OpenRaiser/Intern-Atlas huggingface dataset:
Weighted Envy-Freeness for Submodular Valuations
Venue: arXiv cs.GT 2022
Year: 2022

We investigate the fair allocation of indivisible goods to agents with possibly different entitlements represented by weights. Previous work has shown that guarantees for additive valuations with existing envy-based notions cannot be extended to the case where agents have matroid-rank (i.e., binary submodular) valuations. We propose two families of envy-based notions for matroid-rank and general submodular valuations, one based on the idea of transferability and the other on marginal values. We show that our notions can be satisfied via generalizations of rules such as picking sequences and maximum weighted Nash welfare. In addition, we introduce welfare measures based on harmonic numbers, and show that variants of maximum weighted harmonic welfare offer stronger fairness guarantees than maximum weighted Nash welfare under matroid-rank valuations.
TrADe Re-ID -- Live Person Re-Identification using Tracking and Anomaly Detection
Venue: arXiv cs.CV 2022
Year: 2022

Person Re-Identification (Re-ID) aims to search for a person of interest (query) in a network of cameras. In the classic Re-ID setting the query is sought in a gallery containing properly cropped images of entire bodies. Recently, the live Re-ID setting was introduced to represent the practical application context of Re-ID better. It consists in searching for the query in short videos, containing whole scene frames. The initial live Re-ID baseline used a pedestrian detector to build a large search gallery and a classic Re-ID model to find the query in the gallery. However, the galleries generated were too large and contained low-quality images, which decreased the live Re-ID performance. Here, we present a new live Re-ID approach called TrADe, to generate lower high-quality galleries. TrADe first uses a Tracking algorithm to identify sequences of images of the same individual in the gallery. Following, an Anomaly Detection model is used to select a single good representative of each tracklet. TrADe is validated on the live Re-ID version of the PRID-2011 dataset and shows significant improvements over the baseline.
Learning Deep Optimal Embeddings with Sinkhorn Divergences
Venue: arXiv cs.CV 2022
Year: 2022

Deep Metric Learning algorithms aim to learn an efficient embedding space to preserve the similarity relationships among the input data. Whilst these algorithms have achieved significant performance gains across a wide plethora of tasks, they have also failed to consider and increase comprehensive similarity constraints; thus learning a sub-optimal metric in the embedding space. Moreover, up until now; there have been few studies with respect to their performance in the presence of noisy labels. Here, we address the concern of learning a discriminative deep embedding space by designing a novel, yet effective Deep Class-wise Discrepancy Loss (DCDL) function that segregates the underlying similarity distributions (thus introducing class-wise discrepancy) of the embedding points between each and every class. Our empirical results across three standard image classification datasets and two fine-grained image recognition datasets in the presence and absence of noise clearly demonstrate the need for incorporating such class-wise similarity relationships along with traditional algorithms while learning a discriminative embedding space.
Transformers and CNNs both Beat Humans on SBIR
Venue: arXiv cs.CV 2022
Year: 2022

Sketch-based image retrieval (SBIR) is the task of retrieving natural images (photos) that match the semantics and the spatial configuration of hand-drawn sketch queries. The universality of sketches extends the scope of possible applications and increases the demand for efficient SBIR solutions. In this paper, we study classic triplet-based SBIR solutions and show that a persistent invariance to horizontal flip (even after model finetuning) is harming performance. To overcome this limitation, we propose several approaches and evaluate in depth each of them to check their effectiveness. Our main contributions are twofold: We propose and evaluate several intuitive modifications to build SBIR solutions with better flip equivariance. We show that vision transformers are more suited for the SBIR task, and that they outperform CNNs with a large margin. We carried out numerous experiments and introduce the first models to outperform human performance on a large-scale SBIR benchmark (Sketchy). Our best model achieves a recall of 62.25% (at k = 1) on the sketchy benchmark compared to previous state-of-the-art methods 46.2%.
Syndrome decoding meets multiple instances
Venue: arXiv cs.IT 2022
Year: 2022

The NP-hard problem of decoding random linear codes is crucial to both coding theory and cryptography. In particular, this problem underpins the security of many code based post-quantum cryptographic schemes. The state-of-art algorithms for solving this problem are the information syndrome decoding algorithm and its advanced variants. In this work, we consider syndrome decoding in the multiple instances setting. Two strategies are applied for different scenarios. The first strategy is to solve all instances with the aid of the precomputation technique. We adjust the current framework and distinguish the offline phase and online phase to reduce the amortized complexity. Further, we discuss the impact on the concrete security of some post-quantum schemes. The second strategy is to solve one out of many instances. Adapting the analysis for some earlier algorithm, we discuss the effectiveness of using advanced variants and confirm a related folklore conjecture.
Volumetric-based Contact Point Detection for 7-DoF Grasping
Venue: arXiv cs.RO 2022
Year: 2022

