pwc-archive/papers-with-abstracts huggingface dataset:
EE-MLLM: A Data-Efficient and Compute-Efficient Multimodal Large Language Model
Authors: Feipeng Ma, Yizhou Zhou, Hebei Li, Zilong He, Siying Wu, Fengyun Rao, Yueyi Zhang, Xiaoyan Sun
Date: 2024-08-21 00:00:00
Tasks: Computational Efficiency, Language Modeling, Language Modelling, Large Language Model, Multimodal Large Language Model, TextVQA

In the realm of multimodal research, numerous studies leverage substantial image-text pairs to conduct modal alignment learning, transforming Large Language Models (LLMs) into Multimodal LLMs and excelling in a variety of visual-language tasks. The prevailing methodologies primarily fall into two categories: self-attention-based and cross-attention-based methods. While self-attention-based methods offer superior data efficiency due to their simple MLP architecture, they often suffer from lower computational efficiency due to concatenating visual and textual tokens as input for LLM. Conversely, cross-attention-based methods, although less data-efficient due to additional learnable parameters, exhibit higher computational efficiency by avoiding long sequence input for LLM. To address these trade-offs, we introduce the Data-Efficient and Compute-Efficient Multimodal Large Language Model (EE-MLLM). Without introducing additional modules or learnable parameters, EE-MLLM achieves both data and compute efficiency. Specifically, we modify the original self-attention mechanism in MLLM to a composite attention mechanism. This mechanism has two key characteristics: 1) Eliminating the computational overhead of self-attention within visual tokens to achieve compute efficiency, and 2) Reusing the weights on each layer of LLM to facilitate effective modality alignment between vision and language for data efficiency. Experimental results demonstrate the effectiveness of EE-MLLM across a range of benchmarks, including general-purpose datasets like MMBench and SeedBench, as well as fine-grained tasks such as TextVQA and DocVQA.
NuSegDG: Integration of Heterogeneous Space and Gaussian Kernel for Domain-Generalized Nuclei Segmentation
Authors: Zhenye Lou, Qing Xu, Zekun Jiang, Xiangjian He, Zhen Chen, Yi Wang, Chenxin Li, Maggie M. He, Wenting Duan
Date: 2024-08-21 00:00:00
Tasks: Decoder, Domain Generalization, Image Segmentation, Instance Segmentation, Segmentation, Semantic Segmentation

Domain-generalized nuclei segmentation refers to the generalizability of models to unseen domains based on knowledge learned from source domains and is challenged by various image conditions, cell types, and stain strategies. Recently, the Segment Anything Model (SAM) has made great success in universal image segmentation by interactive prompt modes (e.g., point and box). Despite its strengths, the original SAM presents limited adaptation to medical images. Moreover, SAM requires providing manual bounding box prompts for each object to produce satisfactory segmentation masks, so it is laborious in nuclei segmentation scenarios. To address these limitations, we propose a domain-generalizable framework for nuclei image segmentation, abbreviated to NuSegDG. Specifically, we first devise a Heterogeneous Space Adapter (HS-Adapter) to learn multi-dimensional feature representations of different nuclei domains by injecting a small number of trainable parameters into the image encoder of SAM. To alleviate the labor-intensive requirement of manual prompts, we introduce a Gaussian-Kernel Prompt Encoder (GKP-Encoder) to generate density maps driven by a single point, which guides segmentation predictions by mixing position prompts and semantic prompts. Furthermore, we present a Two-Stage Mask Decoder (TSM-Decoder) to effectively convert semantic masks to instance maps without the manual demand for morphological shape refinement. Based on our experimental evaluations, the proposed NuSegDG demonstrates state-of-the-art performance in nuclei instance segmentation, exhibiting superior domain generalization capabilities. The source code is available at https://github.com/xq141839/NuSegDG.
Embedding Ordinality to Binary Loss Function for Improving Solar Flare Forecasting
Authors: Chetraj Pandey, Anli Ji, Jinsu Hong, Rafal A. Angryk, Berkay Aydin
Date: 2024-08-21 00:00:00
Tasks: Solar Flare Prediction, Transfer Learning

