pwc-archive/papers-with-abstracts huggingface dataset:
FashionDPO:Fine-tune Fashion Outfit Generation Model using Direct Preference Optimization
Authors: Mingzhe Yu, Yunshan Ma, Lei Wu, Changshuo Wang, Xue Li, Lei Meng
Date: 2025-04-17 00:00:00

Personalized outfit generation aims to construct a set of compatible and personalized fashion items as an outfit. Recently, generative AI models have received widespread attention, as they can generate fashion items for users to complete an incomplete outfit or create a complete outfit. However, they have limitations in terms of lacking diversity and relying on the supervised learning paradigm. Recognizing this gap, we propose a novel framework FashionDPO, which fine-tunes the fashion outfit generation model using direct preference optimization. This framework aims to provide a general fine-tuning approach to fashion generative models, refining a pre-trained fashion outfit generation model using automatically generated feedback, without the need to design a task-specific reward function. To make sure that the feedback is comprehensive and objective, we design a multi-expert feedback generation module which covers three evaluation perspectives, \ie quality, compatibility and personalization. Experiments on two established datasets, \ie iFashion and Polyvore-U, demonstrate the effectiveness of our framework in enhancing the model's ability to align with users' personalized preferences while adhering to fashion compatibility principles. Our code and model checkpoints are available at https://github.com/Yzcreator/FashionDPO.
Saliency-Aware Diffusion Reconstruction for Effective Invisible Watermark Removal
Authors: Inzamamul Alam, MD Tanvir Islam, Simon S. Woo
Date: 2025-04-17 00:00:00
Tasks: Image Restoration

As digital content becomes increasingly ubiquitous, the need for robust watermark removal techniques has grown due to the inadequacy of existing embedding techniques, which lack robustness. This paper introduces a novel Saliency-Aware Diffusion Reconstruction (SADRE) framework for watermark elimination on the web, combining adaptive noise injection, region-specific perturbations, and advanced diffusion-based reconstruction. SADRE disrupts embedded watermarks by injecting targeted noise into latent representations guided by saliency masks although preserving essential image features. A reverse diffusion process ensures high-fidelity image restoration, leveraging adaptive noise levels determined by watermark strength. Our framework is theoretically grounded with stability guarantees and achieves robust watermark removal across diverse scenarios. Empirical evaluations on state-of-the-art (SOTA) watermarking techniques demonstrate SADRE's superiority in balancing watermark disruption and image quality. SADRE sets a new benchmark for watermark elimination, offering a flexible and reliable solution for real-world web content. Code is available on~\href{https://github.com/inzamamulDU/SADRE}{\textbf{https://github.com/inzamamulDU/SADRE}}.
SmartFreeEdit: Mask-Free Spatial-Aware Image Editing with Complex Instruction Understanding
Authors: Qianqian Sun, Jixiang Luo, Dell Zhang, Xuelong Li
Date: 2025-04-17 00:00:00
Tasks: Image Generation, Large Language Model, Multimodal Large Language Model, Reasoning Segmentation, spatial-aware image editing, Spatial Reasoning

Recent advancements in image editing have utilized large-scale multimodal models to enable intuitive, natural instruction-driven interactions. However, conventional methods still face significant challenges, particularly in spatial reasoning, precise region segmentation, and maintaining semantic consistency, especially in complex scenes. To overcome these challenges, we introduce SmartFreeEdit, a novel end-to-end framework that integrates a multimodal large language model (MLLM) with a hypergraph-enhanced inpainting architecture, enabling precise, mask-free image editing guided exclusively by natural language instructions. The key innovations of SmartFreeEdit include:(1)the introduction of region aware tokens and a mask embedding paradigm that enhance the spatial understanding of complex scenes;(2) a reasoning segmentation pipeline designed to optimize the generation of editing masks based on natural language instructions;and (3) a hypergraph-augmented inpainting module that ensures the preservation of both structural integrity and semantic coherence during complex edits, overcoming the limitations of local-based image generation. Extensive experiments on the Reason-Edit benchmark demonstrate that SmartFreeEdit surpasses current state-of-the-art methods across multiple evaluation metrics, including segmentation accuracy, instruction adherence, and visual quality preservation, while addressing the issue of local information focus and improving global consistency in the edited image. Our project will be available at https://github.com/smileformylove/SmartFreeEdit.
Towards Realistic Low-Light Image Enhancement via ISP Driven Data Modeling
Authors: Zhihua Wang, Yu Long, Qinghua Lin, Kai Zhang, Yazhu Zhang, Yuming Fang, Li Liu, Xiaochun Cao
Date: 2025-04-16 00:00:00
Tasks: Image Enhancement, Low-Light Image Enhancement, Tone Mapping

