployed on most GPU cards and personal computers, and even on end devices such as mobile phones. For visual encoding, we compress the image representations into much fewer tokens via a perceiver resampler. This allows MiniCPM-V 2.0 to operate with favorable ...