In this paper, we propose a novel loss function aimed at optimizing the binary flare prediction problem by embedding the intrinsic ordinal flare characteristics into the binary cross-entropy (BCE) loss function. This modification is intended to provide the model with better guidance based on the ordinal characteristics of the data and improve the overall performance of the models. For our experiments, we employ a ResNet34-based model with transfer learning to predict $\geq$M-class flares by utilizing the shape-based features of magnetograms of active region (AR) patches spanning from $-$90$^{\circ}$ to $+$90$^{\circ}$ of solar longitude as our input data. We use a composite skill score (CSS) as our evaluation metric, which is calculated as the geometric mean of the True Skill Score (TSS) and the Heidke Skill Score (HSS) to rank and compare our models' performance. The primary contributions of this work are as follows: (i) We introduce a novel approach to encode ordinality into a binary loss function showing an application to solar flare prediction, (ii) We enhance solar flare forecasting by enabling flare predictions for each AR across the entire solar disk, without any longitudinal restrictions, and evaluate and compare performance. (iii) Our candidate model, optimized with the proposed loss function, shows an improvement of $\sim$7%, $\sim$4%, and $\sim$3% for AR patches within $\pm$30$^\circ$, $\pm$60$^\circ$, and $\pm$90$^\circ$ of solar longitude, respectively in terms of CSS, when compared with standard BCE. Additionally, we demonstrate the ability to issue flare forecasts for ARs in near-limb regions (regions between $\pm$60$^{\circ}$ to $\pm$90$^{\circ}$) with a CSS=0.34 (TSS=0.50 and HSS=0.23), expanding the scope of AR-based models for solar flare prediction. This advances the reliability of solar flare forecasts, leading to more effective prediction capabilities.
MambaCSR: Dual-Interleaved Scanning for Compressed Image Super-Resolution With SSMs
Authors: Yulin Ren, Xin Li, Mengxi Guo, Bingchen Li, Shijie Zhao, Zhibo Chen
Date: 2024-08-21 00:00:00
Tasks: Compressed Image Super-resolution, Image Super-Resolution, Mamba, Super-Resolution

We present MambaCSR, a simple but effective framework based on Mamba for the challenging compressed image super-resolution (CSR) task. Particularly, the scanning strategies of Mamba are crucial for effective contextual knowledge modeling in the restoration process despite it relying on selective state space modeling for all tokens. In this work, we propose an efficient dual-interleaved scanning paradigm (DIS) for CSR, which is composed of two scanning strategies: (i) hierarchical interleaved scanning is designed to comprehensively capture and utilize the most potential contextual information within an image by simultaneously taking advantage of the local window-based and sequential scanning methods; (ii) horizontal-to-vertical interleaved scanning is proposed to reduce the computational cost by leaving the redundancy between the scanning of different directions. To overcome the non-uniform compression artifacts, we also propose position-aligned cross-scale scanning to model multi-scale contextual information. Experimental results on multiple benchmarks have shown the great performance of our MambaCSR in the compressed image super-resolution task. The code will be soon available in~\textcolor{magenta}{\url{https://github.com/renyulin-f/MambaCSR}}.
GeoMeter: Probing Depth and Height Perception of Large Visual-Language Models
Authors: Shehreen Azad, Yash Jain, Rishit Garg, Yogesh S Rawat, Vibhav Vineet
Date: 2024-08-21 00:00:00

Geometric understanding is crucial for navigating and interacting with our environment. While large Vision Language Models (VLMs) demonstrate impressive capabilities, deploying them in real-world scenarios necessitates a comparable geometric understanding in visual perception. In this work, we focus on the geometric comprehension of these models; specifically targeting the depths and heights of objects within a scene. Our observations reveal that, although VLMs excel in basic geometric properties perception such as shape and size, they encounter significant challenges in reasoning about the depth and height of objects. To address this, we introduce GeoMeter, a suite of benchmark datasets encompassing Synthetic 2D, Synthetic 3D, and Real-World scenarios to rigorously evaluate these aspects. We benchmark 17 state-of-the-art VLMs using these datasets and find that they consistently struggle with both depth and height perception. Our key insights include detailed analyses of the shortcomings in depth and height reasoning capabilities of VLMs and the inherent bias present in these models. This study aims to pave the way for the development of VLMs with enhanced geometric understanding, crucial for real-world applications.
Enhancing Cross-Modal Medical Image Segmentation through Compositionality
Authors: Aniek Eijpe, Valentina Corbetta, Kalina Chupetlovska, Regina Beets-Tan, Wilson Silva
Date: 2024-08-21 00:00:00
Tasks: Disentanglement, Image Segmentation, Inductive Bias, Medical Image Segmentation, Segmentation, Semantic Segmentation

