OpenRaiser/Intern-Atlas huggingface dataset:
Monocular Depth Estimation Primed by Salient Point Detection and Normalized Hessian Loss
Venue: arXiv cs.CV 2021
Year: 2021

Deep neural networks have recently thrived on single image depth estimation. That being said, current developments on this topic highlight an apparent compromise between accuracy and network size. This work proposes an accurate and lightweight framework for monocular depth estimation based on a self-attention mechanism stemming from salient point detection. Specifically, we utilize a sparse set of keypoints to train a FuSaNet model that consists of two major components: Fusion-Net and Saliency-Net. In addition, we introduce a normalized Hessian loss term invariant to scaling and shear along the depth direction, which is shown to substantially improve the accuracy. The proposed method achieves state-of-the-art results on NYU-Depth-v2 and KITTI while using 3.1-38.4 times smaller model in terms of the number of parameters than baseline approaches. Experiments on the SUN-RGBD further demonstrate the generalizability of the proposed method.
Towards Informed Partitioning for Load Balancing: a Proof-of-Concept
Venue: arXiv cs.DC 2021
Year: 2021

Most parallel applications suffer from load imbalance, a crucial performance degradation factor. In particle simulations, this is mainly due to the migration of particles between processing elements, which eventually gather unevenly and create workload imbalance. Dynamic load balancing is used at various iterations to mitigate load imbalance, employing a partitioning method to divide the computational space evenly while minimizing communications. In this paper, we propose a novel partitioning methodology called ``informed partitioning''. It uses information based on the evolution of the computation to reduce the load balancing growth and the number of load balancing calls. We illustrate informed partitioning by proposing a new geometric partitioning technique for particles simulations. This technique is derived from the well-known recursive coordinate bisection and employs the velocity of the particles to guide the bisection axis. To properly compare the performance of our new method with existing partitioning techniques during application execution, we introduce an effort metric based on a theoretical model of load balanced parallel application time. We propose a proof-of-concept of informed partitioning, through a numerical study, on three N-Body simulations with various particle dynamics, and we discuss its performance against popular geometric partitioning techniques. Moreover, we show that our effort metric can be used to rank partitioning techniques by their efficiency at any time point during the simulation. Eventually, this could be used to choose the best partitioning on the fly. In the numerical study, we report that our novel concept increases the performance of two experiments out of three by up to 76% and 15%, while being marginally slower by only $3\%$ in one experiment. Also, we discuss the limitations of our implementation of informed partitioning and our effort metric.
Multi-Attributed and Structured Text-to-Face Synthesis
Venue: arXiv cs.CV 2021
Year: 2021

Generative Adversarial Networks (GANs) have revolutionized image synthesis through many applications like face generation, photograph editing, and image super-resolution. Image synthesis using GANs has predominantly been uni-modal, with few approaches that can synthesize images from text or other data modes. Text-to-image synthesis, especially text-to-face synthesis, has promising use cases of robust face-generation from eye witness accounts and augmentation of the reading experience with visual cues. However, only a couple of datasets provide consolidated face data and textual descriptions for text-to-face synthesis. Moreover, these textual annotations are less extensive and descriptive, which reduces the diversity of faces generated from it. This paper empirically proves that increasing the number of facial attributes in each textual description helps GANs generate more diverse and real-looking faces. To prove this, we propose a new methodology that focuses on using structured textual descriptions. We also consolidate a Multi-Attributed and Structured Text-to-face (MAST) dataset consisting of high-quality images with structured textual annotations and make it available to researchers to experiment and build upon. Lastly, we report benchmark Frechet's Inception Distance (FID), Facial Semantic Similarity (FSS), and Facial Semantic Distance (FSD) scores for the MAST dataset.
Lightweight Self-Attentive Sequential Recommendation
Venue: arXiv cs.IR 2021
Year: 2021

Modern deep neural networks (DNNs) have greatly facilitated the development of sequential recommender systems by achieving state-of-the-art recommendation performance on various sequential recommendation tasks. Given a sequence of interacted items, existing DNN-based sequential recommenders commonly embed each item into a unique vector to support subsequent computations of the user interest. However, due to the potentially large number of items, the over-parameterised item embedding matrix of a sequential recommender has become a memory bottleneck for efficient deployment in resource-constrained environments, e.g., smartphones and other edge devices. Furthermore, we observe that the widely-used multi-head self-attention, though being effective in modelling sequential dependencies among items, heavily relies on redundant attention units to fully capture both global and local item-item transition patterns within a sequence. In this paper, we introduce a novel lightweight self-attentive network (LSAN) for sequential recommendation. To aggressively compress the original embedding matrix, LSAN leverages the notion of compositional embeddings, where each item embedding is composed by merging a group of selected base embedding vectors derived from substantially smaller embedding matrices. Meanwhile, to account for the intrinsic dynamics of each item, we further propose a temporal context-aware embedding composition scheme. Besides, we develop an innovative twin-attention network that alleviates the redundancy of the traditional multi-head self-attention while retaining full capacity for capturing long- and short-term (i.e., global and local) item dependencies. Comprehensive experiments demonstrate that LSAN significantly advances the accuracy and memory efficiency of existing sequential recommenders.
The Next 700 Program Transformers
Venue: arXiv cs.PL 2021
Year: 2021

