pwc-archive/papers-with-abstracts huggingface dataset:
Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline
Authors: Junlong Cheng, Bin Fu, Jin Ye, Guoan Wang, Tianbin Li, Haoyu Wang, Ruoyu Li, He Yao, Junren Chen, Jingwen Li, Yanzhou Su, Min Zhu, Junjun He
Date: 2024-11-19 00:00:00
Proceeding: CVPR 2025 1
Tasks: Image Segmentation, Interactive Segmentation, Medical Image Segmentation, Object Recognition, Segmentation, Semantic Segmentation

Interactive Medical Image Segmentation (IMIS) has long been constrained by the limited availability of large-scale, diverse, and densely annotated datasets, which hinders model generalization and consistent evaluation across different models. In this paper, we introduce the IMed-361M benchmark dataset, a significant advancement in general IMIS research. First, we collect and standardize over 6.4 million medical images and their corresponding ground truth masks from multiple data sources. Then, leveraging the strong object recognition capabilities of a vision foundational model, we automatically generated dense interactive masks for each image and ensured their quality through rigorous quality control and granularity management. Unlike previous datasets, which are limited by specific modalities or sparse annotations, IMed-361M spans 14 modalities and 204 segmentation targets, totaling 361 million masks-an average of 56 masks per image. Finally, we developed an IMIS baseline network on this dataset that supports high-quality mask generation through interactive inputs, including clicks, bounding boxes, text prompts, and their combinations. We evaluate its performance on medical image segmentation tasks from multiple perspectives, demonstrating superior accuracy and scalability compared to existing interactive segmentation models. To facilitate research on foundational models in medical computer vision, we release the IMed-361M and model at https://github.com/uni-medical/IMIS-Bench.
Stylecodes: Encoding Stylistic Information For Image Generation
Authors: Ciara Rowles
Date: 2024-11-19 00:00:00
Tasks: Image Generation

Diffusion models excel in image generation, but controlling them remains a challenge. We focus on the problem of style-conditioned image generation. Although example images work, they are cumbersome: srefs (style-reference codes) from MidJourney solve this issue by expressing a specific image style in a short numeric code. These have seen widespread adoption throughout social media due to both their ease of sharing and the fact they allow using an image for style control, without having to post the source images themselves. However, users are not able to generate srefs from their own images, nor is the underlying training procedure public. We propose StyleCodes: an open-source and open-research style encoder architecture and training procedure to express image style as a 20-symbol base64 code. Our experiments show that our encoding results in minimal loss in quality compared to traditional image-to-style techniques.
CLIC: Contrastive Learning Framework for Unsupervised Image Complexity Representation
Authors: Shipeng Liu, Liang Zhao, Dengfeng Chen
Date: 2024-11-19 00:00:00
Tasks: Attribute, Contrastive Learning, Image Comprehension

As an essential visual attribute, image complexity affects human image comprehension and directly influences the performance of computer vision tasks. However, accurately assessing and quantifying image complexity faces significant challenges. Previous works needed more generalization capabilities and well-labeled datasets to learn image complexity features. However, creating such datasets requires expensive manual labeling costs, and the models inevitably learn about human subjective biases. To address the above problems, we propose CLIC, an unsupervised framework based on contrastive learning, for learning image complexity representations. The method learns image complexity features on unlabeled data, avoiding the high labeling cost. Specifically, we propose a unique positive and negative sample selection strategy to reinforce the differences in complexity features. At the same time, we introduce an image prior-based Complexity-Aware Loss to constrain the learning process of the model. We conducted extensive experiments for verification, and the results show that CLIC can effectively learn the image complexity representation. CLIC obtained competitive results with supervised methods by fine-tuning on IC9600. In addition, CLIC applied to downstream tasks shows significant performance improvements, demonstrating the potential for application in various real-world scenarios. \href{https://github.com/xauat-liushipeng/CLIC}{code}
Mitigating Perception Bias: A Training-Free Approach to Enhance LMM for Image Quality Assessment
Authors: Siyi Pan, Baoliang Chen, Danni Huang, Hanwei Zhu, Lingyu Zhu, Xiangjie Sui, Shiqi Wang
Date: 2024-11-19 00:00:00
Tasks: Image Captioning, Image Quality Assessment

