OpenRaiser/Intern-Atlas huggingface dataset:
An Image is Worth Multiple Words: Multi-attribute Inversion for Constrained Text-to-Image Synthesis
Venue: arXiv cs.CV 2023
Year: 2023

We consider the problem of constraining diffusion model outputs with a user-supplied reference image. Our key objective is to extract multiple attributes (e.g., color, object, layout, style) from this single reference image, and then generate new samples with them. One line of existing work proposes to invert the reference images into a single textual conditioning vector, enabling generation of new samples with this learned token. These methods, however, do not learn multiple tokens that are necessary to condition model outputs on the multiple attributes noted above. Another line of techniques expand the inversion space to learn multiple embeddings but they do this only along the layer dimension (e.g., one per layer of the DDPM model) or the timestep dimension (one for a set of timesteps in the denoising process), leading to suboptimal attribute disentanglement. To address the aforementioned gaps, the first contribution of this paper is an extensive analysis to determine which attributes are captured in which dimension of the denoising process. As noted above, we consider both the time-step dimension (in reverse denoising) as well as the DDPM model layer dimension. We observe that often a subset of these attributes are captured in the same set of model layers and/or across same denoising timesteps. For instance, color and style are captured across same U-Net layers, whereas layout and color are captured across same timestep stages. Consequently, an inversion process that is designed only for the time-step dimension or the layer dimension is insufficient to disentangle all attributes. This leads to our second contribution where we design a new multi-attribute inversion algorithm, MATTE, with associated disentanglement-enhancing regularization losses, that operates across both dimensions and explicitly leads to four disentangled tokens (color, style, layout, and object).
Provably Efficient CVaR RL in Low-rank MDPs
Venue: arXiv cs.LG 2023
Year: 2023

We study risk-sensitive Reinforcement Learning (RL), where we aim to maximize the Conditional Value at Risk (CVaR) with a fixed risk tolerance $τ$. Prior theoretical work studying risk-sensitive RL focuses on the tabular Markov Decision Processes (MDPs) setting. To extend CVaR RL to settings where state space is large, function approximation must be deployed. We study CVaR RL in low-rank MDPs with nonlinear function approximation. Low-rank MDPs assume the underlying transition kernel admits a low-rank decomposition, but unlike prior linear models, low-rank MDPs do not assume the feature or state-action representation is known. We propose a novel Upper Confidence Bound (UCB) bonus-driven algorithm to carefully balance the interplay between exploration, exploitation, and representation learning in CVaR RL. We prove that our algorithm achieves a sample complexity of $\tilde{O}\left(\frac{H^7 A^2 d^4}{τ^2 ε^2}\right)$ to yield an $ε$-optimal CVaR, where $H$ is the length of each episode, $A$ is the capacity of action space, and $d$ is the dimension of representations. Computational-wise, we design a novel discretized Least-Squares Value Iteration (LSVI) algorithm for the CVaR objective as the planning oracle and show that we can find the near-optimal policy in a polynomial running time with a Maximum Likelihood Estimation oracle. To our knowledge, this is the first provably efficient CVaR RL algorithm in low-rank MDPs.
Evaluating Supervision Levels Trade-Offs for Infrared-Based People Counting
Venue: arXiv cs.CV 2023
Year: 2023

Object detection models are commonly used for people counting (and localization) in many applications but require a dataset with costly bounding box annotations for training. Given the importance of privacy in people counting, these models rely more and more on infrared images, making the task even harder. In this paper, we explore how weaker levels of supervision can affect the performance of deep person counting architectures for image classification and point-level localization. Our experiments indicate that counting people using a CNN Image-Level model achieves competitive results with YOLO detectors and point-level models, yet provides a higher frame rate and a similar amount of model parameters.
Energizing Federated Learning via Filter-Aware Attention
Venue: arXiv cs.CV 2023
Year: 2023

Federated learning (FL) is a promising distributed paradigm, eliminating the need for data sharing but facing challenges from data heterogeneity. Personalized parameter generation through a hypernetwork proves effective, yet existing methods fail to personalize local model structures. This leads to redundant parameters struggling to adapt to diverse data distributions. To address these limitations, we propose FedOFA, utilizing personalized orthogonal filter attention for parameter recalibration. The core is the Two-stream Filter-aware Attention (TFA) module, meticulously designed to extract personalized filter-aware attention maps, incorporating Intra-Filter Attention (IntraFa) and Inter-Filter Attention (InterFA) streams. These streams enhance representation capability and explore optimal implicit structures for local models. Orthogonal regularization minimizes redundancy by averting inter-correlation between filters. Furthermore, we introduce an Attention-Guided Pruning Strategy (AGPS) for communication efficiency. AGPS selectively retains crucial neurons while masking redundant ones, reducing communication costs without performance sacrifice. Importantly, FedOFA operates on the server side, incurring no additional computational cost on the client, making it advantageous in communication-constrained scenarios. Extensive experiments validate superior performance over state-of-the-art approaches, with code availability upon paper acceptance.
FlashOcc: Fast and Memory-Efficient Occupancy Prediction via Channel-to-Height Plugin
Venue: arXiv cs.CV 2023
Year: 2023

