pwc-archive/papers-with-abstracts huggingface dataset:
Towards Explainable Fusion and Balanced Learning in Multimodal Sentiment Analysis
Authors: Miaosen Luo, Yuncheng Jiang, Sijie Mai
Date: 2025-04-16 00:00:00
Tasks: Denoising, Dimensionality Reduction, Kolmogorov-Arnold Networks, Multimodal Sentiment Analysis, Sentiment Analysis

Multimodal Sentiment Analysis (MSA) faces two critical challenges: the lack of interpretability in the decision logic of multimodal fusion and modality imbalance caused by disparities in inter-modal information density. To address these issues, we propose KAN-MCP, a novel framework that integrates the interpretability of Kolmogorov-Arnold Networks (KAN) with the robustness of the Multimodal Clean Pareto (MCPareto) framework. First, KAN leverages its univariate function decomposition to achieve transparent analysis of cross-modal interactions. This structural design allows direct inspection of feature transformations without relying on external interpretation tools, thereby ensuring both high expressiveness and interpretability. Second, the proposed MCPareto enhances robustness by addressing modality imbalance and noise interference. Specifically, we introduce the Dimensionality Reduction and Denoising Modal Information Bottleneck (DRD-MIB) method, which jointly denoises and reduces feature dimensionality. This approach provides KAN with discriminative low-dimensional inputs to reduce the modeling complexity of KAN while preserving critical sentiment-related information. Furthermore, MCPareto dynamically balances gradient contributions across modalities using the purified features output by DRD-MIB, ensuring lossless transmission of auxiliary signals and effectively alleviating modality imbalance. This synergy of interpretability and robustness not only achieves superior performance on benchmark datasets such as CMU-MOSI, CMU-MOSEI, and CH-SIMS v2 but also offers an intuitive visualization interface through KAN's interpretable architecture.
Modular-Cam: Modular Dynamic Camera-view Video Generation with LLM
Authors: Zirui Pan, Xin Wang, YiPeng Zhang, Hong Chen, Kwan Man Cheng, Yaofei Wu, Wenwu Zhu
Date: 2025-04-16 00:00:00
Tasks: Large Language Model, Text-to-Video Generation, Video Generation

Text-to-Video generation, which utilizes the provided text prompt to generate high-quality videos, has drawn increasing attention and achieved great success due to the development of diffusion models recently. Existing methods mainly rely on a pre-trained text encoder to capture the semantic information and perform cross attention with the encoded text prompt to guide the generation of video. However, when it comes to complex prompts that contain dynamic scenes and multiple camera-view transformations, these methods can not decompose the overall information into separate scenes, as well as fail to smoothly change scenes based on the corresponding camera-views. To solve these problems, we propose a novel method, i.e., Modular-Cam. Specifically, to better understand a given complex prompt, we utilize a large language model to analyze user instructions and decouple them into multiple scenes together with transition actions. To generate a video containing dynamic scenes that match the given camera-views, we incorporate the widely-used temporal transformer into the diffusion model to ensure continuity within a single scene and propose CamOperator, a modular network based module that well controls the camera movements. Moreover, we propose AdaControlNet, which utilizes ControlNet to ensure consistency across scenes and adaptively adjusts the color tone of the generated video. Extensive qualitative and quantitative experiments prove our proposed Modular-Cam's strong capability of generating multi-scene videos together with its ability to achieve fine-grained control of camera movements. Generated results are available at https://modular-cam.github.io.
RadMamba: Efficient Human Activity Recognition through Radar-based Micro-Doppler-Oriented Mamba State-Space Model
Authors: Yizhuo Wu, Francesco Fioranelli, Chang Gao
Date: 2025-04-16 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Mamba

