pwc-archive/papers-with-abstracts huggingface dataset:
Residual-NeRF: Learning Residual NeRFs for Transparent Object Manipulation
Authors: Bardienus P. Duisterhof, Yuemin Mao, Si Heng Teng, Jeffrey Ichnowski
Date: 2024-05-10 00:00:00
Tasks: NeRF, Object, Transparent objects

Transparent objects are ubiquitous in industry, pharmaceuticals, and households. Grasping and manipulating these objects is a significant challenge for robots. Existing methods have difficulty reconstructing complete depth maps for challenging transparent objects, leaving holes in the depth reconstruction. Recent work has shown neural radiance fields (NeRFs) work well for depth perception in scenes with transparent objects, and these depth maps can be used to grasp transparent objects with high accuracy. NeRF-based depth reconstruction can still struggle with especially challenging transparent objects and lighting conditions. In this work, we propose Residual-NeRF, a method to improve depth perception and training speed for transparent objects. Robots often operate in the same area, such as a kitchen. By first learning a background NeRF of the scene without transparent objects to be manipulated, we reduce the ambiguity faced by learning the changes with the new object. We propose training two additional networks: a residual NeRF learns to infer residual RGB values and densities, and a Mixnet learns how to combine background and residual NeRFs. We contribute synthetic and real experiments that suggest Residual-NeRF improves depth perception of transparent objects. The results on synthetic data suggest Residual-NeRF outperforms the baselines with a 46.1% lower RMSE and a 29.5% lower MAE. Real-world qualitative experiments suggest Residual-NeRF leads to more robust depth maps with less noise and fewer holes. Website: https://residual-nerf.github.io
Prior-guided Diffusion Model for Cell Segmentation in Quantitative Phase Imaging
Authors: Zhuchen Shao, Mark A. Anastasio, Hua Li
Date: 2024-05-10 00:00:00
Tasks: Cell Segmentation, Segmentation, Semantic Segmentation

Purpose: Quantitative phase imaging (QPI) is a label-free technique that provides high-contrast images of tissues and cells without the use of chemicals or dyes. Accurate semantic segmentation of cells in QPI is essential for various biomedical applications. While DM-based segmentation has demonstrated promising results, the requirement for multiple sampling steps reduces efficiency. This study aims to enhance DM-based segmentation by introducing prior-guided content information into the starting noise, thereby minimizing inefficiencies associated with multiple sampling. Approach: A prior-guided mechanism is introduced into DM-based segmentation, replacing randomly sampled starting noise with noise informed by content information. This mechanism utilizes another trained DM and DDIM inversion to incorporate content information from the to-be-segmented images into the starting noise. An evaluation method is also proposed to assess the quality of the starting noise, considering both content and distribution information. Results: Extensive experiments on various QPI datasets for cell segmentation showed that the proposed method achieved superior performance in DM-based segmentation with only a single sampling. Ablation studies and visual analysis further highlighted the significance of content priors in DM-based segmentation. Conclusion: The proposed method effectively leverages prior content information to improve DM-based segmentation, providing accurate results while reducing the need for multiple samplings. The findings emphasize the importance of integrating content priors into DM-based segmentation methods for optimal performance.
MDNet: Multi-Decoder Network for Abdominal CT Organs Segmentation
Authors: Debesh Jha, Nikhil Kumar Tomar, Koushik Biswas, Gorkem Durak, Matthew Antalek, Zheyuan Zhang, Bin Wang, Md Mostafijur Rahman, Hongyi Pan, Alpay Medetalibeyoglu, Yury Velichko, Daniela Ladner, Amir Borhani, Ulas Bagci
Date: 2024-05-10 00:00:00
Tasks: Decoder, Segmentation, Tumor Segmentation

