pwc-archive/papers-with-abstracts huggingface dataset:
SEKI: Self-Evolution and Knowledge Inspiration based Neural Architecture Search via Large Language Models
Authors: Zicheng Cai, Yaohua Tang, Yutao Lai, Hua Wang, Zhi Chen, Hao Chen
Date: 2025-02-27 00:00:00
Tasks: GPU, Knowledge Distillation, Language Modeling, Language Modelling, Large Language Model, Neural Architecture Search

We introduce SEKI, a novel large language model (LLM)-based neural architecture search (NAS) method. Inspired by the chain-of-thought (CoT) paradigm in modern LLMs, SEKI operates in two key stages: self-evolution and knowledge distillation. In the self-evolution stage, LLMs initially lack sufficient reference examples, so we implement an iterative refinement mechanism that enhances architectures based on performance feedback. Over time, this process accumulates a repository of high-performance architectures. In the knowledge distillation stage, LLMs analyze common patterns among these architectures to generate new, optimized designs. Combining these two stages, SEKI greatly leverages the capacity of LLMs on NAS and without requiring any domain-specific data. Experimental results show that SEKI achieves state-of-the-art (SOTA) performance across various datasets and search spaces while requiring only 0.05 GPU-days, outperforming existing methods in both efficiency and accuracy. Furthermore, SEKI demonstrates strong generalization capabilities, achieving SOTA-competitive results across multiple tasks.
Mitigating Hallucinations in Large Vision-Language Models by Adaptively Constraining Information Flow
Authors: Jiaqi Bai, Hongcheng Guo, Zhongyuan Peng, Jian Yang, Zhoujun Li, Mohan Li, Zhihong Tian
Date: 2025-02-28 00:00:00
Tasks: Hallucination, Object, Object Hallucination, Semantic Similarity, Semantic Textual Similarity

Large vision-language models show tremendous potential in understanding visual information through human languages. However, they are prone to suffer from object hallucination, i.e., the generated image descriptions contain objects that do not exist in the image. In this paper, we reveal that object hallucination can be attributed to overconfidence in irrelevant visual features when soft visual tokens map to the LLM's word embedding space. Specifically, by figuring out the semantic similarity between visual tokens and LLM's word embedding, we observe that the smoothness of similarity distribution strongly correlates with the emergence of object hallucinations. To mitigate hallucinations, we propose using the Variational Information Bottleneck (VIB) to alleviate overconfidence by introducing stochastic noise, facilitating the constraining of irrelevant information. Furthermore, we propose an entropy-based noise-controlling strategy to enable the injected noise to be adaptively constrained regarding the smoothness of the similarity distribution. We adapt the proposed AdaVIB across distinct model architectures. Experimental results demonstrate that the proposed AdaVIB mitigates object hallucinations by effectively alleviating the overconfidence in irrelevant visual features, with consistent improvements on two object hallucination benchmarks.
A Pilot Empirical Study on When and How to Use Knowledge Graphs as Retrieval Augmented Generation
Authors: Xujie Yuan, Yongxu Liu, Shimin Di, Shiwen Wu, Libin Zheng, Rui Meng, Lei Chen, Xiaofang Zhou, Jian Yin
Date: 2025-02-28 00:00:00
Tasks: Knowledge Graphs, RAG, Retrieval-augmented Generation

The integration of Knowledge Graphs (KGs) into the Retrieval Augmented Generation (RAG) framework has attracted significant interest, with early studies showing promise in mitigating hallucinations and improving model accuracy. However, a systematic understanding and comparative analysis of the rapidly emerging KG-RAG methods are still lacking. This paper seeks to lay the foundation for systematically answering the question of when and how to use KG-RAG by analyzing their performance in various application scenarios associated with different technical configurations. After outlining the mind map using KG-RAG framework and summarizing its popular pipeline, we conduct a pilot empirical study of KG-RAG works to reimplement and evaluate 6 KG-RAG methods across 9 datasets in diverse domains and scenarios, analyzing the impact of 9 KG-RAG configurations in combination with 17 LLMs, and combining Metacognition with KG-RAG as a pilot attempt. Our results underscore the critical role of appropriate application conditions and optimal configurations of KG-RAG components.
FedConv: A Learning-on-Model Paradigm for Heterogeneous Federated Clients
Authors: Leming Shen, Qiang Yang, Kaiyan Cui, Yuanqing Zheng, Xiao-Yong Wei, Jianwei Liu, Jinsong Han
Date: 2025-02-28 00:00:00
Tasks: Federated Learning