In this paper, we propose a novel grasp pipeline based on contact point detection on the truncated signed distance function (TSDF) volume to achieve closed-loop 7-degree-of-freedom (7-DoF) grasping on cluttered environments. The key aspects of our method are that 1) the proposed pipeline exploits the TSDF volume in terms of multi-view fusion, contact-point sampling and evaluation, and collision checking, which provides reliable and collision-free 7-DoF gripper poses with real-time performance; 2) the contact-based pose representation effectively eliminates the ambiguity introduced by the normal-based methods, which provides a more precise and flexible solution. Extensive simulated and real-robot experiments demonstrate that the proposed pipeline can select more antipodal and stable grasp poses and outperforms normal-based baselines in terms of the grasp success rate in both simulated and physical scenarios.
Rule-adhering synthetic data -- the lingua franca of learning
Venue: arXiv cs.LG 2022
Year: 2022

AI-generated synthetic data allows to distill the general patterns of existing data, that can then be shared safely as granular-level representative, yet novel data samples within the original semantics. In this work we explore approaches of incorporating domain expertise into the data synthesis, to have the statistical properties as well as pre-existing domain knowledge of rules be represented. The resulting synthetic data generator, that can be probed for any number of new samples, can then serve as a common source of intelligence, as a lingua franca of learning, consumable by humans and machines alike. We demonstrate the concept for a publicly available data set, and evaluate its benefits via descriptive analysis as well as a downstream ML model.
MUST-VQA: MUltilingual Scene-text VQA
Venue: arXiv cs.CV 2022
Year: 2022

In this paper, we present a framework for Multilingual Scene Text Visual Question Answering that deals with new languages in a zero-shot fashion. Specifically, we consider the task of Scene Text Visual Question Answering (STVQA) in which the question can be asked in different languages and it is not necessarily aligned to the scene text language. Thus, we first introduce a natural step towards a more generalized version of STVQA: MUST-VQA. Accounting for this, we discuss two evaluation scenarios in the constrained setting, namely IID and zero-shot and we demonstrate that the models can perform on a par on a zero-shot setting. We further provide extensive experimentation and show the effectiveness of adapting multilingual language models into STVQA tasks.
Collision-Free 6-DoF Trajectory Generation for Omnidirectional Multi-rotor Aerial Vehicle
Venue: arXiv cs.RO 2022
Year: 2022

As a kind of fully actuated system, omnidirectional multirotor aerial vehicles (OMAVs) has more flexible maneuverability than traditional underactuated multirotor aircraft, and it also has more significant advantages in obstacle avoidance flight in complex environments.However, there is almost no way to generate the full degrees of freedom trajectory that can play the OMAVs' potential.Due to the high dimensionality of configuration space, it is challenging to make the designed trajectory generation algorithm efficient and scalable.This paper aims to achieve obstacle avoidance planning of OMAV in complex environments. A 6-DoF trajectory generation framework for OMAVs was designed for the first time based on the geometrically constrained Minimum Control Effort (MINCO) trajectory generation framework.According to the safe regions represented by a series of convex polyhedra, combined with the aircraft's overall shape and dynamic constraints, the framework finally generates a collision-free optimal 6-DoF trajectory.The vehicle's attitude is parameterized into a 3D vector by stereographic projection.Simulation experiments based on Gazebo and PX4 Autopilot are conducted to verify the performance of the proposed framework.
Coherence of linear and surface data: Methodological proposal from the example of road data
Venue: arXiv cs.DB 2022
Year: 2022

The real world and its geographic objects are modeled and represented in different spatial databases. Each of these databases provides only a partial description (in space and time) of the geographic objects represented. Sometimes, producers and users of databases need to connect several of them for updates or comparisons. Much of the work in spatial database management focuses on matching these spatial databases and more particularly network databases, such as road networks. With regard to network data, one situation remains neglected, that of matching a linear database (with polylines objects) with a surface database (with polygons objects). In any case, users also need to connect these two types of spatial database. In this paper, a case study is made using French examples (Cachan, near Paris), as well as international case studies (Bordeaux in France, Victoria in Canada, and Copenhagen in Denmark), to propose an approach intended to make coherent network geographical objects in two different reference frames (linear and surface). This issue is addressed here through the example of road data. The surface data are then formalized in order to adapt them to linear data describing the same geographical objects. In the end, a polygon in the surface data corresponds to a single polyline in the linear data. This consistency should simplify the transfer of information from one reference frame (linear or surface) to the other. In other words, the methodology developed aims to make linear and surfacegeographic data interoperable.
An ensemble Multi-Agent System for non-linear classification
Venue: arXiv cs.LG 2022
Year: 2022