Accurate segmentation of organs from abdominal CT scans is essential for clinical applications such as diagnosis, treatment planning, and patient monitoring. To handle challenges of heterogeneity in organ shapes, sizes, and complex anatomical relationships, we propose a \textbf{\textit{\ac{MDNet}}}, an encoder-decoder network that uses the pre-trained \textit{MiT-B2} as the encoder and multiple different decoder networks. Each decoder network is connected to a different part of the encoder via a multi-scale feature enhancement dilated block. With each decoder, we increase the depth of the network iteratively and refine segmentation masks, enriching feature maps by integrating previous decoders' feature maps. To refine the feature map further, we also utilize the predicted masks from the previous decoder to the current decoder to provide spatial attention across foreground and background regions. MDNet effectively refines the segmentation mask with a high dice similarity coefficient (DSC) of 0.9013 and 0.9169 on the Liver Tumor segmentation (LiTS) and MSD Spleen datasets. Additionally, it reduces Hausdorff distance (HD) to 3.79 for the LiTS dataset and 2.26 for the spleen segmentation dataset, underscoring the precision of MDNet in capturing the complex contours. Moreover, \textit{\ac{MDNet}} is more interpretable and robust compared to the other baseline models.
Perceptual Crack Detection for Rendered 3D Textured Meshes
Authors: Armin Shafiee Sarvestani, Wei Zhou, Zhou Wang
Date: 2024-05-09 00:00:00
Tasks: Image Quality Assessment

Recent years have witnessed many advancements in the applications of 3D textured meshes. As the demand continues to rise, evaluating the perceptual quality of this new type of media content becomes crucial for quality assurance and optimization purposes. Different from traditional image quality assessment, crack is an annoying artifact specific to rendered 3D meshes that severely affects their perceptual quality. In this work, we make one of the first attempts to propose a novel Perceptual Crack Detection (PCD) method for detecting and localizing crack artifacts in rendered meshes. Specifically, motivated by the characteristics of the human visual system (HVS), we adopt contrast and Laplacian measurement modules to characterize crack artifacts and differentiate them from other undesired artifacts. Extensive experiments on large-scale public datasets of 3D textured meshes demonstrate effectiveness and efficiency of the proposed PCD method in correct localization and detection of crack artifacts. %Specifically, We propose a full-reference crack artifact localization method that operates on a pair of input snapshots of distorted and reference 3D objects to generate a final crack map. Moreover, to quantify the performance of the proposed detection method and validate its effectiveness, we propose a simple yet effective weighting mechanism to incorporate the resulting crack map into classical quality assessment (QA) models, which creates significant performance improvement in predicting the perceptual image quality when tested on public datasets of static 3D textured meshes. A software release of the proposed method is publicly available at: https://github.com/arshafiee/crack-detection-VVM
Enhanced Multimodal Content Moderation of Children's Videos using Audiovisual Fusion
Authors: Syed Hammad Ahmed, Muhammad Junaid Khan, Gita Sukthankar
Date: 2024-05-09 00:00:00
Tasks: Prompt Learning, Robust classification

Due to the rise in video content creation targeted towards children, there is a need for robust content moderation schemes for video hosting platforms. A video that is visually benign may include audio content that is inappropriate for young children while being impossible to detect with a unimodal content moderation system. Popular video hosting platforms for children such as YouTube Kids still publish videos which contain audio content that is not conducive to a child's healthy behavioral and physical development. A robust classification of malicious videos requires audio representations in addition to video features. However, recent content moderation approaches rarely employ multimodal architectures that explicitly consider non-speech audio cues. To address this, we present an efficient adaptation of CLIP (Contrastive Language-Image Pre-training) that can leverage contextual audio cues for enhanced content moderation. We incorporate 1) the audio modality and 2) prompt learning, while keeping the backbone modules of each modality frozen. We conduct our experiments on a multimodal version of the MOB (Malicious or Benign) dataset in supervised and few-shot settings.
Rethinking Efficient and Effective Point-based Networks for Event Camera Classification and Regression: EventMamba
Authors: Hongwei Ren, Yue Zhou, Jiadong Zhu, Haotian Fu, Yulong Huang, Xiaopeng Lin, Yuetong Fang, Fei Ma, Hao Yu, Bojun Cheng
Date: 2024-05-09 00:00:00
Tasks: Action Recognition, Mamba, Temporal Information Extraction