Federated Learning (FL) facilitates collaborative training of a shared global model without exposing clients' private data. In practical FL systems, clients (e.g., edge servers, smartphones, and wearables) typically have disparate system resources. Conventional FL, however, adopts a one-size-fits-all solution, where a homogeneous large global model is transmitted to and trained on each client, resulting in an overwhelming workload for less capable clients and starvation for other clients. To address this issue, we propose FedConv, a client-friendly FL framework, which minimizes the computation and memory burden on resource-constrained clients by providing heterogeneous customized sub-models. FedConv features a novel learning-on-model paradigm that learns the parameters of the heterogeneous sub-models via convolutional compression. Unlike traditional compression methods, the compressed models in FedConv can be directly trained on clients without decompression. To aggregate the heterogeneous sub-models, we propose transposed convolutional dilation to convert them back to large models with a unified size while retaining personalized information from clients. The compression and dilation processes, transparent to clients, are optimized on the server leveraging a small public dataset. Extensive experiments on six datasets demonstrate that FedConv outperforms state-of-the-art FL systems in terms of model accuracy (by more than 35% on average), computation and communication overhead (with 33% and 25% reduction, respectively).
Retrieval Backward Attention without Additional Training: Enhance Embeddings of Large Language Models via Repetition
Authors: Yifei Duan, Raphael Shang, Deng Liang, Yongqiang Cai
Date: 2025-02-28 00:00:00
Tasks: Zero-Shot Learning

Language models can be viewed as functions that embed text into Euclidean space, where the quality of the embedding vectors directly determines model performance, training such neural networks involves various uncertainties. This paper focuses on improving the performance of pre-trained language models in zero-shot settings through a simple and easily implementable method. We propose a novel backward attention mechanism to enhance contextual information encoding. Evaluated on the Chinese Massive Text Embedding Benchmark (C-MTEB), our approach achieves significant improvements across multiple tasks, providing valuable insights for advancing zero-shot learning capabilities.
FRU-Adapter: Frame Recalibration Unit Adapter for Dynamic Facial Expression Recognition
Authors: Myungbeom Her, Hamza Ghulam Nabi, and Ji-HyeongHan*
Date: 2025-02-28 00:00:00
Proceeding: Electronics 2025 2
Tasks: Dynamic Facial Expression Recognition, Facial Expression Recognition

Dynamic facial expression recognition (DFER) is one of the most important
 challenges in computer vision, as it plays a crucial role in human–computer interaction. Re
cently, adapter-based approaches have been introduced into DFER, and they have achieved
 remarkable success. However, the adapters still suffer from the following problems: over
looking irrelevant frames and interference with pre-trained information. In this paper, we
 propose a frame recalibration unit adapter (FRU-Adapter) which combines the strengths of
 a frame recalibration unit (FRU) and temporal self-attention (T-SA) to address the afore
mentioned issues. The FRU initially recalibrates the frames by emphasizing important
 frames and suppressing less relevant frames. The recalibrated frames are then fed into
 T-SA to capture the correlations between meaningful frames. As a result, the FRU-Adapter
 captures enhanced temporal dependencies by considering the irrelevant frames in a clip.
 Furthermore, we propose a method for attaching the FRU-Adapter to each encoder layer
 in parallel to reduce the loss of pre-trained information. Notably, the FRU-Adapter uses
 only 2% of the total training parameters per task while achieving an improved accuracy.
 Extended experiments on DFER tasks show that the proposed FRU-Adapter not only out
performs the state-of-the-art models but also exhibits parameter efficiency. The source code
 will be made publicly available.
S4ConvD: Adaptive Scaling and Frequency Adjustment for Energy-Efficient Sensor Networks in Smart Buildings
Authors: Melanie Schaller, Bodo Rosenhahn
Date: 2025-02-28 00:00:00
Tasks: GPU, State Space Models