Deep neural networks (DNNs) have recently become the leading method for low-light image enhancement (LLIE). However, despite significant progress, their outputs may still exhibit issues such as amplified noise, incorrect white balance, or unnatural enhancements when deployed in real world applications. A key challenge is the lack of diverse, large scale training data that captures the complexities of low-light conditions and imaging pipelines. In this paper, we propose a novel image signal processing (ISP) driven data synthesis pipeline that addresses these challenges by generating unlimited paired training data. Specifically, our pipeline begins with easily collected high-quality normal-light images, which are first unprocessed into the RAW format using a reverse ISP. We then synthesize low-light degradations directly in the RAW domain. The resulting data is subsequently processed through a series of ISP stages, including white balance adjustment, color space conversion, tone mapping, and gamma correction, with controlled variations introduced at each stage. This broadens the degradation space and enhances the diversity of the training data, enabling the generated data to capture a wide range of degradations and the complexities inherent in the ISP pipeline. To demonstrate the effectiveness of our synthetic pipeline, we conduct extensive experiments using a vanilla UNet model consisting solely of convolutional layers, group normalization, GeLU activation, and convolutional block attention modules (CBAMs). Extensive testing across multiple datasets reveals that the vanilla UNet model trained with our data synthesis pipeline delivers high fidelity, visually appealing enhancement results, surpassing state-of-the-art (SOTA) methods both quantitatively and qualitatively.
Interpreting the linear structure of vision-language model embedding spaces
Authors: Isabel Papadimitriou, Huangyuan Su, Thomas Fel, Sham Kakade, Stephanie Gil
Date: 2025-04-16 00:00:00
Tasks: Language Modeling, Language Modelling

Vision-language models encode images and text in a joint space, minimizing the distance between corresponding image and text pairs. How are language and images organized in this joint space, and how do the models encode meaning and modality? To investigate this, we train and release sparse autoencoders (SAEs) on the embedding spaces of four vision-language models (CLIP, SigLIP, SigLIP2, and AIMv2). SAEs approximate model embeddings as sparse linear combinations of learned directions, or "concepts". We find that, compared to other methods of linear feature learning, SAEs are better at reconstructing the real embeddings, while also able to retain the most sparsity. Retraining SAEs with different seeds or different data diet leads to two findings: the rare, specific concepts captured by the SAEs are liable to change drastically, but we also show that the key commonly-activating concepts extracted by SAEs are remarkably stable across runs. Interestingly, while most concepts are strongly unimodal in activation, we find they are not merely encoding modality per se. Many lie close to - but not entirely within - the subspace defining modality, suggesting that they encode cross-modal semantics despite their unimodal usage. To quantify this bridging behavior, we introduce the Bridge Score, a metric that identifies concept pairs which are both co-activated across aligned image-text inputs and geometrically aligned in the shared space. This reveals that even unimodal concepts can collaborate to support cross-modal integration. We release interactive demos of the SAEs for all models, allowing researchers to explore the organization of the concept spaces. Overall, our findings uncover a sparse linear structure within VLM embedding spaces that is shaped by modality, yet stitched together through latent bridges-offering new insight into how multimodal meaning is constructed.
Graph-Driven Multimodal Feature Learning Framework for Apparent Personality Assessment
Authors: Kangsheng Wang, Chengwei Ye, Huanzhen Zhang, Linuo Xu, Shuyan Liu
Date: 2025-04-15 00:00:00

