Zobrazeno 1 - 10
of 4 851
pro vyhledávání: '"A. Krähenbühl"'
We propose a new transformer-based image and video tokenizer with Binary Spherical Quantization (BSQ). BSQ projects the high-dimensional visual embedding to a lower-dimensional hypersphere and then applies binary quantization. BSQ is (1) parameter-ef
Externí odkaz:
http://arxiv.org/abs/2406.07548
A recent advance in networking is the deployment of path-aware multipath network architectures, where network endpoints are given multiple network paths to send their data on. In this work, we tackle the challenge of selecting paths for latency-sensi
Externí odkaz:
http://arxiv.org/abs/2405.04319
Autor:
Cho, Jang Hyun, Ivanovic, Boris, Cao, Yulong, Schmerling, Edward, Wang, Yue, Weng, Xinshuo, Li, Boyi, You, Yurong, Krähenbühl, Philipp, Wang, Yan, Pavone, Marco
Multi-modal large language models (MLLMs) have shown incredible capabilities in a variety of 2D vision and language tasks. We extend MLLMs' perceptual capabilities to ground and reason about images in 3-dimensional space. To that end, we first develo
Externí odkaz:
http://arxiv.org/abs/2405.03685
Autor:
Zhao, Yue, Zhao, Long, Zhou, Xingyi, Wu, Jialin, Chu, Chun-Te, Miao, Hui, Schroff, Florian, Adam, Hartwig, Liu, Ting, Gong, Boqing, Krähenbühl, Philipp, Yuan, Liangzhe
The recent advance in vision-language models is largely attributed to the abundance of image-text data. We aim to replicate this success for video-language models, but there simply is not enough human-curated video-text data available. We thus resort
Externí odkaz:
http://arxiv.org/abs/2401.06129
Stabilizing proteins is a foundational step in protein engineering. However, the evolutionary pressure of all extant proteins makes identifying the scarce number of mutations that will improve thermodynamic stability challenging. Deep learning has re
Externí odkaz:
http://arxiv.org/abs/2310.12979
Autor:
Zhao, Yue, Krähenbühl, Philipp
Videos are big, complex to pre-process, and slow to train on. State-of-the-art large-scale video models are trained on clusters of 32 or more GPUs for several days. As a consequence, academia largely ceded the training of large video models to indust
Externí odkaz:
http://arxiv.org/abs/2309.16669
Simulation forms the backbone of modern self-driving development. Simulators help develop, test, and improve driving systems without putting humans, vehicles, or their environment at risk. However, simulators face a major challenge: They rely on real
Externí odkaz:
http://arxiv.org/abs/2307.07947
Autor:
Krähenbühl, Cyrill, Wyss, Marc, Basin, David, Lenders, Vincent, Perrig, Adrian, Strohmeier, Martin
In its current state, the Internet does not provide end users with transparency and control regarding on-path forwarding devices. In particular, the lack of network device information reduces the trustworthiness of the forwarding path and prevents en
Externí odkaz:
http://arxiv.org/abs/2304.03108
Autor:
Cho, Jang Hyun, Krähenbühl, Philipp
Large-scale object detection and instance segmentation face a severe data imbalance. The finer-grained object classes become, the less frequent they appear in our datasets. However, at test-time, we expect a detector that performs well for all classe
Externí odkaz:
http://arxiv.org/abs/2301.09724
Detection Transformer (DETR) directly transforms queries to unique objects by using one-to-one bipartite matching during training and enables end-to-end object detection. Recently, these models have surpassed traditional detectors on COCO with undeni
Externí odkaz:
http://arxiv.org/abs/2212.06137