Predicting energy consumption in smart buildings is challenging due to dependencies in sensor data and the variability of environmental conditions. We introduce S4ConvD, a novel convolutional variant of Deep State Space Models (Deep-SSMs), that minimizes reliance on extensive preprocessing steps. S4ConvD is designed to optimize runtime in resource-constrained environments. By implementing adaptive scaling and frequency adjustments, this model shows to capture complex temporal patterns in building energy dynamics. Experiments on the ASHRAE Great Energy Predictor III dataset reveal that S4ConvD outperforms current benchmarks. Additionally, S4ConvD benefits from significant improvements in GPU runtime through the use of Block Tiling optimization techniques. Thus, S4ConvD has the potential for practical deployment in real-time energy modeling. Furthermore, the complete codebase and dataset are accessible on GitHub, fostering open-source contributions and facilitating further research. Our method also promotes resource-efficient model execution, enhancing both energy forecasting and the potential integration of renewable energy sources into smart grid systems.
BST: Badminton Stroke-type Transformer for Skeleton-based Action Recognition in Racket Sports
Authors: Jing-Yuan Chang
Date: 2025-02-28 00:00:00
Tasks: Action Recognition, Line Detection, Pose Estimation, Skeleton Based Action Recognition, Video Segmentation, Video Semantic Segmentation

Badminton, known for having the fastest ball speeds among all sports, presents significant challenges to the field of computer vision, including player identification, court line detection, shuttlecock trajectory tracking, and player stroke-type classification. In this paper, we introduce a novel video segmentation strategy to extract frames of each player's racket swing in a badminton broadcast match. These segmented frames are then processed by two existing models: one for Human Pose Estimation to obtain player skeletal joints, and the other for shuttlecock trajectory detection to extract shuttlecock trajectories. Leveraging these joints, trajectories, and player positions as inputs, we propose Badminton Stroke-type Transformer (BST) to classify player stroke-types in singles. To the best of our knowledge, experimental results demonstrate that our method outperforms the previous state-of-the-art on the largest publicly available badminton video dataset, ShuttleSet, which shows that effectively leveraging ball trajectory is likely to be a trend for racket sports action recognition.
Fast and Accurate Gigapixel Pathological Image Classification with Hierarchical Distillation Multi-Instance Learning
Authors: Jiuyang Dong, Junjun Jiang, Kui Jiang, Jiahan Li, Yongbing Zhang
Date: 2025-02-28 00:00:00
Proceeding: CVPR 2025 1
Tasks: image-classification, Image Classification, whole slide images

Although multi-instance learning (MIL) has succeeded in pathological image classification, it faces the challenge of high inference costs due to processing numerous patches from gigapixel whole slide images (WSIs). To address this, we propose HDMIL, a hierarchical distillation multi-instance learning framework that achieves fast and accurate classification by eliminating irrelevant patches. HDMIL consists of two key components: the dynamic multi-instance network (DMIN) and the lightweight instance pre-screening network (LIPN). DMIN operates on high-resolution WSIs, while LIPN operates on the corresponding low-resolution counterparts. During training, DMIN are trained for WSI classification while generating attention-score-based masks that indicate irrelevant patches. These masks then guide the training of LIPN to predict the relevance of each low-resolution patch. During testing, LIPN first determines the useful regions within low-resolution WSIs, which indirectly enables us to eliminate irrelevant regions in high-resolution WSIs, thereby reducing inference time without causing performance degradation. In addition, we further design the first Chebyshev-polynomials-based Kolmogorov-Arnold classifier in computational pathology, which enhances the performance of HDMIL through learnable activation layers. Extensive experiments on three public datasets demonstrate that HDMIL outperforms previous state-of-the-art methods, e.g., achieving improvements of 3.13% in AUC while reducing inference time by 28.6% on the Camelyon16 dataset.
MAMUT: A Novel Framework for Modifying Mathematical Formulas for the Generation of Specialized Datasets for Language Model Training
Authors: Jonathan Drechsel, Anja Reusch, Steffen Herbold
Date: 2025-02-28 00:00:00
Tasks: Language Modeling, Language Modelling, Math