Despite the impressive performance of large multimodal models (LMMs) in high-level visual tasks, their capacity for image quality assessment (IQA) remains limited. One main reason is that LMMs are primarily trained for high-level tasks (e.g., image captioning), emphasizing unified image semantics extraction under varied quality. Such semantic-aware yet quality-insensitive perception bias inevitably leads to a heavy reliance on image semantics when those LMMs are forced for quality rating. In this paper, instead of retraining or tuning an LMM costly, we propose a training-free debiasing framework, in which the image quality prediction is rectified by mitigating the bias caused by image semantics. Specifically, we first explore several semantic-preserving distortions that can significantly degrade image quality while maintaining identifiable semantics. By applying these specific distortions to the query or test images, we ensure that the degraded images are recognized as poor quality while their semantics remain. During quality inference, both a query image and its corresponding degraded version are fed to the LMM along with a prompt indicating that the query image quality should be inferred under the condition that the degraded one is deemed poor quality.This prior condition effectively aligns the LMM's quality perception, as all degraded images are consistently rated as poor quality, regardless of their semantic difference.Finally, the quality scores of the query image inferred under different prior conditions (degraded versions) are aggregated using a conditional probability model. Extensive experiments on various IQA datasets show that our debiasing framework could consistently enhance the LMM performance and the code will be publicly available.
Automated 3D Physical Simulation of Open-world Scene with Gaussian Splatting
Authors: Haoyu Zhao, Hao Wang, Xingyue Zhao, Hongqiu Wang, Zhiyu Wu, Chengjiang Long, Hua Zou
Date: 2024-11-19 00:00:00
Tasks: 3D Generation, GPU, Large Language Model, Video Generation, Visual Reasoning

Recent advancements in 3D generation models have opened new possibilities for simulating dynamic 3D object movements and customizing behaviors, yet creating this content remains challenging. Current methods often require manual assignment of precise physical properties for simulations or rely on video generation models to predict them, which is computationally intensive. In this paper, we rethink the usage of multi-modal large language model (MLLM) in physics-based simulation, and present Sim Anything, a physics-based approach that endows static 3D objects with interactive dynamics. We begin with detailed scene reconstruction and object-level 3D open-vocabulary segmentation, progressing to multi-view image in-painting. Inspired by human visual reasoning, we propose MLLM-based Physical Property Perception (MLLM-P3) to predict mean physical properties of objects in a zero-shot manner. Based on the mean values and the object's geometry, the Material Property Distribution Prediction model (MPDP) model then estimates the full distribution, reformulating the problem as probability distribution estimation to reduce computational costs. Finally, we simulate objects in an open-world scene with particles sampled via the Physical-Geometric Adaptive Sampling (PGAS) strategy, efficiently capturing complex deformations and significantly reducing computational costs. Extensive experiments and user studies demonstrate our Sim Anything achieves more realistic motion than state-of-the-art methods within 2 minutes on a single GPU.
Mini-Splatting2: Building 360 Scenes within Minutes via Aggressive Gaussian Densification
Authors: Guangchi Fang, Bing Wang
Date: 2024-11-19 00:00:00

In this study, we explore the essential challenge of fast scene optimization for Gaussian Splatting. Through a thorough analysis of the geometry modeling process, we reveal that dense point clouds can be effectively reconstructed early in optimization through Gaussian representations. This insight leads to our approach of aggressive Gaussian densification, which provides a more efficient alternative to conventional progressive densification methods. By significantly increasing the number of critical Gaussians, we enhance the model capacity to capture dense scene geometry at the early stage of optimization. This strategy is seamlessly integrated into the Mini-Splatting densification and simplification framework, enabling rapid convergence without compromising quality. Additionally, we introduce visibility culling within Gaussian Splatting, leveraging per-view Gaussian importance as precomputed visibility to accelerate the optimization process. Our Mini-Splatting2 achieves a balanced trade-off among optimization time, the number of Gaussians, and rendering quality, establishing a strong baseline for future Gaussian-Splatting-based works. Our work sets the stage for more efficient, high-quality 3D scene modeling in real-world applications, and the code will be made available no matter acceptance.
Joint Vision-Language Social Bias Removal for CLIP
Authors: Haoyu Zhang, Yangyang Guo, Mohan Kankanhalli
Date: 2024-11-19 00:00:00
Proceeding: CVPR 2025 1
Tasks: Attribute

Vision-Language (V-L) pre-trained models such as CLIP show prominent capabilities in various downstream tasks. Despite this promise, V-L models are notoriously limited by their inherent social biases. A typical demonstration is that V-L models often produce biased predictions against specific groups of people, significantly undermining their real-world applicability. Existing approaches endeavor to mitigate the social bias problem in V-L models by removing biased attribute information from model embeddings. However, after our revisiting of these methods, we find that their bias removal is frequently accompanied by greatly compromised V-L alignment capabilities. We then reveal that this performance degradation stems from the unbalanced debiasing in image and text embeddings. To address this issue, we propose a novel V-L debiasing framework to align image and text biases followed by removing them from both modalities. By doing so, our method achieves multi-modal bias mitigation while maintaining the V-L alignment in the debiased embeddings. Additionally, we advocate a new evaluation protocol that can 1) holistically quantify the model debiasing and V-L alignment ability, and 2) evaluate the generalization of social bias removal models. We believe this work will offer new insights and guidance for future studies addressing the social bias problem in CLIP.
Med-2E3: A 2D-Enhanced 3D Medical Multimodal Large Language Model
Authors: Yiming Shi, Xun Zhu, Ying Hu, Chenyi Guo, Miao Li, Ji Wu
Date: 2024-11-19 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Medical Image Analysis, Medical Visual Question Answering, Multimodal Large Language Model, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