Radar-based HAR has emerged as a promising alternative to conventional monitoring approaches, such as wearable devices and camera-based systems, due to its unique privacy preservation and robustness advantages. However, existing solutions based on convolutional and recurrent neural networks, although effective, are computationally demanding during deployment. This limits their applicability in scenarios with constrained resources or those requiring multiple sensors. Advanced architectures, such as ViT and SSM architectures, offer improved modeling capabilities and have made efforts toward lightweight designs. However, their computational complexity remains relatively high. To leverage the strengths of transformer architectures while simultaneously enhancing accuracy and reducing computational complexity, this paper introduces RadMamba, a parameter-efficient, radar micro-Doppler-oriented Mamba SSM specifically tailored for radar-based HAR. Across three diverse datasets, RadMamba matches the top-performing previous model's 99.8% classification accuracy on Dataset DIAT with only 1/400 of its parameters and equals the leading models' 92.0% accuracy on Dataset CI4R with merely 1/10 of their parameters. In scenarios with continuous sequences of actions evaluated on Dataset UoG2020, RadMamba surpasses other models with significantly higher parameter counts by at least 3%, achieving this with only 6.7k parameters. Our code is available at: https://github.com/lab-emi/AIRHAR.
TacoDepth: Towards Efficient Radar-Camera Depth Estimation with One-stage Fusion
Authors: Yiran Wang, Jiaqi Li, Chaoyi Hong, Ruibo Li, Liusheng Sun, Xiao Song, Zhe Wang, Zhiguo Cao, Guosheng Lin
Date: 2025-04-16 00:00:00
Proceeding: CVPR 2025 1
Tasks: Autonomous Vehicles, Depth Estimation

Radar-Camera depth estimation aims to predict dense and accurate metric depth by fusing input images and Radar data. Model efficiency is crucial for this task in pursuit of real-time processing on autonomous vehicles and robotic platforms. However, due to the sparsity of Radar returns, the prevailing methods adopt multi-stage frameworks with intermediate quasi-dense depth, which are time-consuming and not robust. To address these challenges, we propose TacoDepth, an efficient and accurate Radar-Camera depth estimation model with one-stage fusion. Specifically, the graph-based Radar structure extractor and the pyramid-based Radar fusion module are designed to capture and integrate the graph structures of Radar point clouds, delivering superior model efficiency and robustness without relying on the intermediate depth results. Moreover, TacoDepth can be flexible for different inference modes, providing a better balance of speed and accuracy. Extensive experiments are conducted to demonstrate the efficacy of our method. Compared with the previous state-of-the-art approach, TacoDepth improves depth accuracy and processing speed by 12.8% and 91.8%. Our work provides a new perspective on efficient Radar-Camera depth estimation.
Real-time Object and Event Detection Service through Computer Vision and Edge Computing
Authors: Marcos Mendes, Gonçalo Perna, Pedro Rito, Duarte Raposo, Susana Sargento
Date: 2025-04-15 00:00:00
Tasks: Edge-computing, Event Detection

The World Health Organization suggests that road traffic crashes cost approximately 518 billion dollars globally each year, which accounts for 3% of the gross domestic product for most countries. Most fatal road accidents in urban areas involve Vulnerable Road Users (VRUs). Smart cities environments present innovative approaches to combat accidents involving cutting-edge technologies, that include advanced sensors, extensive datasets, Machine Learning (ML) models, communication systems, and edge computing. This paper proposes a strategy and an implementation of a system for road monitoring and safety for smart cities, based on Computer Vision (CV) and edge computing. Promising results were obtained by implementing vision algorithms and tracking using surveillance cameras, that are part of a Smart City testbed, the Aveiro Tech City Living Lab (ATCLL). The algorithm accurately detects and tracks cars, pedestrians, and bicycles, while predicting the road state, the distance between moving objects, and inferring on collision events to prevent collisions, in near real-time.
d1: Scaling Reasoning in Diffusion Large Language Models via Reinforcement Learning
Authors: Siyan Zhao, Devaansh Gupta, Qinqing Zheng, Aditya Grover
Date: 2025-04-16 00:00:00
Tasks: Language Modeling, Language Modelling, Logical Reasoning, Reinforcement Learning (RL)