Given the capability of mitigating the long-tail deficiencies and intricate-shaped absence prevalent in 3D object detection, occupancy prediction has become a pivotal component in autonomous driving systems. However, the procession of three-dimensional voxel-level representations inevitably introduces large overhead in both memory and computation, obstructing the deployment of to-date occupancy prediction approaches. In contrast to the trend of making the model larger and more complicated, we argue that a desirable framework should be deployment-friendly to diverse chips while maintaining high precision. To this end, we propose a plug-and-play paradigm, namely FlashOCC, to consolidate rapid and memory-efficient occupancy prediction while maintaining high precision. Particularly, our FlashOCC makes two improvements based on the contemporary voxel-level occupancy prediction approaches. Firstly, the features are kept in the BEV, enabling the employment of efficient 2D convolutional layers for feature extraction. Secondly, a channel-to-height transformation is introduced to lift the output logits from the BEV into the 3D space. We apply the FlashOCC to diverse occupancy prediction baselines on the challenging Occ3D-nuScenes benchmarks and conduct extensive experiments to validate the effectiveness. The results substantiate the superiority of our plug-and-play paradigm over previous state-of-the-art methods in terms of precision, runtime efficiency, and memory costs, demonstrating its potential for deployment. The code will be made available.
Navigating Cultural Diversity: Barriers and Potentials in Multicultural Agile Software Development Teams
Venue: arXiv cs.SE 2023
Year: 2023

Context: Social aspects are of high importance for being successful using agile methods in software development. People are influenced by their cultural imprint, as the underlying cultural values are guiding us in how we think and act. Thus, one may assume that in multicultural agile software development teams, cultural characteristics influence the result in terms of quality of the team work and consequently, the product to be delivered. Objective: We aim to identify barriers and potentials that may arise in multicultural agile software development teams to provide valuable strategies for both researchers and practitioners faced with barriers or unrealized potentials of cultural diversity. Method: The study is designed as a single-case study with two units of analysis using a mixed-method design consisting quantitative and qualitative methods. Results: First, our results suggest that the cultural characteristics at the team level need to be analyzed individually in intercultural teams, Second, we identified key potentials regarding cultural characteristics providing key potentials such as a individual team subculture that fits agile values like open communication. Third, we derived strategies supporting the potentials of cultural diversity in agile software development teams. Conclusion: Our findings show, that a deeper understanding of cultural influences in multicultural agile software development teams is needed. Based on the results, we already prepare future work to validate the results in other industries.
Concept Sliders: LoRA Adaptors for Precise Control in Diffusion Models
Venue: arXiv cs.CV 2023
Year: 2023

We present a method to create interpretable concept sliders that enable precise control over attributes in image generations from diffusion models. Our approach identifies a low-rank parameter direction corresponding to one concept while minimizing interference with other attributes. A slider is created using a small set of prompts or sample images; thus slider directions can be created for either textual or visual concepts. Concept Sliders are plug-and-play: they can be composed efficiently and continuously modulated, enabling precise control over image generation. In quantitative experiments comparing to previous editing techniques, our sliders exhibit stronger targeted edits with lower interference. We showcase sliders for weather, age, styles, and expressions, as well as slider compositions. We show how sliders can transfer latents from StyleGAN for intuitive editing of visual concepts for which textual description is difficult. We also find that our method can help address persistent quality issues in Stable Diffusion XL including repair of object deformations and fixing distorted hands. Our code, data, and trained sliders are available at https://sliders.baulab.info/
Multi-view Graph Convolution for Participant Recommendation
Venue: arXiv cs.IR 2023
Year: 2023

Social networks have become essential for people's lives. The proliferation of web services further expands social networks at an unprecedented scale, leading to immeasurable commercial value for online platforms. Recently, the group buying (GB) business mode is prevalent and also becoming more popular in E-commerce. GB explicitly forms groups of users with similar interests to secure better discounts from the merchants, often operating within social networks. It is a novel way to further unlock the commercial value by explicitly utilizing the online social network in E-commerce. Participant recommendation, a fundamental problem emerging together with GB, aims to find the participants for a launched group buying process with an initiator and a target item to increase the GB success rate. This paper proposes Multi-View Graph Convolution for Participant Recommendation (MVPRec) to tackle this problem. To differentiate the roles of users (Initiator/Participant) within the GB process, we explicitly reconstruct historical GB data into initiator-view and participant-view graphs. Together with the social graph, we obtain a multi-view user representation with graph encoders. Then MVPRec fuses the GB and social representation with an attention module to obtain the user representation and learns a matching score with the initiator's social friends via a multi-head attention mechanism. Social friends with the Top-k matching score are recommended for the corresponding GB process. Experiments on three datasets justify the effectiveness of MVPRec in the emerging participant recommendation problem.
LABELMAKER: Automatic Semantic Label Generation from RGB-D Trajectories
Venue: arXiv cs.CV 2023
Year: 2023

