Conditional Positional Encodings for Vision Transformers

We propose a conditional positional encoding (CPE) scheme for vision Transformers. Unlike previous fixed or learnable positional encodings, which are pre-defined and independent of input tokens, CPE is dynamically generated and conditioned on the local neighborhood of the input tokens. As a result, CPE can easily generalize to the input sequences that are longer than what the model has ever seen during training. Besides, CPE can keep the desired translation-invariance in the image classification task, resulting in improved classification accuracy. CPE can be effortlessly implemented with a simple Position Encoding Generator (PEG), and it can be seamlessly incorporated into the current Transformer framework. Built on PEG, we present Conditional Position encoding Vision Transformer (CPVT). We demonstrate that CPVT has visually similar attention maps compared to those with learned positional encodings. Benefit from the conditional positional encoding scheme, we obtain state-of-the-art results on the ImageNet classification task compared with vision Transformers to date. Our code will be made available at https://github.com/Meituan-AutoML/CPVT .Comment: A general purpose conditional position encoding for vision transformer

Target-Driven Structured Transformer Planner for Vision-Language Navigation

Vision-language navigation is the task of directing an embodied agent to navigate in 3D scenes with natural language instructions. For the agent, inferring the long-term navigation target from visual-linguistic clues is crucial for reliable path planning, which, however, has rarely been studied before in literature. In this article, we propose a Target-Driven Structured Transformer Planner (TD-STP) for long-horizon goal-guided and room layout-aware navigation. Specifically, we devise an Imaginary Scene Tokenization mechanism for explicit estimation of the long-term target (even located in unexplored environments). In addition, we design a Structured Transformer Planner which elegantly incorporates the explored room layout into a neural attention architecture for structured and global planning. Experimental results demonstrate that our TD-STP substantially improves previous best methods' success rate by 2% and 5% on the test set of R2R and REVERIE benchmarks, respectively. Our code is available at https://github.com/YushengZhao/TD-STP

RobuSTore : a distributed storage architecture with robust and high performance

Emerging large-scale scientific applications involve access to distributed large-scale data collections, and require high and robust performance. However, the inherent disk performance variation in distributed shared systems makes it difficult to achieve high and robust performance with traditional parallel storage schemes. We propose RobuSTore, a novel storage architecture, which combines erasure codes and speculative access to tolerate the performance variation. RobuSTore uses erasure codes to add flexible redundancy then spreads the encoded data across a large number of disks. Speculative access to the redundant data enables application requests to be satisfied with only early-completed blocks, reducing performance dependence on the behavior of individual disks. To demonstrate the feasibility of the RobuSTore architecture, we design a system framework to integrate erasure coding and speculative access mechanisms, and discuss the critical choices for the framework. We evaluate the RobuSTore architecture using detailed software simulation across a wide range of system configurations. Our simulation results affirm the high and robust performance of RobuSTore compared to traditional parallel storage systems. For example, to read 1 GB data from 64 disks with random data layout, RobuSTore achieves an average bandwidth of over 400 MBps, nearly 15x that achieved by a baseline RAID-0 scheme. At the same time, RobuSTore achieves standard deviation of access latency of only 0.5 seconds, less than 25% of the total access latency, which improves about 5-fold comparing to RAID-0. To write 1 GB data to 64 disks, RobuSTore achieves average bandwidth of 180 MBps, five times faster than RAID-0 even if RobuSTore writes 300% redundant data. RobuSTore secures these benefits at moderate cost of about 2-3x storage capacity overhead and 50% network and disk I/O overhea

RobuSTore: A Distributed Storage Architecture with Robust and High Performance

Emerging large-scale scientific applications require to access large data objects in high and robust performance. We propose RobuSTore, a storage architecture that combines erasure codes and speculative access mechanisms for parallel write and read in distributed environments. The mechanisms can effectively aggregate the bandwidth from a large number of distributed disks and statistically tolerate pear-disk performance variation. Our simulation results affirm the high and robust performance of RobuSTore in both write and read operations compared to traditional parallel storage systems. For example, for a 1GB data access using 64 disks, RobuSTore achieves average bandwidth of 186MBps for write and 400MBps for read, nearly 6x and 15x that achieved by a RAID-0 system. The standard deviation of access latency is only 0.5 second, about 9 % of the write latency and 20 % of the read latency, and a 5-fold improvement from RAID-0. The improvements are achieved at moderate cost: about 40 % increase in I/O operations and 2x-3x increase in storage capacity utilization. 1

RobuSTore: Robust Performance for Distributed Storage Systems

Abstract *1 Emerging large-scale scientific applications involve massive, distributed, shared data collections in petabytes, and require robust, high performance for read-dominated workloads. Achieving robust performance, i.e. low variability, in storage systems is difficult. We propose RobuSTore, a novel storage technique, which combines erasure codes and speculative access to reduce performance variability and increase performance. RobuSTore uses erasure codes to add flexible redundancy then spreads the encoded data across a large number of disks. Speculative access to the redundant data from multiple disks enables application requests to be satisfied with only early-arriving blocks, reducing performance dependence on the behavior of individual disks. We present the design and an evaluation of RobuSTore which shows improved robustness, reducing the standard deviation of access latencies by as much as 5-fold vs. traditional RAID. In addition, RobuSTore improves access bandwidth by as much as 15-fold. RobuSTore secures these benefits at the cost of a 2-3x storage capacity overhead and ~1.5x network and disk I/O overhead. 1