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Found 13 papers, 10 papers with code
VILA: On Pre-training for Visual Language Models
Visual language models (VLMs) rapidly progressed with the recent success of large language models.
Ranked #21 on
Visual Question Answering
on MM-Vet
BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation
Multi-sensor fusion is essential for an accurate and reliable autonomous driving system.
Ranked #4 on
3D Object Detection
on nuScenes
PatchNet -- Short-range Template Matching for Efficient Video Processing
Object recognition is a fundamental problem in many video processing tasks, accurately locating seen objects at low computation cost paves the way for on-device video recognition.
A Delay Metric for Video Object Detection: What Average Precision Fails to Tell
Average precision (AP) is a widely used metric to evaluate detection accuracy of image and video object detectors.
CaTDet: Cascaded Tracked Detector for Efficient Object Detection from Video
Experiments on the KITTI dataset show that CaTDet reduces operation count by 5. 1-8. 7x with the same mean Average Precision(mAP) as the single-model Faster R-CNN detector and incurs additional delay of 0. 3 frame.
Deep Gradient Compression Reduce the Communication Bandwidth For distributed Traning
Large-scale distributed training requires significant communication bandwidth for gradient exchange that limits the scalability of multi-node training, and requires expensive high-bandwidth network infrastructure.
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
The situation gets even worse with distributed training on mobile devices (federated learning), which suffers from higher latency, lower throughput, and intermittent poor connections.
Exploring the Regularity of Sparse Structure in Convolutional Neural Networks
Since memory reference is more than two orders of magnitude more expensive than arithmetic operations, the regularity of sparse structure leads to more efficient hardware design.
Trained Ternary Quantization
To solve this problem, we propose Trained Ternary Quantization (TTQ), a method that can reduce the precision of weights in neural networks to ternary values.
ESE: Efficient Speech Recognition Engine with Sparse LSTM on FPGA
Evaluated on the LSTM for speech recognition benchmark, ESE is 43x and 3x faster than Core i7 5930k CPU and Pascal Titan X GPU implementations.
DSD: Dense-Sparse-Dense Training for Deep Neural Networks
We propose DSD, a dense-sparse-dense training flow, for regularizing deep neural networks and achieving better optimization performance.
EIE: Efficient Inference Engine on Compressed Deep Neural Network
EIE has a processing power of 102GOPS/s working directly on a compressed network, corresponding to 3TOPS/s on an uncompressed network, and processes FC layers of AlexNet at 1. 88x10^4 frames/sec with a power dissipation of only 600mW.
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
To address this limitation, we introduce "deep compression", a three stage pipeline: pruning, trained quantization and Huffman coding, that work together to reduce the storage requirement of neural networks by 35x to 49x without affecting their accuracy.