Predicting personality traits automatically has become a challenging problem in computer vision. This paper introduces an innovative multimodal feature learning framework for personality analysis in short video clips. For visual processing, we construct a facial graph and design a Geo-based two-stream network incorporating an attention mechanism, leveraging both Graph Convolutional Networks (GCN) and Convolutional Neural Networks (CNN) to capture static facial expressions. Additionally, ResNet18 and VGGFace networks are employed to extract global scene and facial appearance features at the frame level. To capture dynamic temporal information, we integrate a BiGRU with a temporal attention module for extracting salient frame representations. To enhance the model's robustness, we incorporate the VGGish CNN for audio-based features and XLM-Roberta for text-based features. Finally, a multimodal channel attention mechanism is introduced to integrate different modalities, and a Multi-Layer Perceptron (MLP) regression model is used to predict personality traits. Experimental results confirm that our proposed framework surpasses existing state-of-the-art approaches in performance.
Attention GhostUNet++: Enhanced Segmentation of Adipose Tissue and Liver in CT Images
Authors: Mansoor Hayat, Supavadee Aramvith, Subrata Bhattacharjee, Nouman Ahmad
Date: 2025-04-14 00:00:00
Tasks: Computational Efficiency, Liver Segmentation, Segmentation

Accurate segmentation of abdominal adipose tissue, including subcutaneous (SAT) and visceral adipose tissue (VAT), along with liver segmentation, is essential for understanding body composition and associated health risks such as type 2 diabetes and cardiovascular disease. This study proposes Attention GhostUNet++, a novel deep learning model incorporating Channel, Spatial, and Depth Attention mechanisms into the Ghost UNet++ bottleneck for automated, precise segmentation. Evaluated on the AATTCT-IDS and LiTS datasets, the model achieved Dice coefficients of 0.9430 for VAT, 0.9639 for SAT, and 0.9652 for liver segmentation, surpassing baseline models. Despite minor limitations in boundary detail segmentation, the proposed model significantly enhances feature refinement, contextual understanding, and computational efficiency, offering a robust solution for body composition analysis. The implementation of the proposed Attention GhostUNet++ model is available at:https://github.com/MansoorHayat777/Attention-GhostUNetPlusPlus.
ALT: A Python Package for Lightweight Feature Representation in Time Series Classification
Authors: Balázs P. Halmos, Balázs Hajós, Vince Á. Molnár, Marcell T. Kurbucz, Antal Jakovác
Date: 2025-04-17 00:00:00
Tasks: Benchmarking, Time Series, Time Series Classification

We introduce ALT, an open-source Python package created for efficient and accurate time series classification (TSC). The package implements the adaptive law-based transformation (ALT) algorithm, which transforms raw time series data into a linearly separable feature space using variable-length shifted time windows. This adaptive approach enhances its predecessor, the linear law-based transformation (LLT), by effectively capturing patterns of varying temporal scales. The software is implemented for scalability, interpretability, and ease of use, achieving state-of-the-art performance with minimal computational overhead. Extensive benchmarking on real-world datasets demonstrates the utility of ALT for diverse TSC tasks in physics and related domains.
RL-PINNs: Reinforcement Learning-Driven Adaptive Sampling for Efficient Training of PINNs
Authors: Zhenao Song
Date: 2025-04-17 00:00:00
Tasks: Reinforcement Learning (RL)

Physics-Informed Neural Networks (PINNs) have emerged as a powerful framework for solving partial differential equations (PDEs). However, their performance heavily relies on the strategy used to select training points. Conventional adaptive sampling methods, such as residual-based refinement, often require multi-round sampling and repeated retraining of PINNs, leading to computational inefficiency due to redundant points and costly gradient computations-particularly in high-dimensional or high-order derivative scenarios. To address these limitations, we propose RL-PINNs, a reinforcement learning(RL)-driven adaptive sampling framework that enables efficient training with only a single round of sampling. Our approach formulates adaptive sampling as a Markov decision process, where an RL agent dynamically selects optimal training points by maximizing a long-term utility metric. Critically, we replace gradient-dependent residual metrics with a computationally efficient function variation as the reward signal, eliminating the overhead of derivative calculations. Furthermore, we employ a delayed reward mechanism to prioritize long-term training stability over short-term gains. Extensive experiments across diverse PDE benchmarks, including low-regular, nonlinear, high-dimensional, and high-order problems, demonstrate that RL-PINNs significantly outperforms existing residual-driven adaptive methods in accuracy. Notably, RL-PINNs achieve this with negligible sampling overhead, making them scalable to high-dimensional and high-order problems.
snnTrans-DHZ: A Lightweight Spiking Neural Network Architecture for Underwater Image Dehazing
Authors: Vidya Sudevan, Fakhreddine Zayer, Rizwana Kausar, Sajid Javed, Hamad Karki, Giulia De Masi, Jorge Dias
Date: 2025-04-13 00:00:00
Tasks: Image Dehazing, Image Reconstruction, SSIM

