pwc-archive/papers-with-abstracts huggingface dataset:
HiDe-LLaVA: Hierarchical Decoupling for Continual Instruction Tuning of Multimodal Large Language Model
Authors: Haiyang Guo, Fanhu Zeng, Ziwei Xiang, Fei Zhu, Da-Han Wang, Xu-Yao Zhang, Cheng-Lin Liu
Date: 2025-03-17 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Multimodal Large Language Model

Instruction tuning is widely used to improve a pre-trained Multimodal Large Language Model (MLLM) by training it on curated task-specific datasets, enabling better comprehension of human instructions. However, it is infeasible to collect all possible instruction datasets simultaneously in real-world scenarios. Thus, enabling MLLM with continual instruction tuning is essential for maintaining their adaptability. However, existing methods often trade off memory efficiency for performance gains, significantly compromising overall efficiency. In this paper, we propose a task-specific expansion and task-general fusion framework based on the variations in Centered Kernel Alignment (CKA) similarity across different model layers when trained on diverse datasets. Furthermore, we analyze the information leakage present in the existing benchmark and propose a new and more challenging benchmark to rationally evaluate the performance of different methods. Comprehensive experiments showcase a significant performance improvement of our method compared to existing state-of-the-art methods. Our code will be public available.
StickMotion: Generating 3D Human Motions by Drawing a Stickman
Authors: Tao Wang, Zhihua Wu, Qiaozhi He, Jiaming Chu, Ling Qian, Yu Cheng, Junliang Xing, Jian Zhao, Lei Jin
Date: 2025-03-05 00:00:00
Proceeding: CVPR 2025 1
Tasks: Motion Generation

Text-to-motion generation, which translates textual descriptions into human motions, has been challenging in accurately capturing detailed user-imagined motions from simple text inputs. This paper introduces StickMotion, an efficient diffusion-based network designed for multi-condition scenarios, which generates desired motions based on traditional text and our proposed stickman conditions for global and local control of these motions, respectively. We address the challenges introduced by the user-friendly stickman from three perspectives: 1) Data generation. We develop an algorithm to generate hand-drawn stickmen automatically across different dataset formats. 2) Multi-condition fusion. We propose a multi-condition module that integrates into the diffusion process and obtains outputs of all possible condition combinations, reducing computational complexity and enhancing StickMotion's performance compared to conventional approaches with the self-attention module. 3) Dynamic supervision. We empower StickMotion to make minor adjustments to the stickman's position within the output sequences, generating more natural movements through our proposed dynamic supervision strategy. Through quantitative experiments and user studies, sketching stickmen saves users about 51.5% of their time generating motions consistent with their imagination. Our codes, demos, and relevant data will be released to facilitate further research and validation within the scientific community.
VRsketch2Gaussian: 3D VR Sketch Guided 3D Object Generation with Gaussian Splatting
Authors: Songen Gu, Haoxuan Song, Binjie Liu, Qian Yu, Sanyi Zhang, Haiyong Jiang, Jin Huang, Feng Tian
Date: 2025-03-16 00:00:00
Tasks: 3D Generation, 3DGS

We propose VRSketch2Gaussian, a first VR sketch-guided, multi-modal, native 3D object generation framework that incorporates a 3D Gaussian Splatting representation. As part of our work, we introduce VRSS, the first large-scale paired dataset containing VR sketches, text, images, and 3DGS, bridging the gap in multi-modal VR sketch-based generation. Our approach features the following key innovations: 1) Sketch-CLIP feature alignment. We propose a two-stage alignment strategy that bridges the domain gap between sparse VR sketch embeddings and rich CLIP embeddings, facilitating both VR sketch-based retrieval and generation tasks. 2) Fine-Grained multi-modal conditioning. We disentangle the 3D generation process by using explicit VR sketches for geometric conditioning and text descriptions for appearance control. To facilitate this, we propose a generalizable VR sketch encoder that effectively aligns different modalities. 3) Efficient and high-fidelity 3D native generation. Our method leverages a 3D-native generation approach that enables fast and texture-rich 3D object synthesis. Experiments conducted on our VRSS dataset demonstrate that our method achieves high-quality, multi-modal VR sketch-based 3D generation. We believe our VRSS dataset and VRsketch2Gaussian method will be beneficial for the 3D generation community.
Design and Implementation of an FPGA-Based Hardware Accelerator for Transformer
Authors: Richie Li, Sicheng Chen
Date: 2025-03-20 00:00:00
Tasks: CPU, High-Level Synthesis