Self-Adaptive Multi-Agent Systems (AMAS) transform machine learning problems into problems of local cooperation between agents. We present smapy, an ensemble based AMAS implementation for mobility prediction, whose agents are provided with machine learning models in addition to their cooperation rules. With a detailed methodology, we show that it is possible to use linear models for nonlinear classification on a benchmark transport mode detection dataset, if they are integrated in a cooperative multi-agent structure. The results obtained show a significant improvement of the performance of linear models in non-linear contexts thanks to the multi-agent approach.
A Temporal Anomaly Detection System for Vehicles utilizing Functional Working Groups and Sensor Channels
Venue: arXiv cs.LG 2022
Year: 2022

A modern vehicle fitted with sensors, actuators, and Electronic Control Units (ECUs) can be divided into several operational subsystems called Functional Working Groups (FWGs). Examples of these FWGs include the engine system, transmission, fuel system, brakes, etc. Each FWG has associated sensor-channels that gauge vehicular operating conditions. This data rich environment is conducive to the development of Predictive Maintenance (PdM) technologies. Undercutting various PdM technologies is the need for robust anomaly detection models that can identify events or observations which deviate significantly from the majority of the data and do not conform to a well defined notion of normal vehicular operational behavior. In this paper, we introduce the Vehicle Performance, Reliability, and Operations (VePRO) dataset and use it to create a multi-phased approach to anomaly detection. Utilizing Temporal Convolution Networks (TCN), our anomaly detection system can achieve 96% detection accuracy and accurately predicts 91% of true anomalies. The performance of our anomaly detection system improves when sensor channels from multiple FWGs are utilized.
CAT: Controllable Attribute Translation for Fair Facial Attribute Classification
Venue: arXiv cs.CV 2022
Year: 2022

As the social impact of visual recognition has been under scrutiny, several protected-attribute balanced datasets emerged to address dataset bias in imbalanced datasets. However, in facial attribute classification, dataset bias stems from both protected attribute level and facial attribute level, which makes it challenging to construct a multi-attribute-level balanced real dataset. To bridge the gap, we propose an effective pipeline to generate high-quality and sufficient facial images with desired facial attributes and supplement the original dataset to be a balanced dataset at both levels, which theoretically satisfies several fairness criteria. The effectiveness of our method is verified on sex classification and facial attribute classification by yielding comparable task performance as the original dataset and further improving fairness in a comprehensive fairness evaluation with a wide range of metrics. Furthermore, our method outperforms both resampling and balanced dataset construction to address dataset bias, and debiasing models to address task bias.
PAPyA: Performance Analysis of Large RDF Graphs Processing Made Easy
Venue: arXiv cs.DB 2022
Year: 2022

Prescriptive Performance Analysis (PPA) has shown to be more useful than traditional descriptive and diagnostic analyses for making sense of Big Data (BD) frameworks' performance. In practice, when processing large (RDF) graphs on top of relational BD systems, several design decisions emerge and cannot be decided automatically, e.g., the choice of the schema, the partitioning technique, and the storage formats. PPA, and in particular ranking functions, helps enable actionable insights on performance data, leading practitioners to an easier choice of the best way to deploy BD frameworks, especially for graph processing. However, the amount of experimental work required to implement PPA is still huge. In this paper, we present PAPyA 1, a library for implementing PPA that allows (1) preparing RDF graphs data for a processing pipeline over relational BD systems, (2) enables automatic ranking of the performance in a user-defined solution space of experimental dimensions; (3) allows user-defined flexible extensions in terms of systems to test and ranking methods. We showcase PAPyA on a set of experiments based on the SparkSQL framework. PAPyA simplifies the performance analytics of BD systems for processing large (RDF) graphs.We provide PAPyA as a public open-source library under an MIT license that will be a catalyst for designing new research prescriptive analytical techniques for BD applications.
On the interplay of adversarial robustness and architecture components: patches, convolution and attention
Venue: arXiv cs.CV 2022
Year: 2022

In recent years novel architecture components for image classification have been developed, starting with attention and patches used in transformers. While prior works have analyzed the influence of some aspects of architecture components on the robustness to adversarial attacks, in particular for vision transformers, the understanding of the main factors is still limited. We compare several (non)-robust classifiers with different architectures and study their properties, including the effect of adversarial training on the interpretability of the learnt features and robustness to unseen threat models. An ablation from ResNet to ConvNeXt reveals key architectural changes leading to almost $10\%$ higher $\ell_\infty$-robustness.
Pose Attention-Guided Profile-to-Frontal Face Recognition
Venue: arXiv cs.CV 2022
Year: 2022

