2 research outputs found
Coreset-Based Adaptive Tracking
We propose a method for learning from streaming visual data using a compact,
constant size representation of all the data that was seen until a given
moment. Specifically, we construct a 'coreset' representation of streaming data
using a parallelized algorithm, which is an approximation of a set with
relation to the squared distances between this set and all other points in its
ambient space. We learn an adaptive object appearance model from the coreset
tree in constant time and logarithmic space and use it for object tracking by
detection. Our method obtains excellent results for object tracking on three
standard datasets over more than 100 videos. The ability to summarize data
efficiently makes our method ideally suited for tracking in long videos in
presence of space and time constraints. We demonstrate this ability by
outperforming a variety of algorithms on the TLD dataset with 2685 frames on
average. This coreset based learning approach can be applied for both real-time
learning of small, varied data and fast learning of big data.Comment: 8 pages, 5 figures, In submission to IEEE TPAMI (Transactions on
Pattern Analysis and Machine Intelligence
Coreset-Based Neural Network Compression
We propose a novel Convolutional Neural Network (CNN) compression algorithm
based on coreset representations of filters. We exploit the redundancies extant
in the space of CNN weights and neuronal activations (across samples) in order
to obtain compression. Our method requires no retraining, is easy to implement,
and obtains state-of-the-art compression performance across a wide variety of
CNN architectures. Coupled with quantization and Huffman coding, we create
networks that provide AlexNet-like accuracy, with a memory footprint that is
smaller than the original AlexNet, while also introducing
significant reductions in inference time as well. Additionally these compressed
networks when fine-tuned, successfully generalize to other domains as well.Comment: Camera-Ready version for ECCV 201