Semantic annotations are indispensable to train or evaluate perception models, yet very costly to acquire. This work introduces a fully automated 2D/3D labeling framework that, without any human intervention, can generate labels for RGB-D scans at equal (or better) level of accuracy than comparable manually annotated datasets such as ScanNet. Our approach is based on an ensemble of state-of-the-art segmentation models and 3D lifting through neural rendering. We demonstrate the effectiveness of our LabelMaker pipeline by generating significantly better labels for the ScanNet datasets and automatically labelling the previously unlabeled ARKitScenes dataset. Code and models are available at https://labelmaker.org
Unifying Corroborative and Contributive Attributions in Large Language Models
Venue: arXiv cs.CL 2023
Year: 2023

As businesses, products, and services spring up around large language models, the trustworthiness of these models hinges on the verifiability of their outputs. However, methods for explaining language model outputs largely fall across two distinct fields of study which both use the term "attribution" to refer to entirely separate techniques: citation generation and training data attribution. In many modern applications, such as legal document generation and medical question answering, both types of attributions are important. In this work, we argue for and present a unified framework of large language model attributions. We show how existing methods of different types of attribution fall under the unified framework. We also use the framework to discuss real-world use cases where one or both types of attributions are required. We believe that this unified framework will guide the use case driven development of systems that leverage both types of attribution, as well as the standardization of their evaluation.
Improvements in Interlayer Pipelining of CNN Accelerators Using Genetic Algorithms
Venue: arXiv cs.AR 2023
Year: 2023

Deploying Convolutional Neural Networks (CNNs) on edge platforms necessitates efficient hardware acceleration. Any unnecessary data movement in such accelerators can unacceptably degrade performance and efficiency. To address this, we develop a layer fusion technique targeting CNNs, that reduces off-chip data communication using a Genetic Algorithm (GA) applied to graph-based topological sort. Results show a 1.8$\times$ increase in energy efficiency and 1.9$\times$ improvement in energy-delay product (EDP) for MobileNet-v3 on a SIMBA-like mobile architecture. Our approach consistently improves workload performance, averaging 1.4$\times$ improvement to EDP for SIMBA and 1.12$\times$ for Eyeriss.
Procedural Generation of Grain Orientations using the Wave Function Collapse Algorithm
Venue: arXiv cs.CV 2023
Year: 2023

Statistics of grain sizes and orientations in metals correlate to the material's mechanical properties. Reproducing representative volume elements for further analysis of deformation and failure in metals, like 316L stainless steel, is particularly important due to their wide use in manufacturing goods today. Two approaches, initially created for video games, were considered for the procedural generation of representative grain microstructures. The first is the Wave Function Collapse (WFC) algorithm, and the second is constraint propagation and probabilistic inference through Markov Junior, a free and open-source software. This study aimed to investigate these two algorithms' effectiveness in using reference electron backscatter diffraction (EBSD) maps and recreating a statistically similar one that could be used in further research. It utilized two stainless steel EBSD maps as references to test both algorithms. First, the WFC algorithm was too constricting and, thus, incapable of producing images that resembled EBSDs. The second, MarkovJunior, was much more effective in creating a Voronoi tessellation that could be used to create an EBSD map in Python. When comparing the results between the reference and the generated EBSD, we discovered that the orientation and volume fractions were extremely similar. With the study, it was concluded that MarkovJunior is an effective machine learning tool that can reproduce representative grain microstructures.
Power grid operational risk assessment using graph neural network surrogates
Venue: arXiv cs.LG 2023
Year: 2023

We investigate the utility of graph neural networks (GNNs) as proxies of power grid operational decision-making algorithms (optimal power flow (OPF) and security-constrained unit commitment (SCUC)) to enable rigorous quantification of the operational risk. To conduct principled risk analysis, numerous Monte Carlo (MC) samples are drawn from the (foretasted) probability distributions of spatio-temporally correlated stochastic grid variables. The corresponding OPF and SCUC solutions, which are needed to quantify the risk, are generated using traditional OPF and SCUC solvers to generate data for training GNN model(s). The GNN model performance is evaluated in terms of the accuracy of predicting quantities of interests (QoIs) derived from the decision variables in OPF and SCUC. Specifically, we focus on thermal power generation and load shedding at system and individual zone level. We also perform reliability and risk quantification based on GNN predictions and compare with that obtained from OPF/SCUC solutions. Our results demonstrate that GNNs are capable of providing fast and accurate prediction of QoIs and thus can be good surrogate models for OPF and SCUC. The excellent accuracy of GNN-based reliability and risk assessment further suggests that GNN surrogate has the potential to be applied in real-time and hours-ahead risk quantification.
ABFL: Angular Boundary Discontinuity Free Loss for Arbitrary Oriented Object Detection in Aerial Images
Venue: arXiv cs.CV 2023
Year: 2023