In recent years, face recognition systems have achieved exceptional success due to promising advances in deep learning architectures. However, they still fail to achieve expected accuracy when matching profile images against a gallery of frontal images. Current approaches either perform pose normalization (i.e., frontalization) or disentangle pose information for face recognition. We instead propose a new approach to utilize pose as an auxiliary information via an attention mechanism. In this paper, we hypothesize that pose attended information using an attention mechanism can guide contextual and distinctive feature extraction from profile faces, which further benefits a better representation learning in an embedded domain. To achieve this, first, we design a unified coupled profile-to-frontal face recognition network. It learns the mapping from faces to a compact embedding subspace via a class-specific contrastive loss. Second, we develop a novel pose attention block (PAB) to specially guide the pose-agnostic feature extraction from profile faces. To be more specific, PAB is designed to explicitly help the network to focus on important features along both channel and spatial dimension while learning discriminative yet pose invariant features in an embedding subspace. To validate the effectiveness of our proposed method, we conduct experiments on both controlled and in the wild benchmarks including Multi-PIE, CFP, IJBC, and show superiority over the state of the arts.
MR4MR: Mixed Reality for Melody Reincarnation
Venue: arXiv cs.HC 2022
Year: 2022

There is a long history of an effort made to explore musical elements with the entities and spaces around us, such as musique concrète and ambient music. In the context of computer music and digital art, interactive experiences that concentrate on the surrounding objects and physical spaces have also been designed. In recent years, with the development and popularization of devices, an increasing number of works have been designed in Extended Reality to create such musical experiences. In this paper, we describe MR4MR, a sound installation work that allows users to experience melodies produced from interactions with their surrounding space in the context of Mixed Reality (MR). Using HoloLens, an MR head-mounted display, users can bump virtual objects that emit sound against real objects in their surroundings. Then, by continuously creating a melody following the sound made by the object and re-generating randomly and gradually changing melody using music generation machine learning models, users can feel their ambient melody "reincarnating".
Can We Solve 3D Vision Tasks Starting from A 2D Vision Transformer?
Venue: arXiv cs.CV 2022
Year: 2022

Vision Transformers (ViTs) have proven to be effective, in solving 2D image understanding tasks by training over large-scale image datasets; and meanwhile as a somehow separate track, in modeling the 3D visual world too such as voxels or point clouds. However, with the growing hope that transformers can become the "universal" modeling tool for heterogeneous data, ViTs for 2D and 3D tasks have so far adopted vastly different architecture designs that are hardly transferable. That invites an (over-)ambitious question: can we close the gap between the 2D and 3D ViT architectures? As a piloting study, this paper demonstrates the appealing promise to understand the 3D visual world, using a standard 2D ViT architecture, with only minimal customization at the input and output levels without redesigning the pipeline. To build a 3D ViT from its 2D sibling, we "inflate" the patch embedding and token sequence, accompanied with new positional encoding mechanisms designed to match the 3D data geometry. The resultant "minimalist" 3D ViT, named Simple3D-Former, performs surprisingly robustly on popular 3D tasks such as object classification, point cloud segmentation and indoor scene detection, compared to highly customized 3D-specific designs. It can hence act as a strong baseline for new 3D ViTs. Moreover, we note that pursing a unified 2D-3D ViT design has practical relevance besides just scientific curiosity. Specifically, we demonstrate that Simple3D-Former naturally enables to exploit the wealth of pre-trained weights from large-scale realistic 2D images (e.g., ImageNet), which can be plugged in to enhancing the 3D task performance "for free".
uChecker: Masked Pretrained Language Models as Unsupervised Chinese Spelling Checkers
Venue: arXiv cs.CL 2022
Year: 2022

The task of Chinese Spelling Check (CSC) is aiming to detect and correct spelling errors that can be found in the text. While manually annotating a high-quality dataset is expensive and time-consuming, thus the scale of the training dataset is usually very small (e.g., SIGHAN15 only contains 2339 samples for training), therefore supervised-learning based models usually suffer the data sparsity limitation and over-fitting issue, especially in the era of big language models. In this paper, we are dedicated to investigating the \textbf{unsupervised} paradigm to address the CSC problem and we propose a framework named \textbf{uChecker} to conduct unsupervised spelling error detection and correction. Masked pretrained language models such as BERT are introduced as the backbone model considering their powerful language diagnosis capability. Benefiting from the various and flexible MASKing operations, we propose a Confusionset-guided masking strategy to fine-train the masked language model to further improve the performance of unsupervised detection and correction. Experimental results on standard datasets demonstrate the effectiveness of our proposed model uChecker in terms of character-level and sentence-level Accuracy, Precision, Recall, and F1-Measure on tasks of spelling error detection and correction respectively.