Mathematical formulas are a fundamental and widely used component in various scientific fields, serving as a universal language for expressing complex concepts and relationships. While state-of-the-art transformer models excel in processing and understanding natural language, they encounter challenges with mathematical notation, which involves a complex structure and diverse representations. This study focuses on the development of specialized training datasets to enhance the encoding of mathematical content. We introduce Math Mutator (MAMUT), a framework capable of generating equivalent and falsified versions of a given mathematical formula in LaTeX notation, effectively capturing the mathematical variety in notation of the same concept. Based on MAMUT, we have generated four large mathematical datasets containing diverse notation, which can be used to train language models with enhanced mathematical embeddings.
Improved 3D Point-Line Mapping Regression for Camera Relocalization
Authors: Bach-Thuan Bui, Huy-Hoang Bui, Yasuyuki Fujii, Dinh-Tuan Tran, Joo-Ho Lee
Date: 2025-02-28 00:00:00
Tasks: Camera Relocalization, regression

In this paper, we present a new approach for improving 3D point and line mapping regression for camera re-localization. Previous methods typically rely on feature matching (FM) with stored descriptors or use a single network to encode both points and lines. While FM-based methods perform well in large-scale environments, they become computationally expensive with a growing number of mapping points and lines. Conversely, approaches that learn to encode mapping features within a single network reduce memory footprint but are prone to overfitting, as they may capture unnecessary correlations between points and lines. We propose that these features should be learned independently, each with a distinct focus, to achieve optimal accuracy. To this end, we introduce a new architecture that learns to prioritize each feature independently before combining them for localization. Experimental results demonstrate that our approach significantly enhances the 3D map point and line regression performance for camera re-localization. The implementation of our method will be publicly available at: https://github.com/ais-lab/pl2map/.
Everything, Everywhere, All at Once: Is Mechanistic Interpretability Identifiable?
Authors: Maxime Méloux, Silviu Maniu, François Portet, Maxime Peyrard
Date: 2025-02-28 00:00:00
Tasks: All

As AI systems are used in high-stakes applications, ensuring interpretability is crucial. Mechanistic Interpretability (MI) aims to reverse-engineer neural networks by extracting human-understandable algorithms to explain their behavior. This work examines a key question: for a given behavior, and under MI's criteria, does a unique explanation exist? Drawing on identifiability in statistics, where parameters are uniquely inferred under specific assumptions, we explore the identifiability of MI explanations. We identify two main MI strategies: (1) "where-then-what," which isolates a circuit replicating model behavior before interpreting it, and (2) "what-then-where," which starts with candidate algorithms and searches for neural activation subspaces implementing them, using causal alignment. We test both strategies on Boolean functions and small multi-layer perceptrons, fully enumerating candidate explanations. Our experiments reveal systematic non-identifiability: multiple circuits can replicate behavior, a circuit can have multiple interpretations, several algorithms can align with the network, and one algorithm can align with different subspaces. Is uniqueness necessary? A pragmatic approach may require only predictive and manipulability standards. If uniqueness is essential for understanding, stricter criteria may be needed. We also reference the inner interpretability framework, which validates explanations through multiple criteria. This work contributes to defining explanation standards in AI.
Excertos da Historia Keynesiana
Authors: Gustavo Lima Moura
Date: 2025-02-21 00:00:00

This article analytically describes the contributions of Keynesian theory in the post-World War I context, by means of a synoptic reading of the bibliographical sources indicated, the Keynesian theory within its own historical and philosophical context. The discussion covers the main concepts of his theory. The aim is, through the excerpts highlighted in the article, to briefly contextualize the origins of the ideas of John Maynard Keynes' economic thought.
GauCho: Gaussian Distributions with Cholesky Decomposition for Oriented Object Detection
Authors: Jeffri Murrugarra-LLerena, Jose Henrique Lima Marques, Claudio R. Jung
Date: 2025-02-03 00:00:00
Proceeding: CVPR 2025 1
Tasks: object-detection, Object Detection, Oriented Object Detection, regression