In this paper, we describe a hierarchy of program transformers in which the transformer at each level of the hierarchy builds on top of those at lower levels. The program transformer at level 1 of the hierarchy corresponds to positive supercompilation, and that at level 2 corresponds to distillation. We prove that the transformers at each level terminate. We then consider the speedups that can be obtained at each level in the hierarchy, and try to characterise the improvements that can be made.
Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization
Venue: arXiv cs.LG 2021
Year: 2021

Adaptive gradient methods such as Adam have gained increasing popularity in deep learning optimization. However, it has been observed that compared with (stochastic) gradient descent, Adam can converge to a different solution with a significantly worse test error in many deep learning applications such as image classification, even with a fine-tuned regularization. In this paper, we provide a theoretical explanation for this phenomenon: we show that in the nonconvex setting of learning over-parameterized two-layer convolutional neural networks starting from the same random initialization, for a class of data distributions (inspired from image data), Adam and gradient descent (GD) can converge to different global solutions of the training objective with provably different generalization errors, even with weight decay regularization. In contrast, we show that if the training objective is convex, and the weight decay regularization is employed, any optimization algorithms including Adam and GD will converge to the same solution if the training is successful. This suggests that the inferior generalization performance of Adam is fundamentally tied to the nonconvex landscape of deep learning optimization.
Design and Scaffolded Training of an Efficient DNN Operator for Computer Vision on the Edge
Venue: arXiv cs.AR 2021
Year: 2021

Massively parallel systolic arrays and resource-efficient depthwise separable convolutions are two promising techniques to accelerate DNN inference on the edge. Interestingly, their combination is inefficient: Computational patterns of depthwise separable convolutions do not exhibit a rhythmic systolic flow and lack sufficient data reuse to saturate systolic arrays. We formally analyse this inefficiency and propose an efficient operator, an optimal hardware dataflow, and a superior training methodology towards alleviating this. The efficient operator, called FuSeConv, is a drop-in replacement for depthwise separable convolutions. FuSeConv factorizes convolution fully along their spatial and depth dimensions. The resultant computation efficiently maps to systolic arrays. The optimal dataflow, called Spatial-Tiled Output Stationary (ST-OS), maximizes the efficiency of FuSeConv on systolic arrays. It maps independent convolutions to rows of the array to maximize resource utilization with negligible VLSI overheads. Neural Operator Scaffolding (NOS) scaffolds the training of FuSeConv by distilling knowledge from the expensive depthwise separable convolutions. This bridges the accuracy gap between FuSeConv networks and baselines. Additionally, NOS can be combined with Neural Architecture Search (NAS) to trade-off latency and accuracy. The HW/SW co-design of FuSeConv with ST-OS achieves a significant speedup of 4.1-9.25X with state-of-the-art efficient networks for ImageNet. The parameter efficiency of FuSeConv and its significant out-performance over depthwise separable convolutions on systolic arrays illustrates their promise as a strong solution on the edge. Training FuSeConv networks with NOS achieves accuracy comparable to the baselines. Further, by combining NOS with NAS, we design networks that define state-of-the-art models improving on both accuracy and latency on systolic arrays.
Podcast Metadata and Content: Episode Relevance andAttractiveness in Ad Hoc Search
Venue: arXiv cs.IR 2021
Year: 2021

Rapidly growing online podcast archives contain diverse content on a wide range of topics. These archives form an important resource for entertainment and professional use, but their value can only be realized if users can rapidly and reliably locate content of interest. Search for relevant content can be based on metadata provided by content creators, but also on transcripts of the spoken content itself. Excavating relevant content from deep within these audio streams for diverse types of information needs requires varying the approach to systems prototyping. We describe a set of diverse podcast information needs and different approaches to assessing retrieved content for relevance. We use these information needs in an investigation of the utility and effectiveness of these information sources. Based on our analysis, we recommend approaches for indexing and retrieving podcast content for ad hoc search.
TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarios
Venue: arXiv cs.CV 2021
Year: 2021