The analysis of 3D medical images is crucial for modern healthcare, yet traditional task-specific models are becoming increasingly inadequate due to limited generalizability across diverse clinical scenarios. Multimodal large language models (MLLMs) offer a promising solution to these challenges. However, existing MLLMs have limitations in fully leveraging the rich, hierarchical information embedded in 3D medical images. Inspired by clinical practice, where radiologists focus on both 3D spatial structure and 2D planar content, we propose Med-2E3, a novel MLLM for 3D medical image analysis that integrates 3D and 2D encoders. To aggregate 2D features more effectively, we design a Text-Guided Inter-Slice (TG-IS) scoring module, which scores the attention of each 2D slice based on slice contents and task instructions. To the best of our knowledge, Med-2E3 is the first MLLM to integrate both 3D and 2D features for 3D medical image analysis. Experiments on a large-scale, open-source 3D medical multimodal benchmark demonstrate that Med-2E3 exhibits task-specific attention distribution and significantly outperforms current state-of-the-art models, with a 14% improvement in report generation and a 5% gain in medical visual question answering (VQA), highlighting the model's potential in addressing complex multimodal clinical tasks. The code will be released upon acceptance.
FGP: Feature-Gradient-Prune for Efficient Convolutional Layer Pruning
Authors: Qingsong Lv, Jiasheng Sun, Sheng Zhou, Xu Zhang, Liangcheng Li, Yun Gao, Sun Qiao, Jie Song, Jiajun Bu
Date: 2024-11-19 00:00:00
Tasks: Computational Efficiency

To reduce computational overhead while maintaining model performance, model pruning techniques have been proposed. Among these, structured pruning, which removes entire convolutional channels or layers, significantly enhances computational efficiency and is compatible with hardware acceleration. However, existing pruning methods that rely solely on image features or gradients often result in the retention of redundant channels, negatively impacting inference efficiency. To address this issue, this paper introduces a novel pruning method called Feature-Gradient Pruning (FGP). This approach integrates both feature-based and gradient-based information to more effectively evaluate the importance of channels across various target classes, enabling a more accurate identification of channels that are critical to model performance. Experimental results demonstrate that the proposed method improves both model compactness and practicality while maintaining stable performance. Experiments conducted across multiple tasks and datasets show that FGP significantly reduces computational costs and minimizes accuracy loss compared to existing methods, highlighting its effectiveness in optimizing pruning outcomes. The source code is available at: https://github.com/FGP-code/FGP.
Faster Multi-GPU Training with PPLL: A Pipeline Parallelism Framework Leveraging Local Learning
Authors: Xiuyuan Guo, Chengqi Xu, Guinan Guo, Feiyu Zhu, Changpeng Cai, Peizhe Wang, Xiaoming Wei, Junhao Su, Jialin Gao
Date: 2024-11-19 00:00:00
Tasks: GPU

Currently, training large-scale deep learning models is typically achieved through parallel training across multiple GPUs. However, due to the inherent communication overhead and synchronization delays in traditional model parallelism methods, seamless parallel training cannot be achieved, which, to some extent, affects overall training efficiency. To address this issue, we present PPLL (Pipeline Parallelism based on Local Learning), a novel framework that leverages local learning algorithms to enable effective parallel training across multiple GPUs. PPLL divides the model into several distinct blocks, each allocated to a separate GPU. By utilizing queues to manage data transfers between GPUs, PPLL ensures seamless cross-GPU communication, allowing multiple blocks to execute forward and backward passes in a pipelined manner. This design minimizes idle times and prevents bottlenecks typically caused by sequential gradient updates, thereby accelerating the overall training process. We validate PPLL through extensive experiments using ResNet and Vision Transformer (ViT) architectures on CIFAR-10, SVHN, and STL-10 datasets. Our results demonstrate that PPLL significantly enhances the training speed of the local learning method while achieving comparable or even superior training speed to traditional pipeline parallelism (PP) without sacrificing model performance. In a 4-GPU training setup, PPLL accelerated local learning training on ViT and ResNet by 162% and 33%, respectively, achieving 1.25x and 0.85x the speed of traditional pipeline parallelism.
Decoupling Training-Free Guided Diffusion by ADMM
Authors: Youyuan Zhang, Zehua Liu, Zenan Li, Zhaoyu Li, James J. Clark, Xujie Si
Date: 2024-11-18 00:00:00
Proceeding: CVPR 2025 1
Tasks: Image Generation, Motion Synthesis