Oriented Object Detection (OOD) has received increased attention in the past years, being a suitable solution for detecting elongated objects in remote sensing analysis. In particular, using regression loss functions based on Gaussian distributions has become attractive since they yield simple and differentiable terms. However, existing solutions are still based on regression heads that produce Oriented Bounding Boxes (OBBs), and the known problem of angular boundary discontinuity persists. In this work, we propose a regression head for OOD that directly produces Gaussian distributions based on the Cholesky matrix decomposition. The proposed head, named GauCho, theoretically mitigates the boundary discontinuity problem and is fully compatible with recent Gaussian-based regression loss functions. Furthermore, we advocate using Oriented Ellipses (OEs) to represent oriented objects, which relates to GauCho through a bijective function and alleviates the encoding ambiguity problem for circular objects. Our experimental results show that GauCho can be a viable alternative to the traditional OBB head, achieving results comparable to or better than state-of-the-art detectors for the challenging dataset DOTA
NutriGen: Personalized Meal Plan Generator Leveraging Large Language Models to Enhance Dietary and Nutritional Adherence
Authors: Saman Khamesian, Asiful Arefeen, Stephanie M. Carpenter, Hassan Ghasemzadeh
Date: 2025-02-28 00:00:00
Tasks: Nutrition, Prompt Engineering, Recommendation Systems

Maintaining a balanced diet is essential for overall health, yet many individuals struggle with meal planning due to nutritional complexity, time constraints, and lack of dietary knowledge. Personalized food recommendations can help address these challenges by tailoring meal plans to individual preferences, habits, and dietary restrictions. However, existing dietary recommendation systems often lack adaptability, fail to consider real-world constraints such as food ingredient availability, and require extensive user input, making them impractical for sustainable and scalable daily use. To address these limitations, we introduce NutriGen, a framework based on large language models (LLM) designed to generate personalized meal plans that align with user-defined dietary preferences and constraints. By building a personalized nutrition database and leveraging prompt engineering, our approach enables LLMs to incorporate reliable nutritional references like the USDA nutrition database while maintaining flexibility and ease-of-use. We demonstrate that LLMs have strong potential in generating accurate and user-friendly food recommendations, addressing key limitations in existing dietary recommendation systems by providing structured, practical, and scalable meal plans. Our evaluation shows that Llama 3.1 8B and GPT-3.5 Turbo achieve the lowest percentage errors of 1.55\% and 3.68\%, respectively, producing meal plans that closely align with user-defined caloric targets while minimizing deviation and improving precision. Additionally, we compared the performance of DeepSeek V3 against several established models to evaluate its potential in personalized nutrition planning.
Gungnir: Exploiting Stylistic Features in Images for Backdoor Attacks on Diffusion Models
Authors: Yu Pan, Jiahao Chen, Bingrong Dai, Lin Wang, Yi Du, Jiao Liu
Date: 2025-02-28 00:00:00
Tasks: Backdoor Attack, backdoor defense, Image Generation

In recent years, Diffusion Models (DMs) have demonstrated significant advances in the field of image generation. However, according to current research, DMs are vulnerable to backdoor attacks, which allow attackers to control the model's output by inputting data containing covert triggers, such as a specific visual patch or phrase. Existing defense strategies are well equipped to thwart such attacks through backdoor detection and trigger inversion because previous attack methods are constrained by limited input spaces and low-dimensional triggers. For example, visual triggers are easily observed by defenders, text-based or attention-based triggers are more susceptible to neural network detection. To explore more possibilities of backdoor attack in DMs, we propose Gungnir, a novel method that enables attackers to activate the backdoor in DMs through style triggers within input images. Our approach proposes using stylistic features as triggers for the first time and implements backdoor attacks successfully in image-to-image tasks by introducing Reconstructing-Adversarial Noise (RAN) and Short-Term Timesteps-Retention (STTR). Our technique generates trigger-embedded images that are perceptually indistinguishable from clean images, thus bypassing both manual inspection and automated detection neural networks. Experiments demonstrate that Gungnir can easily bypass existing defense methods. Among existing DM defense frameworks, our approach achieves a 0 backdoor detection rate (BDR). Our codes are available at https://github.com/paoche11/Gungnir.