Transformer-based large language models (LLMs) rely heavily on intensive matrix multiplications for attention and feed-forward layers, with the Q, K, and V linear projections in the Multi-Head Self-Attention (MHA) module constituting a decisive performance bottleneck. In this work, we introduce a highly optimized tiled matrix multiplication accelerator on a resource-constrained Xilinx KV260 FPGA that not only addresses this challenge but sets a new standard for efficiency and performance. Our design exploits persistent on-chip storage, a robust two-level tiling strategy for maximal data reuse, and a systolic-like unrolled compute engine that together deliver unparalleled speed and energy efficiency. Integrated with DistilBERT for Q, K, and V projections, our accelerator achieves an unequivocal 7x speedup over ARM CPU implementations (PyTorch) and an extraordinary 200x improvement over naive NumPy, reaching a throughput of up to 3.1~GFLOPs for matrix multiplications on (64,768) x (768,3072) matrices while operating at a conservative 100 MHz. These results decisively demonstrate the transformative potential of FPGA-based acceleration for critical Transformer operations, paving the way for scalable and energy-efficient deep learning inference on edge devices.
FFaceNeRF: Few-shot Face Editing in Neural Radiance Fields
Authors: Kwan Yun, Chaelin Kim, Hangyeul Shin, Junyong Noh
Date: 2025-03-21 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3D-Aware Image Synthesis, Face Generation, NeRF

Recent 3D face editing methods using masks have produced high-quality edited images by leveraging Neural Radiance Fields (NeRF). Despite their impressive performance, existing methods often provide limited user control due to the use of pre-trained segmentation masks. To utilize masks with a desired layout, an extensive training dataset is required, which is challenging to gather. We present FFaceNeRF, a NeRF-based face editing technique that can overcome the challenge of limited user control due to the use of fixed mask layouts. Our method employs a geometry adapter with feature injection, allowing for effective manipulation of geometry attributes. Additionally, we adopt latent mixing for tri-plane augmentation, which enables training with a few samples. This facilitates rapid model adaptation to desired mask layouts, crucial for applications in fields like personalized medical imaging or creative face editing. Our comparative evaluations demonstrate that FFaceNeRF surpasses existing mask based face editing methods in terms of flexibility, control, and generated image quality, paving the way for future advancements in customized and high-fidelity 3D face editing. The code is available on the {\href{https://kwanyun.github.io/FFaceNeRF_page/}{project-page}}.
Accodemy: AI Powered Code Learning Platform to Assist Novice Programmers in Overcoming the Fear of Coding
Authors: M. A. F. Aamina, V. Kavishcan, W. M. P. B. B. Jayaratne, K. K. D. S. N. Kannangara, A. A. Aamil, Achini Adikari
Date: 2025-03-09 00:00:00
Tasks: Chatbot

Computer programming represents a rapidly evolving and sought-after career path in the 21st century. Nevertheless, novice learners may find the process intimidating for several reasons, such as limited and highly competitive career opportunities, peer and parental pressure for academic success, and course difficulties. These factors frequently contribute to anxiety and eventual dropout as a result of fear. Furthermore, research has demonstrated that beginners are significantly deterred by the fear of failure, which results in programming anxiety and and a sense of being overwhelmed by intricate topics, ultimately leading to dropping out. This project undertakes an exploration beyond the scope of conventional code learning platforms by identifying and utilising effective and personalised strategies of learning. The proposed solution incorporates features such as AI-generated challenging questions, mindfulness quotes, and tips to motivate users, along with an AI chatbot that functions as a motivational aid. In addition, the suggested solution integrates personalized roadmaps and gamification elements to maintain user involvement. The project aims to systematically monitor the progress of novice programmers and enhance their knowledge of coding with a personalised, revised curriculum to help mitigate the fear of coding and boost confidence.
A Planning Framework for Adaptive Labeling
Authors: Daksh Mittal, Yuanzhe Ma, Shalmali Joshi, Hongseok Namkoong
Date: 2025-02-10 00:00:00
Tasks: Uncertainty Quantification