Cross-modal medical image segmentation presents a significant challenge, as different imaging modalities produce images with varying resolutions, contrasts, and appearances of anatomical structures. We introduce compositionality as an inductive bias in a cross-modal segmentation network to improve segmentation performance and interpretability while reducing complexity. The proposed network is an end-to-end cross-modal segmentation framework that enforces compositionality on the learned representations using learnable von Mises-Fisher kernels. These kernels facilitate content-style disentanglement in the learned representations, resulting in compositional content representations that are inherently interpretable and effectively disentangle different anatomical structures. The experimental results demonstrate enhanced segmentation performance and reduced computational costs on multiple medical datasets. Additionally, we demonstrate the interpretability of the learned compositional features. Code and checkpoints will be publicly available at: https://github.com/Trustworthy-AI-UU-NKI/Cross-Modal-Segmentation.
On Learnable Parameters of Optimal and Suboptimal Deep Learning Models
Authors: Ziwei Zheng, HuiZhi Liang, Vaclav Snasel, Vito Latora, Panos Pardalos, Giuseppe Nicosia, Varun Ojha
Date: 2024-08-21 00:00:00
Tasks: Deep Learning

We scrutinize the structural and operational aspects of deep learning models, particularly focusing on the nuances of learnable parameters (weight) statistics, distribution, node interaction, and visualization. By establishing correlations between variance in weight patterns and overall network performance, we investigate the varying (optimal and suboptimal) performances of various deep-learning models. Our empirical analysis extends across widely recognized datasets such as MNIST, Fashion-MNIST, and CIFAR-10, and various deep learning models such as deep neural networks (DNNs), convolutional neural networks (CNNs), and vision transformer (ViT), enabling us to pinpoint characteristics of learnable parameters that correlate with successful networks. Through extensive experiments on the diverse architectures of deep learning models, we shed light on the critical factors that influence the functionality and efficiency of DNNs. Our findings reveal that successful networks, irrespective of datasets or models, are invariably similar to other successful networks in their converged weights statistics and distribution, while poor-performing networks vary in their weights. In addition, our research shows that the learnable parameters of widely varied deep learning models such as DNN, CNN, and ViT exhibit similar learning characteristics.
ControlCol: Controllability in Automatic Speaker Video Colorization
Authors: Rory Ward, John G. Breslin, Peter Corcoran
Date: 2024-08-21 00:00:00
Tasks: Colorization, SSIM

Adding color to black-and-white speaker videos automatically is a highly desirable technique. It is an artistic process that requires interactivity with humans for the best results. Many existing automatic video colorization systems provide little opportunity for the user to guide the colorization process. In this work, we introduce a novel automatic speaker video colorization system which provides controllability to the user while also maintaining high colorization quality relative to state-of-the-art techniques. We name this system ControlCol. ControlCol performs 3.5% better than the previous state-of-the-art DeOldify on the Grid and Lombard Grid datasets when PSNR, SSIM, FID and FVD are used as metrics. This result is also supported by our human evaluation, where in a head-to-head comparison, ControlCol is preferred 90% of the time to DeOldify. Example videos can be seen in the supplementary material.
FRAP: Faithful and Realistic Text-to-Image Generation with Adaptive Prompt Weighting
Authors: Liyao Jiang, Negar Hassanpour, Mohammad Salameh, Mohan Sai Singamsetti, Fengyu Sun, Wei Lu, Di Niu
Date: 2024-08-21 00:00:00
Tasks: Image Generation, Text to Image Generation, Text-to-Image Generation