Event cameras draw inspiration from biological systems, boasting low latency and high dynamic range while consuming minimal power. The most current approach to processing Event Cloud often involves converting it into frame-based representations, which neglects the sparsity of events, loses fine-grained temporal information, and increases the computational burden. In contrast, Point Cloud is a popular representation for processing 3-dimensional data and serves as an alternative method to exploit local and global spatial features. Nevertheless, previous point-based methods show an unsatisfactory performance compared to the frame-based method in dealing with spatio-temporal event streams. In order to bridge the gap, we propose EventMamba, an efficient and effective framework based on Point Cloud representation by rethinking the distinction between Event Cloud and Point Cloud, emphasizing vital temporal information. The Event Cloud is subsequently fed into a hierarchical structure with staged modules to process both implicit and explicit temporal features. Specifically, we redesign the global extractor to enhance explicit temporal extraction among a long sequence of events with temporal aggregation and State Space Model (SSM) based Mamba. Our model consumes minimal computational resources in the experiments and still exhibits SOTA point-based performance on six different scales of action recognition datasets. It even outperformed all frame-based methods on both Camera Pose Relocalization (CPR) and eye-tracking regression tasks. Our code is available at: https://github.com/rhwxmx/EventMamba.
A Mixture of Experts Approach to 3D Human Motion Prediction
Authors: Edmund Shieh, Joshua Lee Franco, Kang Min Bae, Tej Lalvani
Date: 2024-05-09 00:00:00
Tasks: Human motion prediction, Mixture-of-Experts, motion prediction

This project addresses the challenge of human motion prediction, a critical area for applications such as au- tonomous vehicle movement detection. Previous works have emphasized the need for low inference times to provide real time performance for applications like these. Our primary objective is to critically evaluate existing model ar- chitectures, identifying their advantages and opportunities for improvement by replicating the state-of-the-art (SOTA) Spatio-Temporal Transformer model as best as possible given computational con- straints. These models have surpassed the limitations of RNN-based models and have demonstrated the ability to generate plausible motion sequences over both short and long term horizons through the use of spatio-temporal rep- resentations. We also propose a novel architecture to ad- dress challenges of real time inference speed by incorpo- rating a Mixture of Experts (MoE) block within the Spatial- Temporal (ST) attention layer. The particular variation that is used is Soft MoE, a fully-differentiable sparse Transformer that has shown promising ability to enable larger model capacity at lower inference cost. We make out code publicly available at https://github.com/edshieh/motionprediction
UnSegGNet: Unsupervised Image Segmentation using Graph Neural Networks
Authors: Kovvuri Sai Gopal Reddy, Bodduluri Saran, A. Mudit Adityaja, Saurabh J. Shigwan, Nitin Kumar
Date: 2024-05-09 00:00:00
Tasks: Image Segmentation, Object Recognition, Segmentation, Semantic Segmentation, Unsupervised Image Segmentation

Image segmentation, the process of partitioning an image into meaningful regions, plays a pivotal role in computer vision and medical imaging applications. Unsupervised segmentation, particularly in the absence of labeled data, remains a challenging task due to the inter-class similarity and variations in intensity and resolution. In this study, we extract high-level features of the input image using pretrained vision transformer. Subsequently, the proposed method leverages the underlying graph structures of the images, seeking to discover and delineate meaningful boundaries using graph neural networks and modularity based optimization criteria without relying on pre-labeled training data. Experimental results on benchmark datasets demonstrate the effectiveness and versatility of the proposed approach, showcasing competitive performance compared to the state-of-the-art unsupervised segmentation methods. This research contributes to the broader field of unsupervised medical imaging and computer vision by presenting an innovative methodology for image segmentation that aligns with real-world challenges. The proposed method holds promise for diverse applications, including medical imaging, remote sensing, and object recognition, where labeled data may be scarce or unavailable. The github repository of the code is available on [https://github.com/ksgr5566/unseggnet]
BB-Patch: BlackBox Adversarial Patch-Attack using Zeroth-Order Optimization
Authors: Satyadwyoom Kumar, Saurabh Gupta, Arun Balaji Buduru
Date: 2024-05-09 00:00:00
Tasks: Adversarial Attack, Deep Learning, valid