Arbitrary oriented object detection (AOOD) in aerial images is a widely concerned and highly challenging task, and plays an important role in many scenarios. The core of AOOD involves the representation, encoding, and feature augmentation of oriented bounding-boxes (Bboxes). Existing methods lack intuitive modeling of angle difference measurement in oriented Bbox representations. Oriented Bboxes under different representations exhibit rotational symmetry with varying periods due to angle periodicity. The angular boundary discontinuity (ABD) problem at periodic boundary positions is caused by rotational symmetry in measuring angular differences. In addition, existing methods also use additional encoding-decoding structures for oriented Bboxes. In this paper, we design an angular boundary free loss (ABFL) based on the von Mises distribution. The ABFL aims to solve the ABD problem when detecting oriented objects. Specifically, ABFL proposes to treat angles as circular data rather than linear data when measuring angle differences, aiming to introduce angle periodicity to alleviate the ABD problem and improve the accuracy of angle difference measurement. In addition, ABFL provides a simple and effective solution for various periodic boundary discontinuities caused by rotational symmetry in AOOD tasks, as it does not require additional encoding-decoding structures for oriented Bboxes. Extensive experiments on the DOTA and HRSC2016 datasets show that the proposed ABFL loss outperforms some state-of-the-art methods focused on addressing the ABD problem.
Design for Assurance: Employing Functional Verification Tools for Thwarting Hardware Trojan Threat in 3PIPs
Venue: arXiv cs.CR 2023
Year: 2023

Third-party intellectual property cores are essential building blocks of modern system-on-chip and integrated circuit designs. However, these design components usually come from vendors of different trust levels and may contain undocumented design functionality. Distinguishing such stealthy lightweight malicious design modification can be a challenging task due to the lack of a golden reference. In this work, we make a step towards design for assurance by developing a method for identifying and preventing hardware Trojans, employing functional verification tools and languages familiar to hardware designers. We dump synthesized design netlist mapped to a field programmable gate array technology library and perform switching as well as coverage analysis at the granularity of look-up-tables (LUTs) in order to identify specious signals and cells. We automatically extract and formally prove properties related to switching and coverage, which allows us to retrieve Trojan trigger condition. We further provide a solution to preventing Trojan from activation by reconfiguring the confirmed malicious LUTs. Experimental results have demonstrated that our method can detect and mitigate Trust-Hub as well as recently reported don't care Trojans.
Game Theoretic Application to Intersection Management: A Literature Review
Venue: arXiv cs.GT 2023
Year: 2023

The emergence of vehicle-to-everything (V2X) technology offers new insights into intersection management. This, however, has also presented new challenges, such as the need to understand and model the interactions of traffic participants, including their competition and cooperation behaviors. Game theory has been widely adopted to study rationally selfish or cooperative behaviors during interactions and has been applied to advanced intersection management. In this paper, we review the application of game theory to intersection management and sort out relevant studies under various levels of intelligence and connectivity. First, the problem of urban intersection management and its challenges are briefly introduced. The basic elements of game theory specifically for intersection applications are then summarized. Next, we present the game-theoretic models and solutions that have been applied to intersection management. Finally, the limitations and potential opportunities for subsequent studies within the game-theoretic application to intersection management are discussed.
Learning Part Motion of Articulated Objects Using Spatially Continuous Neural Implicit Representations
Venue: arXiv cs.CV 2023
Year: 2023

Articulated objects (e.g., doors and drawers) exist everywhere in our life. Different from rigid objects, articulated objects have higher degrees of freedom and are rich in geometries, semantics, and part functions. Modeling different kinds of parts and articulations with nerual networks plays an essential role in articulated object understanding and manipulation, and will further benefit 3D vision and robotics communities. To model articulated objects, most previous works directly encode articulated objects into feature representations, without specific designs for parts, articulations and part motions. In this paper, we introduce a novel framework that explicitly disentangles the part motion of articulated objects by predicting the transformation matrix of points on the part surface, using spatially continuous neural implicit representations to model the part motion smoothly in the space. More importantly, while many methods could only model a certain kind of joint motion (such as the revolution in the clockwise order), our proposed framework is generic to different kinds of joint motions in that transformation matrix can model diverse kinds of joint motions in the space. Quantitative and qualitative results of experiments over diverse categories of articulated objects demonstrate the effectiveness of our proposed framework.