Text-to-image (T2I) diffusion models have demonstrated impressive capabilities in generating high-quality images given a text prompt. However, ensuring the prompt-image alignment remains a considerable challenge, i.e., generating images that faithfully align with the prompt's semantics. Recent works attempt to improve the faithfulness by optimizing the latent code, which potentially could cause the latent code to go out-of-distribution and thus produce unrealistic images. In this paper, we propose FRAP, a simple, yet effective approach based on adaptively adjusting the per-token prompt weights to improve prompt-image alignment and authenticity of the generated images. We design an online algorithm to adaptively update each token's weight coefficient, which is achieved by minimizing a unified objective function that encourages object presence and the binding of object-modifier pairs. Through extensive evaluations, we show FRAP generates images with significantly higher prompt-image alignment to prompts from complex datasets, while having a lower average latency compared to recent latent code optimization methods, e.g., 4 seconds faster than D&B on the COCO-Subject dataset. Furthermore, through visual comparisons and evaluation on the CLIP-IQA-Real metric, we show that FRAP not only improves prompt-image alignment but also generates more authentic images with realistic appearances. We also explore combining FRAP with prompt rewriting LLM to recover their degraded prompt-image alignment, where we observe improvements in both prompt-image alignment and image quality.
FedGS: Federated Gradient Scaling for Heterogeneous Medical Image Segmentation
Authors: Philip Schutte, Valentina Corbetta, Regina Beets-Tan, Wilson Silva
Date: 2024-08-21 00:00:00
Tasks: Disentanglement, Federated Learning, Image Segmentation, Medical Image Segmentation, Representation Learning, Segmentation, Semantic Segmentation

Federated Learning (FL) in Deep Learning (DL)-automated medical image segmentation helps preserving privacy by enabling collaborative model training without sharing patient data. However, FL faces challenges with data heterogeneity among institutions, leading to suboptimal global models. Integrating Disentangled Representation Learning (DRL) in FL can enhance robustness by separating data into distinct representations. Existing DRL methods assume heterogeneity lies solely in style features, overlooking content-based variability like lesion size and shape. We propose FedGS, a novel FL aggregation method, to improve segmentation performance on small, under-represented targets while maintaining overall efficacy. FedGS demonstrates superior performance over FedAvg, particularly for small lesions, across PolypGen and LiTS datasets. The code and pre-trained checkpoints are available at the following link: https://github.com/Trustworthy-AI-UU-NKI/Federated-Learning-Disentanglement
Supervised Representation Learning towards Generalizable Assembly State Recognition
Authors: Tim J. Schoonbeek, Goutham Balachandran, Hans Onvlee, Tim Houben, Shao-Hsuan Hung, Jacek Kustra, Peter H. N. de With, Fons van der Sommen
Date: 2024-08-21 00:00:00
Tasks: Representation Learning

Assembly state recognition facilitates the execution of assembly procedures, offering feedback to enhance efficiency and minimize errors. However, recognizing assembly states poses challenges in scalability, since parts are frequently updated, and the robustness to execution errors remains underexplored. To address these challenges, this paper proposes an approach based on representation learning and the novel intermediate-state informed loss function modification (ISIL). ISIL leverages unlabeled transitions between states and demonstrates significant improvements in clustering and classification performance for all tested architectures and losses. Despite being trained exclusively on images without execution errors, thorough analysis on error states demonstrates that our approach accurately distinguishes between correct states and states with various types of execution errors. The integration of the proposed algorithm can offer meaningful assistance to workers and mitigate unexpected losses due to procedural mishaps in industrial settings. The code is available at: https://timschoonbeek.github.io/state_rec
Robust 3D Gaussian Splatting for Novel View Synthesis in Presence of Distractors
Authors: Paul Ungermann, Armin Ettenhofer, Matthias Nießner, Barbara Roessle
Date: 2024-08-21 00:00:00
Tasks: Novel View Synthesis