In this paper, we consider the conditional generation problem by guiding off-the-shelf unconditional diffusion models with differentiable loss functions in a plug-and-play fashion. While previous research has primarily focused on balancing the unconditional diffusion model and the guided loss through a tuned weight hyperparameter, we propose a novel framework that distinctly decouples these two components. Specifically, we introduce two variables ${x}$ and ${z}$, to represent the generated samples governed by the unconditional generation model and the guidance function, respectively. This decoupling reformulates conditional generation into two manageable subproblems, unified by the constraint ${x} = {z}$. Leveraging this setup, we develop a new algorithm based on the Alternating Direction Method of Multipliers (ADMM) to adaptively balance these components. Additionally, we establish the equivalence between the diffusion reverse step and the proximal operator of ADMM and provide a detailed convergence analysis of our algorithm under certain mild assumptions. Our experiments demonstrate that our proposed method ADMMDiff consistently generates high-quality samples while ensuring strong adherence to the conditioning criteria. It outperforms existing methods across a range of conditional generation tasks, including image generation with various guidance and controllable motion synthesis.
Promoting User Data Autonomy During the Dissolution of a Monopolistic Firm
Authors: Rushabh Solanki, Elliot Creager
Date: 2024-11-20 00:00:00
Tasks: Machine Unlearning

The deployment of AI in consumer products is currently focused on the use of so-called foundation models, large neural networks pre-trained on massive corpora of digital records. This emphasis on scaling up datasets and pre-training computation raises the risk of further consolidating the industry, and enabling monopolistic (or oligopolistic) behavior. Judges and regulators seeking to improve market competition may employ various remedies. This paper explores dissolution -- the breaking up of a monopolistic entity into smaller firms -- as one such remedy, focusing in particular on the technical challenges and opportunities involved in the breaking up of large models and datasets. We show how the framework of Conscious Data Contribution can enable user autonomy during under dissolution. Through a simulation study, we explore how fine-tuning and the phenomenon of "catastrophic forgetting" could actually prove beneficial as a type of machine unlearning that allows users to specify which data they want used for what purposes.
Quantum Attention for Vision Transformers in High Energy Physics
Authors: Alessandro Tesi, Gopal Ramesh Dahale, Sergei Gleyzer, Kyoungchul Kong, Tom Magorsch, Konstantin T. Matchev, Katia Matcheva
Date: 2024-11-20 00:00:00
Tasks: Computational Efficiency

We present a novel hybrid quantum-classical vision transformer architecture incorporating quantum orthogonal neural networks (QONNs) to enhance performance and computational efficiency in high-energy physics applications. Building on advancements in quantum vision transformers, our approach addresses limitations of prior models by leveraging the inherent advantages of QONNs, including stability and efficient parameterization in high-dimensional spaces. We evaluate the proposed architecture using multi-detector jet images from CMS Open Data, focusing on the task of distinguishing quark-initiated from gluon-initiated jets. The results indicate that embedding quantum orthogonal transformations within the attention mechanism can provide robust performance while offering promising scalability for machine learning challenges associated with the upcoming High Luminosity Large Hadron Collider. This work highlights the potential of quantum-enhanced models to address the computational demands of next-generation particle physics experiments.
Procurement Auctions via Approximately Optimal Submodular Optimization
Authors: Yuan Deng, Amin Karbasi, Vahab Mirrokni, Renato Paes Leme, Grigoris Velegkas, Song Zuo
Date: 2024-11-20 00:00:00

We study procurement auctions, where an auctioneer seeks to acquire services from strategic sellers with private costs. The quality of services is measured by a submodular function known to the auctioneer. Our goal is to design computationally efficient procurement auctions that (approximately) maximize the difference between the quality of the acquired services and the total cost of the sellers, while ensuring incentive compatibility (IC), individual rationality (IR) for sellers, and non-negative surplus (NAS) for the auctioneer. Our contributions are twofold: (i) we provide an improved analysis of existing algorithms for non-positive submodular function maximization, and (ii) we design efficient frameworks that transform submodular optimization algorithms into mechanisms that are IC, IR, NAS, and approximation-preserving. These frameworks apply to both the offline setting, where all sellers' bids and services are available simultaneously, and the online setting, where sellers arrive in an adversarial order, requiring the auctioneer to make irrevocable decisions. We also explore whether state-of-the-art submodular optimization algorithms can be converted into descending auctions in adversarial settings, where the schedule of descending prices is determined by an adversary. We show that a submodular optimization algorithm satisfying bi-criteria $(1/2, 1)$-approximation in welfare can be effectively adapted to a descending auction. Additionally, we establish a connection between descending auctions and online submodular optimization. Finally, we demonstrate the practical applications of our frameworks by instantiating them with state-of-the-art submodular optimization algorithms and empirically comparing their welfare performance on publicly available datasets with thousands of sellers.