Underwater image dehazing is critical for vision-based marine operations because light scattering and absorption can severely reduce visibility. This paper introduces snnTrans-DHZ, a lightweight Spiking Neural Network (SNN) specifically designed for underwater dehazing. By leveraging the temporal dynamics of SNNs, snnTrans-DHZ efficiently processes time-dependent raw image sequences while maintaining low power consumption. Static underwater images are first converted into time-dependent sequences by repeatedly inputting the same image over user-defined timesteps. These RGB sequences are then transformed into LAB color space representations and processed concurrently. The architecture features three key modules: (i) a K estimator that extracts features from multiple color space representations; (ii) a Background Light Estimator that jointly infers the background light component from the RGB-LAB images; and (iii) a soft image reconstruction module that produces haze-free, visibility-enhanced outputs. The snnTrans-DHZ model is directly trained using a surrogate gradient-based backpropagation through time (BPTT) strategy alongside a novel combined loss function. Evaluated on the UIEB benchmark, snnTrans-DHZ achieves a PSNR of 21.68 dB and an SSIM of 0.8795, and on the EUVP dataset, it yields a PSNR of 23.46 dB and an SSIM of 0.8439. With only 0.5670 million network parameters, and requiring just 7.42 GSOPs and 0.0151 J of energy, the algorithm significantly outperforms existing state-of-the-art methods in terms of efficiency. These features make snnTrans-DHZ highly suitable for deployment in underwater robotics, marine exploration, and environmental monitoring.
Tilus: A Virtual Machine for Arbitrary Low-Precision GPGPU Computation in LLM Serving
Authors: Yaoyao Ding, Bohan Hou, Xiao Zhang, Allan Lin, Tianqi Chen, Cody Yu Hao, Yida Wang, Gennady Pekhimenko
Date: 2025-04-17 00:00:00
Tasks: GPU

Serving Large Language Models (LLMs) is critical for AI-powered applications but demands substantial computational resources, particularly in memory bandwidth and computational throughput. Low-precision computation has emerged as a key technique to improve efficiency while reducing resource consumption. Existing approaches for generating low-precision kernels are limited to weight bit widths that are powers of two and suffer from suboptimal performance due to high-level GPU programming abstractions. These abstractions restrict critical optimizations, such as fine-grained register management and optimized memory access patterns, which are essential for efficient low-precision computations. In this paper, we introduce a virtual machine (VM) designed for General-Purpose GPU (GPGPU) computing, enabling support for low-precision data types with arbitrary bit widths while maintaining GPU programmability. The proposed VM features a thread-block-level programming model, a hierarchical memory space, a novel algebraic layout system, and extensive support for diverse low-precision data types. VM programs are compiled into highly efficient GPU programs with automatic vectorization and instruction selection. Extensive experiments demonstrate that our VM efficiently supports a full spectrum of low-precision data types, and outperforms state-of-the-art low-precision kernels on their supported types. Compared to existing compilers like Triton and Ladder, as well as hand-optimized kernels such as QuantLLM and Marlin, our VM achieves performance improvements of 1.75x, 2.61x, 1.29x and 1.03x, respectively.
ViTa-Zero: Zero-shot Visuotactile Object 6D Pose Estimation
Authors: Hongyu Li, James Akl, Srinath Sridhar, Tye Brady, Taskin Padir
Date: 2025-04-17 00:00:00
Tasks: 6D Pose Estimation, hand-object pose, Object, Pose Estimation