3D Gaussian Splatting has shown impressive novel view synthesis results; nonetheless, it is vulnerable to dynamic objects polluting the input data of an otherwise static scene, so called distractors. Distractors have severe impact on the rendering quality as they get represented as view-dependent effects or result in floating artifacts. Our goal is to identify and ignore such distractors during the 3D Gaussian optimization to obtain a clean reconstruction. To this end, we take a self-supervised approach that looks at the image residuals during the optimization to determine areas that have likely been falsified by a distractor. In addition, we leverage a pretrained segmentation network to provide object awareness, enabling more accurate exclusion of distractors. This way, we obtain segmentation masks of distractors to effectively ignore them in the loss formulation. We demonstrate that our approach is robust to various distractors and strongly improves rendering quality on distractor-polluted scenes, improving PSNR by 1.86dB compared to 3D Gaussian Splatting.
Interpretable Long-term Action Quality Assessment
Authors: Xu Dong, Xinran Liu, Wanqing Li, Anthony Adeyemi-Ejeye, Andrew Gilbert
Date: 2024-08-21 00:00:00
Tasks: Action Quality Assessment, Diversity, regression

Long-term Action Quality Assessment (AQA) evaluates the execution of activities in videos. However, the length presents challenges in fine-grained interpretability, with current AQA methods typically producing a single score by averaging clip features, lacking detailed semantic meanings of individual clips. Long-term videos pose additional difficulty due to the complexity and diversity of actions, exacerbating interpretability challenges. While query-based transformer networks offer promising long-term modeling capabilities, their interpretability in AQA remains unsatisfactory due to a phenomenon we term Temporal Skipping, where the model skips self-attention layers to prevent output degradation. To address this, we propose an attention loss function and a query initialization method to enhance performance and interpretability. Additionally, we introduce a weight-score regression module designed to approximate the scoring patterns observed in human judgments and replace conventional single-score regression, improving the rationality of interpretability. Our approach achieves state-of-the-art results on three real-world, long-term AQA benchmarks. Our code is available at: https://github.com/dx199771/Interpretability-AQA
LiFCal: Online Light Field Camera Calibration via Bundle Adjustment
Authors: Aymeric Fleith, Doaa Ahmed, Daniel Cremers, Niclas Zeller
Date: 2024-08-21 00:00:00
Tasks: Camera Calibration, Depth Estimation

We propose LiFCal, a novel geometric online calibration pipeline for MLA-based light field cameras. LiFCal accurately determines model parameters from a moving camera sequence without precise calibration targets, integrating arbitrary metric scaling constraints. It optimizes intrinsic parameters of the light field camera model, the 3D coordinates of a sparse set of scene points and camera poses in a single bundle adjustment defined directly on micro image points. We show that LiFCal can reliably and repeatably calibrate a focused plenoptic camera using different input sequences, providing intrinsic camera parameters extremely close to state-of-the-art methods, while offering two main advantages: it can be applied in a target-free scene, and it is implemented online in a complete and continuous pipeline. Furthermore, we demonstrate the quality of the obtained camera parameters in downstream tasks like depth estimation and SLAM. Webpage: https://lifcal.github.io/
Exploring Robustness of Visual State Space model against Backdoor Attacks
Authors: Cheng-Yi Lee, Cheng-Chang Tsai, Chia-Mu Yu, Chun-Shien Lu
Date: 2024-08-21 00:00:00

Visual State Space Model (VSS) has demonstrated remarkable performance in various computer vision tasks. However, in the process of development, backdoor attacks have brought severe challenges to security. Such attacks cause an infected model to predict target labels when a specific trigger is activated, while the model behaves normally on benign samples. In this paper, we conduct systematic experiments to comprehend on robustness of VSS through the lens of backdoor attacks, specifically how the state space model (SSM) mechanism affects robustness. We first investigate the vulnerability of VSS to different backdoor triggers and reveal that the SSM mechanism, which captures contextual information within patches, makes the VSS model more susceptible to backdoor triggers compared to models without SSM. Furthermore, we analyze the sensitivity of the VSS model to patch processing techniques and discover that these triggers are effectively disrupted. Based on these observations, we consider an effective backdoor for the VSS model that recurs in each patch to resist patch perturbations. Extensive experiments across three datasets and various backdoor attacks reveal that the VSS model performs comparably to Transformers (ViTs) but is less robust than the Gated CNNs, which comprise only stacked Gated CNN blocks without SSM.