Ground truth labels/outcomes are critical for advancing scientific and engineering applications, e.g., evaluating the treatment effect of an intervention or performance of a predictive model. Since randomly sampling inputs for labeling can be prohibitively expensive, we introduce an adaptive labeling framework where measurement effort can be reallocated in batches. We formulate this problem as a Markov decision process where posterior beliefs evolve over time as batches of labels are collected (state transition), and batches (actions) are chosen to minimize uncertainty at the end of data collection. We design a computational framework that is agnostic to different uncertainty quantification approaches including those based on deep learning, and allows a diverse array of policy gradient approaches by relying on continuous policy parameterizations. On real and synthetic datasets, we demonstrate even a one-step lookahead policy can substantially outperform common adaptive labeling heuristics, highlighting the virtue of planning. On the methodological side, we note that standard REINFORCE-style policy gradient estimators can suffer high variance since they rely only on zeroth order information. We propose a direct backpropagation-based approach, Smoothed-Autodiff, based on a carefully smoothed version of the original non-differentiable MDP. Our method enjoys low variance at the price of introducing bias, and we theoretically and empirically show that this trade-off can be favorable.
FastCuRL: Curriculum Reinforcement Learning with Progressive Context Extension for Efficient Training R1-like Reasoning Models
Authors: Mingyang Song, Mao Zheng, Zheng Li, Wenjie Yang, Xuan Luo, Yue Pan, Feng Zhang
Date: 2025-03-21 00:00:00
Tasks: Language Modeling, Language Modelling, Math, reinforcement-learning, Reinforcement Learning

Improving the training efficiency remains one of the most significant challenges in large-scale reinforcement learning. In this paper, we investigate how the model's context length and the complexity of the training dataset influence the training process of R1-like models. Our experiments reveal three key insights: (1) adopting longer context lengths may not necessarily result in better performance; (2) selecting an appropriate context length helps mitigate entropy collapse; and (3) appropriately controlling the model's context length and curating training data based on input prompt length can effectively improve RL training efficiency, achieving better performance with shorter thinking length. Inspired by these insights, we propose FastCuRL, a curriculum reinforcement learning framework with the progressive context extension strategy, and successfully accelerate the training process of RL models. Experimental results demonstrate that FastCuRL-1.5B-Preview surpasses DeepScaleR-1.5B-Preview across all five benchmarks while only utilizing 50\% of training steps. Furthermore, all training stages for FastCuRL-1.5B-Preview are completed using a single node with 8 GPUs.
CVE-Bench: A Benchmark for AI Agents' Ability to Exploit Real-World Web Application Vulnerabilities
Authors: Yuxuan Zhu, Antony Kellermann, Dylan Bowman, Philip Li, Akul Gupta, Adarsh Danda, Richard Fang, Conner Jensen, Eric Ihli, Jason Benn, Jet Geronimo, Avi Dhir, Sudhit Rao, Kaicheng Yu, Twm Stone, Daniel Kang
Date: 2025-03-21 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Large language model (LLM) agents are increasingly capable of autonomously conducting cyberattacks, posing significant threats to existing applications. This growing risk highlights the urgent need for a real-world benchmark to evaluate the ability of LLM agents to exploit web application vulnerabilities. However, existing benchmarks fall short as they are limited to abstracted Capture the Flag competitions or lack comprehensive coverage. Building a benchmark for real-world vulnerabilities involves both specialized expertise to reproduce exploits and a systematic approach to evaluating unpredictable threats. To address this challenge, we introduce CVE-Bench, a real-world cybersecurity benchmark based on critical-severity Common Vulnerabilities and Exposures. In CVE-Bench, we design a sandbox framework that enables LLM agents to exploit vulnerable web applications in scenarios that mimic real-world conditions, while also providing effective evaluation of their exploits. Our evaluation shows that the state-of-the-art agent framework can resolve up to 13% of vulnerabilities.
Dereflection Any Image with Diffusion Priors and Diversified Data
Authors: Jichen Hu, Chen Yang, Zanwei Zhou, Jiemin Fang, Xiaokang Yang, Qi Tian, Wei Shen
Date: 2025-03-21 00:00:00
Tasks: Diversity, Reflection Removal