Object detection on drone-captured scenarios is a recent popular task. As drones always navigate in different altitudes, the object scale varies violently, which burdens the optimization of networks. Moreover, high-speed and low-altitude flight bring in the motion blur on the densely packed objects, which leads to great challenge of object distinction. To solve the two issues mentioned above, we propose TPH-YOLOv5. Based on YOLOv5, we add one more prediction head to detect different-scale objects. Then we replace the original prediction heads with Transformer Prediction Heads (TPH) to explore the prediction potential with self-attention mechanism. We also integrate convolutional block attention model (CBAM) to find attention region on scenarios with dense objects. To achieve more improvement of our proposed TPH-YOLOv5, we provide bags of useful strategies such as data augmentation, multiscale testing, multi-model integration and utilizing extra classifier. Extensive experiments on dataset VisDrone2021 show that TPH-YOLOv5 have good performance with impressive interpretability on drone-captured scenarios. On DET-test-challenge dataset, the AP result of TPH-YOLOv5 are 39.18%, which is better than previous SOTA method (DPNetV3) by 1.81%. On VisDrone Challenge 2021, TPHYOLOv5 wins 5th place and achieves well-matched results with 1st place model (AP 39.43%). Compared to baseline model (YOLOv5), TPH-YOLOv5 improves about 7%, which is encouraging and competitive.
Dynamic Structural Clustering on Graphs
Venue: arXiv cs.DS 2021
Year: 2021

Structural Clustering ($DynClu$) is one of the most popular graph clustering paradigms. In this paper, we consider $StrClu$ under two commonly adapted similarities, namely Jaccard similarity and cosine similarity on a dynamic graph, $G = \langle V, E\rangle$, subject to edge insertions and deletions (updates). The goal is to maintain certain information under updates, so that the $StrClu$ clustering result on~$G$ can be retrieved in $O(|V| + |E|)$ time, upon request. The state-of-the-art worst-case cost is $O(|V|)$ per update; we improve this update-time bound significantly with the $ρ$-approximate notion. Specifically, for a specified failure probability, $δ^*$, and every sequence of $M$ updates (no need to know $M$'s value in advance), our algorithm, $DynELM$, achieves $O(\log^2 |V| + \log |V| \cdot \log \frac{M}{δ^*})$ amortized cost for each update, at all times in linear space. Moreover, $DynELM$ provides a provable "sandwich" guarantee on the clustering quality at all times after \emph{each update} with probability at least $1 - δ^*$. We further develop $DynELM$ into our ultimate algorithm, $DynStrClu$, which also supports cluster-group-by queries. Given $Q\subseteq V$, this puts the non-empty intersection of $Q$ and each $StrClu$ cluster into a distinct group. $DynStrClu$ not only achieves all the guarantees of $DynELM$, but also runs cluster-group-by queries in $O(|Q|\cdot \log |V|)$ time. We demonstrate the performance of our algorithms via extensive experiments, on 15 real datasets. Experimental results confirm that our algorithms are up to three orders of magnitude more efficient than state-of-the-art competitors, and still provide quality structural clustering results. Furthermore, we study the difference between the two similarities w.r.t. the quality of approximate clustering results.
Online Service Placement and Request Scheduling in MEC Networks
Venue: arXiv cs.DC 2021
Year: 2021

Mobile edge computing (MEC) emerges as a promising solution for servicing delay-sensitive tasks at the edge network. A body of recent literature started to focus on cost-efficient service placement and request scheduling. This work investigates the joint optimization of service placement and request scheduling in a dense MEC network, and develops an efficient online algorithm that achieves close-to-optimal performance. Our online algorithm consists of two basic modules: (1) a regularization with look-ahead approach from competitive online convex optimization, for decomposing the offline relaxed minimization problem into multiple sub-problems, each of which can be efficiently solved in each time slot; (2) a randomized rounding method to transform the fractional solution of offline relaxed problem into integer solution of the original minimization problem, guaranteeing a low competitive ratio. Both theoretical analysis and simulation studies corroborate the efficacy of our proposed online MEC optimization algorithm.
PoissonSeg: Semi-Supervised Few-Shot Medical Image Segmentation via Poisson Learning
Venue: arXiv cs.CV 2021
Year: 2021