Recent large language models (LLMs) have demonstrated strong reasoning capabilities that benefits from online reinforcement learning (RL). These capabilities have primarily been demonstrated within the left-to-right autoregressive (AR) generation paradigm. In contrast, non-autoregressive paradigms based on diffusion generate text in a coarse-to-fine manner. Although recent diffusion-based large language models (dLLMs) have achieved competitive language modeling performance compared to their AR counterparts, it remains unclear if dLLMs can also leverage recent advances in LLM reasoning. To this end, we propose d1, a framework to adapt pre-trained masked dLLMs into reasoning models via a combination of supervised finetuning (SFT) and RL. Specifically, we develop and extend techniques to improve reasoning in pretrained dLLMs: (a) we utilize a masked SFT technique to distill knowledge and instill self-improvement behavior directly from existing datasets, and (b) we introduce a novel critic-free, policy-gradient based RL algorithm called diffu-GRPO. Through empirical studies, we investigate the performance of different post-training recipes on multiple mathematical and logical reasoning benchmarks. We find that d1 yields the best performance and significantly improves performance of a state-of-the-art dLLM.
Reasoning-Based AI for Startup Evaluation (R.A.I.S.E.): A Memory-Augmented, Multi-Step Decision Framework
Authors: Jack Preuveneers, Joseph Ternasky, Fuat Alican, Yigit Ihlamur
Date: 2025-04-16 00:00:00
Tasks: Decision Making

We present a novel framework that bridges the gap between the interpretability of decision trees and the advanced reasoning capabilities of large language models (LLMs) to predict startup success. Our approach leverages chain-of-thought prompting to generate detailed reasoning logs, which are subsequently distilled into structured, human-understandable logical rules. The pipeline integrates multiple enhancements - efficient data ingestion, a two-step refinement process, ensemble candidate sampling, simulated reinforcement learning scoring, and persistent memory - to ensure both stable decision-making and transparent output. Experimental evaluations on curated startup datasets demonstrate that our combined pipeline improves precision by 54% from 0.225 to 0.346 and accuracy by 50% from 0.46 to 0.70 compared to a standalone OpenAI o3 model. Notably, our model achieves over 2x the precision of a random classifier (16%). By combining state-of-the-art AI reasoning with explicit rule-based explanations, our method not only augments traditional decision-making processes but also facilitates expert intervention and continuous policy refinement. This work lays the foundation for the implementation of interpretable LLM-powered decision frameworks in high-stakes investment environments and other domains that require transparent and data-driven insights.
pix2pockets: Shot Suggestions in 8-Ball Pool from a Single Image in the Wild
Authors: Jonas Myhre Schiøtt, Viktor Sebastian Petersen, Dimitrios P. Papadopoulos
Date: 2025-04-16 00:00:00
Tasks: Benchmarking, object-detection, Object Detection, Reinforcement Learning (RL), Semantic Segmentation

Computer vision models have seen increased usage in sports, and reinforcement learning (RL) is famous for beating humans in strategic games such as Chess and Go. In this paper, we are interested in building upon these advances and examining the game of classic 8-ball pool. We introduce pix2pockets, a foundation for an RL-assisted pool coach. Given a single image of a pool table, we first aim to detect the table and the balls and then propose the optimal shot suggestion. For the first task, we build a dataset with 195 diverse images where we manually annotate all balls and table dots, leading to 5748 object segmentation masks. For the second task, we build a standardized RL environment that allows easy development and benchmarking of any RL algorithm. Our object detection model yields an AP50 of 91.2 while our ball location pipeline obtains an error of only 0.4 cm. Furthermore, we compare standard RL algorithms to set a baseline for the shot suggestion task and we show that all of them fail to pocket all balls without making a foul move. We also present a simple baseline that achieves a per-shot success rate of 94.7% and clears a full game in a single turn 30% of the time.
Evolutionary Reinforcement Learning for Interpretable Decision-Making in Supply Chain Management
Authors: Stefano Genetti, Alberto Longobardi, Giovanni Iacca
Date: 2025-04-16 00:00:00
Tasks: Decision Making, Management, Reinforcement Learning (RL)