Reflection removal of a single image remains a highly challenging task due to the complex entanglement between target scenes and unwanted reflections. Despite significant progress, existing methods are hindered by the scarcity of high-quality, diverse data and insufficient restoration priors, resulting in limited generalization across various real-world scenarios. In this paper, we propose Dereflection Any Image, a comprehensive solution with an efficient data preparation pipeline and a generalizable model for robust reflection removal. First, we introduce a dataset named Diverse Reflection Removal (DRR) created by randomly rotating reflective mediums in target scenes, enabling variation of reflection angles and intensities, and setting a new benchmark in scale, quality, and diversity. Second, we propose a diffusion-based framework with one-step diffusion for deterministic outputs and fast inference. To ensure stable learning, we design a three-stage progressive training strategy, including reflection-invariant finetuning to encourage consistent outputs across varying reflection patterns that characterize our dataset. Extensive experiments show that our method achieves SOTA performance on both common benchmarks and challenging in-the-wild images, showing superior generalization across diverse real-world scenes.
Structure Is Not Enough: Leveraging Behavior for Neural Network Weight Reconstruction
Authors: Léo Meynent, Ivan Melev, Konstantin Schürholt, Göran Kauermann, Damian Borth
Date: 2025-03-21 00:00:00
Tasks: Representation Learning

The weights of neural networks (NNs) have recently gained prominence as a new data modality in machine learning, with applications ranging from accuracy and hyperparameter prediction to representation learning or weight generation. One approach to leverage NN weights involves training autoencoders (AEs), using contrastive and reconstruction losses. This allows such models to be applied to a wide variety of downstream tasks, and they demonstrate strong predictive performance and low reconstruction error. However, despite the low reconstruction error, these AEs reconstruct NN models with deteriorated performance compared to the original ones, limiting their usability with regard to model weight generation. In this paper, we identify a limitation of weight-space AEs, specifically highlighting that a structural loss, that uses the Euclidean distance between original and reconstructed weights, fails to capture some features critical for reconstructing high-performing models. We analyze the addition of a behavioral loss for training AEs in weight space, where we compare the output of the reconstructed model with that of the original one, given some common input. We show a strong synergy between structural and behavioral signals, leading to increased performance in all downstream tasks evaluated, in particular NN weights reconstruction and generation.
Empowering Medical Multi-Agents with Clinical Consultation Flow for Dynamic Diagnosis
Authors: Sihan Wang, Suiyang Jiang, Yibo Gao, Boming Wang, Shangqi Gao, Xiahai Zhuang
Date: 2025-03-19 00:00:00
Tasks: Decision Making, Diagnostic, Reinforcement Learning (RL)

Traditional AI-based healthcare systems often rely on single-modal data, limiting diagnostic accuracy due to incomplete information. However, recent advancements in foundation models show promising potential for enhancing diagnosis combining multi-modal information. While these models excel in static tasks, they struggle with dynamic diagnosis, failing to manage multi-turn interactions and often making premature diagnostic decisions due to insufficient persistence in information collection.To address this, we propose a multi-agent framework inspired by consultation flow and reinforcement learning (RL) to simulate the entire consultation process, integrating multiple clinical information for effective diagnosis. Our approach incorporates a hierarchical action set, structured from clinic consultation flow and medical textbook, to effectively guide the decision-making process. This strategy improves agent interactions, enabling them to adapt and optimize actions based on the dynamic state. We evaluated our framework on a public dynamic diagnosis benchmark. The proposed framework evidentially improves the baseline methods and achieves state-of-the-art performance compared to existing foundation model-based methods.
InvoZone
Authors: amelia john
Date: 2014-01-01 00:00:00
Proceeding: https://invozone.com/ 2014 1