Deep Learning has become popular due to its vast applications in almost all domains. However, models trained using deep learning are prone to failure for adversarial samples and carry a considerable risk in sensitive applications. Most of these adversarial attack strategies assume that the adversary has access to the training data, the model parameters, and the input during deployment, hence, focus on perturbing the pixel level information present in the input image. Adversarial Patches were introduced to the community which helped in bringing out the vulnerability of deep learning models in a much more pragmatic manner but here the attacker has a white-box access to the model parameters. Recently, there has been an attempt to develop these adversarial attacks using black-box techniques. However, certain assumptions such as availability large training data is not valid for a real-life scenarios. In a real-life scenario, the attacker can only assume the type of model architecture used from a select list of state-of-the-art architectures while having access to only a subset of input dataset. Hence, we propose an black-box adversarial attack strategy that produces adversarial patches which can be applied anywhere in the input image to perform an adversarial attack.
Characterizing the Accuracy -- Efficiency Trade-off of Low-rank Decomposition in Language Models
Authors: Chakshu Moar, Faraz Tahmasebi, Michael Pellauer, Hyoukjun Kwon
Date: 2024-05-10 00:00:00
Tasks: AI Agent, Model Compression, Navigate, Quantization

Recent large language models (LLMs) employ billions of parameters to enable broad problem-solving capabilities. Such language models also tend to be memory-bound because of the dominance of matrix-vector and matrix-matrix multiplications with low arithmetic intensity. Therefore, optimizing the memory footprint and traffic is an important optimization direction for LLMs today. Model compression methods such as quantization and parameter pruning have been actively explored to achieve memory footprint and traffic optimization. However, the accuracy-efficiency trade-off of rank pruning (i.e., low-rank decomposition) for LLMs is not well-understood yet. Therefore, in this work, we characterize the accuracy-efficiency trade-off of a low-rank decomposition method, specifically Tucker decomposition, on recent language models, including an open-source LLM, Llama 2. We formalize the low-rank decomposition design space and show that the decomposition design space is enormous (e.g., O($2^{39}$) for Llama2-7B). To navigate such a vast design space, we formulate it and perform thorough case studies of accuracy-efficiency trade-offs using six widely used LLM benchmarks on BERT and Llama 2 models. Our results show that we can achieve a 9\% model size reduction with minimal accuracy drops, which range from 4\%p (\%p refers to "percentage point," which refers to the absolute difference between two percentage numbers; 74\% -> 78\% = 4\%p increase) to 10\%p, depending on the difficulty of the benchmark, without any retraining to recover accuracy after decomposition. The results show that low-rank decomposition can be a promising direction for LLM-based applications that require real-time service at scale (e.g., AI agent and real-time coding assistant), where the latency is as important as the model accuracy.
Calo-VQ: Vector-Quantized Two-Stage Generative Model in Calorimeter Simulation
Authors: Qibin Liu, Chase Shimmin, Xiulong Liu, Eli Shlizerman, Shu Li, Shih-Chieh Hsu
Date: 2024-05-10 00:00:00

We introduce a novel machine learning method developed for the fast simulation of calorimeter detector response, adapting vector-quantized variational autoencoder (VQ-VAE). Our model adopts a two-stage generation strategy: initially compressing geometry-aware calorimeter data into a discrete latent space, followed by the application of a sequence model to learn and generate the latent tokens. Extensive experimentation on the Calo-challenge dataset underscores the efficiency of our approach, showcasing a remarkable improvement in the generation speed compared with conventional method by a factor of 2000. Remarkably, our model achieves the generation of calorimeter showers within milliseconds. Furthermore, comprehensive quantitative evaluations across various metrics are performed to validate physics performance of generation.
Explaining Text Similarity in Transformer Models
Authors: Alexandros Vasileiou, Oliver Eberle
Date: 2024-05-10 00:00:00
Tasks: Information Retrieval, Retrieval, Semantic Similarity, Semantic Textual Similarity, Text Retrieval, text similarity