In the context of Industry 4.0, Supply Chain Management (SCM) faces challenges in adopting advanced optimization techniques due to the "black-box" nature of most AI-based solutions, which causes reluctance among company stakeholders. To overcome this issue, in this work, we employ an Interpretable Artificial Intelligence (IAI) approach that combines evolutionary computation with Reinforcement Learning (RL) to generate interpretable decision-making policies in the form of decision trees. This IAI solution is embedded within a simulation-based optimization framework specifically designed to handle the inherent uncertainties and stochastic behaviors of modern supply chains. To our knowledge, this marks the first attempt to combine IAI with simulation-based optimization for decision-making in SCM. The methodology is tested on two supply chain optimization problems, one fictional and one from the real world, and its performance is compared against widely used optimization and RL algorithms. The results reveal that the interpretable approach delivers competitive, and sometimes better, performance, challenging the prevailing notion that there must be a trade-off between interpretability and optimization efficiency. Additionally, the developed framework demonstrates strong potential for industrial applications, offering seamless integration with various Python-based algorithms.
A Computationally Efficient Algorithm for Infinite-Horizon Average-Reward Linear MDPs
Authors: Kihyuk Hong, Ambuj Tewari
Date: 2025-04-16 00:00:00

We study reinforcement learning in infinite-horizon average-reward settings with linear MDPs. Previous work addresses this problem by approximating the average-reward setting by discounted setting and employing a value iteration-based algorithm that uses clipping to constrain the span of the value function for improved statistical efficiency. However, the clipping procedure requires computing the minimum of the value function over the entire state space, which is prohibitive since the state space in linear MDP setting can be large or even infinite. In this paper, we introduce a value iteration method with efficient clipping operation that only requires computing the minimum of value functions over the set of states visited by the algorithm. Our algorithm enjoys the same regret bound as the previous work while being computationally efficient, with computational complexity that is independent of the size of the state space.
Deep Learning Approaches for Medical Imaging Under Varying Degrees of Label Availability: A Comprehensive Survey
Authors: Siteng Ma, Honghui Du, Yu An, Jing Wang, Qinqin Wang, Haochang Wu, Aonghus Lawlor, Ruihai Dong
Date: 2025-04-15 00:00:00
Tasks: image-classification, Image Classification, Missing Labels

Deep learning has achieved significant breakthroughs in medical imaging, but these advancements are often dependent on large, well-annotated datasets. However, obtaining such datasets poses a significant challenge, as it requires time-consuming and labor-intensive annotations from medical experts. Consequently, there is growing interest in learning paradigms such as incomplete, inexact, and absent supervision, which are designed to operate under limited, inexact, or missing labels. This survey categorizes and reviews the evolving research in these areas, analyzing around 600 notable contributions since 2018. It covers tasks such as image classification, segmentation, and detection across various medical application areas, including but not limited to brain, chest, and cardiac imaging. We attempt to establish the relationships among existing research studies in related areas. We provide formal definitions of different learning paradigms and offer a comprehensive summary and interpretation of various learning mechanisms and strategies, aiding readers in better understanding the current research landscape and ideas. We also discuss potential future research challenges.
A Review of YOLOv12: Attention-Based Enhancements vs. Previous Versions
Authors: Rahima Khanam, Muhammad Hussain
Date: 2025-04-16 00:00:00
Tasks: Computational Efficiency, Object, object-detection, Object Detection, Real-Time Object Detection

The YOLO (You Only Look Once) series has been a leading framework in real-time object detection, consistently improving the balance between speed and accuracy. However, integrating attention mechanisms into YOLO has been challenging due to their high computational overhead. YOLOv12 introduces a novel approach that successfully incorporates attention-based enhancements while preserving real-time performance. This paper provides a comprehensive review of YOLOv12's architectural innovations, including Area Attention for computationally efficient self-attention, Residual Efficient Layer Aggregation Networks for improved feature aggregation, and FlashAttention for optimized memory access. Additionally, we benchmark YOLOv12 against prior YOLO versions and competing object detectors, analyzing its improvements in accuracy, inference speed, and computational efficiency. Through this analysis, we demonstrate how YOLOv12 advances real-time object detection by refining the latency-accuracy trade-off and optimizing computational resources.
AttriVision: Advancing Generalization in Pedestrian Attribute Recognition using CLIP
Authors: Mehran ADIBI SEDEH, Assia Benbihi, Romain MARTIN, Marianne Clausel, Cédric Pradalier
Date: 2025-02-18 00:00:00
Proceeding: WACV 2025 2
Tasks: Attribute, Pedestrian Attribute Recognition