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AsthmaSCELNet: A Lightweight Supervised Contrastive Embedding Learning Framework For Asthma Classification Using Lung Sounds
Authors: Arka Roy, Udit Satija
Date: 2023-08-01 00:00:00
Proceeding: Interspeech 2023 8
Tasks: Asthmatic Lung Sound Classification, Audio Classification, Classification, Diagnostic, Lung Disease Classification, Lung Sound Classification, Specificity

Asthma is one of the most prevalent respiratory disorders, which can be identified by different modalities such as speech, wheezing of lung sounds (LSs), spirometric measures, etc. In this paper, we propose AsthmaSCELNet, a lightweight supervised contrastive embedding learning framework, to classify asthmatic LSs by providing adequate classification margin across the embeddings of healthy and asthma LS, in contrast to vanilla supervised learning. Our proposed framework consists of three steps: pre-processing, melspectrogram extraction, and classification. The AsthmaSCELNet consists of two stages: embedding learning using a lightweight embedding extraction backbone module that extracts compact embedding from the melspectrogram, and classification by the learnt embeddings using multi-layer perceptrons. The proposed framework achieves an accuracy, sensitivity, and specificity of 98.54%, 98.27%, and 98.73% respectively, that outperforms existing methods based on LSs and other modalities.
Benign Overfitting with Quantum Kernels
Authors: Joachim Tomasi, Sandrine Anthoine, Hachem Kadri
Date: 2025-03-21 00:00:00

Quantum kernels quantify similarity between data points by measuring the inner product between quantum states, computed through quantum circuit measurements. By embedding data into quantum systems, quantum kernel feature maps, that may be classically intractable to compute, could efficiently exploit high-dimensional Hilbert spaces to capture complex patterns. However, designing effective quantum feature maps remains a major challenge. Many quantum kernels, such as the fidelity kernel, suffer from exponential concentration, leading to near-identity kernel matrices that fail to capture meaningful data correlations and lead to overfitting and poor generalization. In this paper, we propose a novel strategy for constructing quantum kernels that achieve good generalization performance, drawing inspiration from benign overfitting in classical machine learning. Our approach introduces the concept of local-global quantum kernels, which combine two complementary components: a local quantum kernel based on measurements of small subsystems and a global quantum kernel derived from full-system measurements. Through numerical experiments, we demonstrate that local-global quantum kernels exhibit benign overfitting, supporting the effectiveness of our approach in enhancing quantum kernel methods.
AsTFSONN: A Unified Framework Based on Time-Frequency Domain Self-Operational Neural Network for Asthmatic Lung Sound Classification
Authors: Arka Roy, Udit Satija
Date: 2023-07-10 00:00:00
Proceeding: 2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA) 2023 7
Tasks: Asthmatic Lung Sound Classification, Audio Classification, Diagnostic, Lung Disease Classification, Lung Sound Classification, Sound Classification, Specificity

Asthma is one of the most severe chronic respiratory diseases which can be diagnosed using several modalities, such as lung function test or spirometric measures, peak flow meter-based measures, sputum eosinophils, pathological speech, and wheezing events of the lung auscultation sound, etc. Lung sound examinations are more accurate for diagnosing respiratory problems since these are associated with respiratory abnormalities occurred due to pulmonary disorders. In this paper, we propose a time-frequency domain self-operational neural network (SONN) based framework, namely, AsTFSONN, to efficiently categorize asthmatic lung sound signals, which uses the SONN-based heterogeneous neural model by incorporating an additional non-linearity into the neural network architecture, unlike the vanilla convolutional neural model that uses homogeneous perceptions which resemble the fundamental linear neuron model. The proposed framework comprises three major stages: pre-processing of the input lung sounds, mel-spectrogram time-frequency representation (TFR) extraction, and finally, classification using AsTFSONN based on the mel-spectrogram images. The proposed framework supersedes the notable prior works of asthma classification based on lung sounds and other diagnostic modalities by achieving the highest accuracy, specificity, sensitivity, and ICBHI-score of 98.50%, 98.80%, 98.11%, and 98.46%, respectively, using lung sounds as the input diagnostic modality, as evaluated on publicly available chest wall lung sound dataset.