Object 6D pose estimation is a critical challenge in robotics, particularly for manipulation tasks. While prior research combining visual and tactile (visuotactile) information has shown promise, these approaches often struggle with generalization due to the limited availability of visuotactile data. In this paper, we introduce ViTa-Zero, a zero-shot visuotactile pose estimation framework. Our key innovation lies in leveraging a visual model as its backbone and performing feasibility checking and test-time optimization based on physical constraints derived from tactile and proprioceptive observations. Specifically, we model the gripper-object interaction as a spring-mass system, where tactile sensors induce attractive forces, and proprioception generates repulsive forces. We validate our framework through experiments on a real-world robot setup, demonstrating its effectiveness across representative visual backbones and manipulation scenarios, including grasping, object picking, and bimanual handover. Compared to the visual models, our approach overcomes some drastic failure modes while tracking the in-hand object pose. In our experiments, our approach shows an average increase of 55% in AUC of ADD-S and 60% in ADD, along with an 80% lower position error compared to FoundationPose.
RUKA: Rethinking the Design of Humanoid Hands with Learning
Authors: Anya Zorin, Irmak Guzey, Billy Yan, Aadhithya Iyer, Lisa Kondrich, Nikhil X. Bhattasali, Lerrel Pinto
Date: 2025-04-17 00:00:00

Dexterous manipulation is a fundamental capability for robotic systems, yet progress has been limited by hardware trade-offs between precision, compactness, strength, and affordability. Existing control methods impose compromises on hand designs and applications. However, learning-based approaches present opportunities to rethink these trade-offs, particularly to address challenges with tendon-driven actuation and low-cost materials. This work presents RUKA, a tendon-driven humanoid hand that is compact, affordable, and capable. Made from 3D-printed parts and off-the-shelf components, RUKA has 5 fingers with 15 underactuated degrees of freedom enabling diverse human-like grasps. Its tendon-driven actuation allows powerful grasping in a compact, human-sized form factor. To address control challenges, we learn joint-to-actuator and fingertip-to-actuator models from motion-capture data collected by the MANUS glove, leveraging the hand's morphological accuracy. Extensive evaluations demonstrate RUKA's superior reachability, durability, and strength compared to other robotic hands. Teleoperation tasks further showcase RUKA's dexterous movements. The open-source design and assembly instructions of RUKA, code, and data are available at https://ruka-hand.github.io/.
Uncertainty-Aware Trajectory Prediction via Rule-Regularized Heteroscedastic Deep Classification
Authors: Kumar Manas, Christian Schlauch, Adrian Paschke, Christian Wirth, Nadja Klein
Date: 2025-04-17 00:00:00
Tasks: Diversity, Gaussian Processes, Language Modeling, Language Modelling, Large Language Model, Out-of-Distribution Generalization, Retrieval-augmented Generation, Trajectory Prediction

Deep learning-based trajectory prediction models have demonstrated promising capabilities in capturing complex interactions. However, their out-of-distribution generalization remains a significant challenge, particularly due to unbalanced data and a lack of enough data and diversity to ensure robustness and calibration. To address this, we propose SHIFT (Spectral Heteroscedastic Informed Forecasting for Trajectories), a novel framework that uniquely combines well-calibrated uncertainty modeling with informative priors derived through automated rule extraction. SHIFT reformulates trajectory prediction as a classification task and employs heteroscedastic spectral-normalized Gaussian processes to effectively disentangle epistemic and aleatoric uncertainties. We learn informative priors from training labels, which are automatically generated from natural language driving rules, such as stop rules and drivability constraints, using a retrieval-augmented generation framework powered by a large language model. Extensive evaluations over the nuScenes dataset, including challenging low-data and cross-location scenarios, demonstrate that SHIFT outperforms state-of-the-art methods, achieving substantial gains in uncertainty calibration and displacement metrics. In particular, our model excels in complex scenarios, such as intersections, where uncertainty is inherently higher. Project page: https://kumarmanas.github.io/SHIFT/.