As Transformers have become state-of-the-art models for natural language processing (NLP) tasks, the need to understand and explain their predictions is increasingly apparent. Especially in unsupervised applications, such as information retrieval tasks, similarity models built on top of foundation model representations have been widely applied. However, their inner prediction mechanisms have mostly remained opaque. Recent advances in explainable AI have made it possible to mitigate these limitations by leveraging improved explanations for Transformers through layer-wise relevance propagation (LRP). Using BiLRP, an extension developed for computing second-order explanations in bilinear similarity models, we investigate which feature interactions drive similarity in NLP models. We validate the resulting explanations and demonstrate their utility in three corpus-level use cases, analyzing grammatical interactions, multilingual semantics, and biomedical text retrieval. Our findings contribute to a deeper understanding of different semantic similarity tasks and models, highlighting how novel explainable AI methods enable in-depth analyses and corpus-level insights.
Decomposing weather forecasting into advection and convection with neural networks
Authors: Mengxuan Chen, Ziqi Yuan, Jinxiao Zhang, Runmin Dong, Haohuan Fu
Date: 2024-05-10 00:00:00
Tasks: Graph Attention, Weather Forecasting

Operational weather forecasting models have advanced for decades on both the explicit numerical solvers and the empirical physical parameterization schemes. However, the involved high computational costs and uncertainties in these existing schemes are requiring potential improvements through alternative machine learning methods. Previous works use a unified model to learn the dynamics and physics of the atmospheric model. Contrarily, we propose a simple yet effective machine learning model that learns the horizontal movement in the dynamical core and vertical movement in the physical parameterization separately. By replacing the advection with a graph attention network and the convection with a multi-layer perceptron, our model provides a new and efficient perspective to simulate the transition of variables in atmospheric models. We also assess the model's performance over a 5-day iterative forecasting. Under the same input variables and training methods, our model outperforms existing data-driven methods with a significantly-reduced number of parameters with a resolution of 5.625 deg. Overall, this work aims to contribute to the ongoing efforts that leverage machine learning techniques for improving both the accuracy and efficiency of global weather forecasting.
The Role of Learning Algorithms in Collective Action
Authors: Omri Ben-Dov, Jake Fawkes, Samira Samadi, Amartya Sanyal
Date: 2024-05-10 00:00:00
Tasks: Inductive Bias

Collective action in machine learning is the study of the control that a coordinated group can have over machine learning algorithms. While previous research has concentrated on assessing the impact of collectives against Bayes (sub-)optimal classifiers, this perspective is limited in that it does not account for the choice of learning algorithm. Since classifiers seldom behave like Bayes classifiers and are influenced by the choice of learning algorithms along with their inherent biases, in this work we initiate the study of how the choice of the learning algorithm plays a role in the success of a collective in practical settings. Specifically, we focus on distributionally robust optimization (DRO), popular for improving a worst group error, and on the ubiquitous stochastic gradient descent (SGD), due to its inductive bias for "simpler" functions. Our empirical results, supported by a theoretical foundation, show that the effective size and success of the collective are highly dependent on properties of the learning algorithm. This highlights the necessity of taking the learning algorithm into account when studying the impact of collective action in machine learning.
No-Regret is not enough! Bandits with General Constraints through Adaptive Regret Minimization
Authors: Martino Bernasconi, Matteo Castiglioni, Andrea Celli
Date: 2024-05-10 00:00:00
Tasks: Multi-Armed Bandits

In the bandits with knapsacks framework (BwK) the learner has $m$ resource-consumption (packing) constraints. We focus on the generalization of BwK in which the learner has a set of general long-term constraints. The goal of the learner is to maximize their cumulative reward, while at the same time achieving small cumulative constraints violations. In this scenario, there exist simple instances where conventional methods for BwK fail to yield sublinear violations of constraints. We show that it is possible to circumvent this issue by requiring the primal and dual algorithm to be weakly adaptive. Indeed, even in absence on any information on the Slater's parameter $\rho$ characterizing the problem, the interplay between weakly adaptive primal and dual regret minimizers yields a "self-bounding" property of dual variables. In particular, their norm remains suitably upper bounded across the entire time horizon even without explicit projection steps. By exploiting this property, we provide best-of-both-worlds guarantees for stochastic and adversarial inputs. In the first case, we show that the algorithm guarantees sublinear regret. In the latter case, we establish a tight competitive ratio of $\rho/(1+\rho)$. In both settings, constraints violations are guaranteed to be sublinear in time. Finally, this results allow us to obtain new result for the problem of contextual bandits with linear constraints, providing the first no-$\alpha$-regret guarantees for adversarial contexts.