The application of deep learning to medical image segmentation has been hampered due to the lack of abundant pixel-level annotated data. Few-shot Semantic Segmentation (FSS) is a promising strategy for breaking the deadlock. However, a high-performing FSS model still requires sufficient pixel-level annotated classes for training to avoid overfitting, which leads to its performance bottleneck in medical image segmentation due to the unmet need for annotations. Thus, semi-supervised FSS for medical images is accordingly proposed to utilize unlabeled data for further performance improvement. Nevertheless, existing semi-supervised FSS methods has two obvious defects: (1) neglecting the relationship between the labeled and unlabeled data; (2) using unlabeled data directly for end-to-end training leads to degenerated representation learning. To address these problems, we propose a novel semi-supervised FSS framework for medical image segmentation. The proposed framework employs Poisson learning for modeling data relationship and propagating supervision signals, and Spatial Consistency Calibration for encouraging the model to learn more coherent representations. In this process, unlabeled samples do not involve in end-to-end training, but provide supervisory information for query image segmentation through graph-based learning. We conduct extensive experiments on three medical image segmentation datasets (i.e. ISIC skin lesion segmentation, abdominal organs segmentation for MRI and abdominal organs segmentation for CT) to demonstrate the state-of-the-art performance and broad applicability of the proposed framework.
Revenue Maximization through Cell Switching and Spectrum Leasing in 5G HetNets
Venue: arXiv cs.NI 2021
Year: 2021

One of the ways of achieving improved capacity in mobile cellular networks is via network densification. Even though densification increases the capacity of the network, it also leads to increased energy consumption which can be curbed by dynamically switching off some base stations (BSs) during periods of low traffic. However, dynamic cell switching has the challenge of spectrum under-utilizationas the spectrum originally occupied by the BSs that are turned off remains dormant. This dormant spectrum can be leased by the primary network (PN) operators, who hold the license, to the secondary network (SN) operators who cannot afford to purchase the spectrum license. Thus enabling the PN to gain additional revenue from spectrum leasing as well as from electricity cost savings due to reduced energy consumption. Therefore, in this work, we propose a cell switching and spectrum leasing framework based on simulated annealing (SA) algorithm to maximize the revenue of the PN while respecting the quality-of-service constraints. The performance evaluation reveals that the proposed method is very close to optimal exhaustive search method with a significant reduction in the computation complexity.
Data Augmentation for Low-Resource Named Entity Recognition Using Backtranslation
Venue: arXiv cs.CL 2021
Year: 2021

The state of art natural language processing systems relies on sizable training datasets to achieve high performance. Lack of such datasets in the specialized low resource domains lead to suboptimal performance. In this work, we adapt backtranslation to generate high quality and linguistically diverse synthetic data for low-resource named entity recognition. We perform experiments on two datasets from the materials science (MaSciP) and biomedical domains (S800). The empirical results demonstrate the effectiveness of our proposed augmentation strategy, particularly in the low-resource scenario.
Disentangling What and Where for 3D Object-Centric Representations Through Active Inference
Venue: arXiv cs.AI 2021
Year: 2021

Although modern object detection and classification models achieve high accuracy, these are typically constrained in advance on a fixed train set and are therefore not flexible to deal with novel, unseen object categories. Moreover, these models most often operate on a single frame, which may yield incorrect classifications in case of ambiguous viewpoints. In this paper, we propose an active inference agent that actively gathers evidence for object classifications, and can learn novel object categories over time. Drawing inspiration from the human brain, we build object-centric generative models composed of two information streams, a what- and a where-stream. The what-stream predicts whether the observed object belongs to a specific category, while the where-stream is responsible for representing the object in its internal 3D reference frame. We show that our agent (i) is able to learn representations for many object categories in an unsupervised way, (ii) achieves state-of-the-art classification accuracies, actively resolving ambiguity when required and (iii) identifies novel object categories. Furthermore, we validate our system in an end-to-end fashion where the agent is able to search for an object at a given pose from a pixel-based rendering. We believe that this is a first step towards building modular, intelligent systems that can be used for a wide range of tasks involving three dimensional objects.
Multi-Modulation Network for Audio-Visual Event Localization
Venue: arXiv cs.CV 2021
Year: 2021

We study the problem of localizing audio-visual events that are both audible and visible in a video. Existing works focus on encoding and aligning audio and visual features at the segment level while neglecting informative correlation between segments of the two modalities and between multi-scale event proposals. We propose a novel MultiModulation Network (M2N) to learn the above correlation and leverage it as semantic guidance to modulate the related auditory, visual, and fused features. In particular, during feature encoding, we propose cross-modal normalization and intra-modal normalization. The former modulates the features of two modalities by establishing and exploiting the cross-modal relationship. The latter modulates the features of a single modality with the event-relevant semantic guidance of the same modality. In the fusion stage,we propose a multi-scale proposal modulating module and a multi-alignment segment modulating module to introduce multi-scale event proposals and enable dense matching between cross-modal segments. With the auditory, visual, and fused features modulated by the correlation information regarding audio-visual events, M2N performs accurate event localization. Extensive experiments conducted on the AVE dataset demonstrate that our proposed method outperforms the state of the art in both supervised event localization and cross-modality localization.