Pedestrian Attribute Recognition (PAR) is a critical task
in computer vision that identifies semantic attributes such
as gender, age, clothing, and accessories from images of
individuals. This task is essential in applications such as
surveillance, smart city infrastructure, and security systems. Despite significant advances in deep learning, PAR
remains challenging due to strong imbalances in the attribute classes and the need for robust generalization across
different datasets and environments. In this work, we address these two limitations with AttriVision, a novel approach that adopts the generic CLIP features to make
PAR better generalize and introduces a new Focal CrossEntropy (FCE) loss function to handle the inherent class
imbalance in PAR datasets. FCE improves the model’s robustness by giving more weight to difficult-to-classify samples. Our method also demonstrates remarkable transferability to other attribute recognition tasks, such as vehicle attributes, without any architectural modifications. This
transferability makes AttriVision a powerful and versatile
tool for attribute recognition. We validate our approach
on the Unified Pedestrian Attribute Recognition (UPAR)
dataset that integrates data from several sources including PA100K, PETA, RAPv2, and Market1501. AttriVision
achieves new state-of-the-art results on UPAR, with a mean
accuracy of 89.4% and an F1 score of 91.9%. These
results demonstrate the model’s effectiveness in handling
real-world variability, including differences in image sensors, viewing conditions, and person densities, making it
highly suitable for a wide range of real-world applications.
2D Object Detection: A Survey
Authors: Emanuele Malagoli, Luca Di Persio
Date: 2025-03-07 00:00:00
Proceeding: Mathematics 2025 3
Tasks: 2D Object Detection, Object, object-detection, Object Detection, Real-Time Object Detection, Survey

Object detection is a fundamental task in computer vision, aiming to identify and localize objects of interest within an image. Over the past two decades, the domain has changed profoundly, evolving into an active and fast-moving field while simultaneously becoming the foundation for a wide range of modern applications. This survey provides a comprehensive review of the evolution of 2D generic object detection, tracing its development from traditional methods relying on handcrafted features to modern approaches driven by deep learning. The review systematically categorizes contemporary object detection methods into three key paradigms: one-stage, two-stage, and transformer-based, highlighting their development milestones and core contributions. The paper provides an in-depth analysis of each paradigm, detailing landmark methods and their impact on the progression of the field. Additionally, the survey examines some fundamental components of 2D object detection such as loss functions, datasets, evaluation metrics, and future trends.
FUSION: Fully Integration of Vision-Language Representations for Deep Cross-Modal Understanding
Authors: Zheng Liu, Mengjie Liu, Jingzhou Chen, Jingwei Xu, Bin Cui, Conghui He, Wentao Zhang
Date: 2025-04-14 00:00:00

We introduce FUSION, a family of multimodal large language models (MLLMs) with a fully vision-language alignment and integration paradigm. Unlike existing methods that primarily rely on late-stage modality interaction during LLM decoding, our approach achieves deep, dynamic integration throughout the entire processing pipeline. To this end, we propose Text-Guided Unified Vision Encoding, incorporating textual information in vision encoding to achieve pixel-level integration. We further design Context-Aware Recursive Alignment Decoding that recursively aggregates visual features conditioned on textual context during decoding, enabling fine-grained, question-level semantic integration. To guide feature mapping and mitigate modality discrepancies, we develop Dual-Supervised Semantic Mapping Loss. Additionally, we construct a Synthesized Language-Driven Question-Answer (QA) dataset through a new data synthesis method, prioritizing high-quality QA pairs to optimize text-guided feature integration. Building on these foundations, we train FUSION at two scales-3B, 8B-and demonstrate that our full-modality integration approach significantly outperforms existing methods with only 630 vision tokens. Notably, FUSION 3B surpasses Cambrian-1 8B and Florence-VL 8B on most benchmarks. FUSION 3B continues to outperform Cambrian-1 8B even when limited to 300 vision tokens. Our ablation studies show that FUSION outperforms LLaVA-NeXT on over half of the benchmarks under same configuration without dynamic resolution, highlighting the effectiveness of our approach. We release our code, model weights, and dataset. https://github.com/starriver030515/FUSION