pwc-archive/papers-with-abstracts huggingface dataset:
Road Traffic Sign Recognition method using Siamese network Combining Efficient-CNN based Encoder
Authors: Zhenghao Xi, Yuchao Shao, Yang Zheng, Xiang Liu, Yaqi Liu, Yitong Cai
Date: 2025-02-21 00:00:00
Tasks: Traffic Sign Recognition

Traffic signs recognition (TSR) plays an essential role in assistant driving and intelligent transportation system. However, the noise of complex environment may lead to motion-blur or occlusion problems, which raise the tough challenge to real-time recognition with high accuracy and robust. In this article, we propose IECES-network which with improved encoders and Siamese net. The three-stage approach of our method includes Efficient-CNN based encoders, Siamese backbone and the fully-connected layers. We firstly use convolutional encoders to extract and encode the traffic sign features of augmented training samples and standard images. Then, we design the Siamese neural network with Efficient-CNN based encoder and contrastive loss function, which can be trained to improve the robustness of TSR problem when facing the samples of motion-blur and occlusion by computing the distance between inputs and templates. Additionally, the template branch of the proposed network can be stopped when executing the recognition tasks after training to raise the process speed of our real-time model, and alleviate the computational resource and parameter scale. Finally, we recombined the feature code and a fully-connected layer with SoftMax function to classify the codes of samples and recognize the category of traffic signs. The results of experiments on the Tsinghua-Tencent 100K dataset and the German Traffic Sign Recognition Benchmark dataset demonstrate the performance of the proposed IECESnetwork. Compared with other state-of-the-art methods, in the case of motion-blur and occluded environment, the proposed method achieves competitive performance precision-recall and accuracy metric average is 88.1%, 86.43% and 86.1% with a 2.9M lightweight scale, respectively. Moreover, processing time of our model is 0.1s per frame, of which the speed is increased by 1.5 times compared with existing methods.
Implicit Neural Representations for Chemical Reaction Paths
Authors: Kalyan Ramakrishnan, Lars L. Schaaf, Chen Lin, Guangrun Wang, Philip Torr
Date: 2025-02-20 00:00:00

We show that neural networks can be optimized to represent minimum energy paths as continuous functions, offering a flexible alternative to discrete path-search methods like Nudged Elastic Band (NEB). Our approach parameterizes reaction paths with a network trained on a loss function that discards tangential energy gradients and enables instant estimation of the transition state. We first validate the method on two-dimensional potentials and then demonstrate its advantages over NEB on challenging atomistic systems where (i) poor initial guesses yield unphysical paths, (ii) multiple competing paths exist, or (iii) the reaction follows a complex multi-step mechanism. Results highlight the versatility of the method: for instance, a simple adjustment to the sampling strategy during optimization can help escape local-minimum solutions. Finally, in a low-dimensional setting, we demonstrate that a single neural network can learn from existing paths and generalize to unseen systems, showing promise for a universal reaction path representation.
Exploring the Role of Artificial Intelligence and Machine Learning in Process Optimization for Chemical Industry
Authors: Zishuo Lin, Jiajie Wang, Zhe Yan, Peiyong Ma
Date: 2025-02-15 00:00:00

The crucial field of Optical Chemical Structure Recognition (OCSR) aims to transform chemical structure photographs into machine-readable formats so that chemical databases may be efficiently stored and queried. Although a number of OCSR technologies have been created, little is known about how well they work in different picture deterioration scenarios. In this work, a new dataset of chemically structured images that have been systematically harmed graphically by compression, noise, distortion, and black overlays is presented. On these subsets, publicly accessible OCSR tools were thoroughly tested to determine how resilient they were to unfavorable circumstances. The outcomes show notable performance variation, underscoring each tool's advantages and disadvantages. Interestingly, MolScribe performed best under heavy compression (55.8% at 99%) and had the highest identification rate on undamaged photos (94.6%). MolVec performed exceptionally well against noise and black overlay (86.8% at 40%), although it declined under extreme distortion (<70%). With recognition rates below 30%, Decimer demonstrated strong sensitivity to noise and black overlay, but Imago had the lowest baseline accuracy (73.6%). The creative assessment of this study offers important new information about how well the OCSR tool performs when images deteriorate, as well as useful standards for tool development in the future.
MQADet: A Plug-and-Play Paradigm for Enhancing Open-Vocabulary Object Detection via Multimodal Question Answering
Authors: Caixiong Li, Xiongwei Zhao, Jinhang Zhang, Xing Zhang, Zhou Wu
Date: 2025-02-23 00:00:00
Tasks: Object, object-detection, Object Detection, Open-vocabulary object detection, Open Vocabulary Object Detection, Question Answering

Open-vocabulary detection (OVD) is a challenging task to detect and classify objects from an unrestricted set of categories, including those unseen during training. Existing open-vocabulary detectors are limited by complex visual-textual misalignment and long-tailed category imbalances, leading to suboptimal performance in challenging scenarios. To address these limitations, we introduce \textbf{MQADet}, a universal paradigm for enhancing existing open-vocabulary detectors by leveraging the cross-modal reasoning capabilities of multimodal large language models (MLLMs). MQADet functions as a plug-and-play solution that integrates seamlessly with pre-trained object detectors without substantial additional training costs. Specifically, we design a novel three-stage Multimodal Question Answering (MQA) pipeline to guide the MLLMs to precisely localize complex textual and visual targets while effectively enhancing the focus of existing object detectors on relevant objects. To validate our approach, we present a new benchmark for evaluating our paradigm on four challenging open-vocabulary datasets, employing three state-of-the-art object detectors as baselines. Experimental results demonstrate that our proposed paradigm significantly improves the performance of existing detectors, particularly in unseen complex categories, across diverse and challenging scenarios. To facilitate future research, we will publicly release our code.
Automatically Evaluating the Paper Reviewing Capability of Large Language Models
Authors: Hyungyu Shin, Jingyu Tang, Yoonjoo Lee, Nayoung Kim, Hyunseung Lim, Ji Yong Cho, Hwajung Hong, Moontae Lee, Juho Kim
Date: 2025-02-24 00:00:00

Peer review is essential for scientific progress, but it faces challenges such as reviewer shortages and growing workloads. Although Large Language Models (LLMs) show potential for providing assistance, research has reported significant limitations in the reviews they generate. While the insights are valuable, conducting the analysis is challenging due to the considerable time and effort required, especially given the rapid pace of LLM developments. To address the challenge, we developed an automatic evaluation pipeline to assess the LLMs' paper review capability by comparing them with expert-generated reviews. By constructing a dataset consisting of 676 OpenReview papers, we examined the agreement between LLMs and experts in their strength and weakness identifications. The results showed that LLMs lack balanced perspectives, significantly overlook novelty assessment when criticizing, and produce poor acceptance decisions. Our automated pipeline enables a scalable evaluation of LLMs' paper review capability over time.
DICEPTION: A Generalist Diffusion Model for Visual Perceptual Tasks
Authors: Canyu Zhao, MingYu Liu, Huanyi Zheng, Muzhi Zhu, Zhiyue Zhao, Hao Chen, Tong He, Chunhua Shen
Date: 2025-02-24 00:00:00
Tasks: Conditional Image Generation, Image Generation, Semantic Segmentation

Our primary goal here is to create a good, generalist perception model that can tackle multiple tasks, within limits on computational resources and training data. To achieve this, we resort to text-to-image diffusion models pre-trained on billions of images. Our exhaustive evaluation metrics demonstrate that DICEPTION effectively tackles multiple perception tasks, achieving performance on par with state-of-the-art models. We achieve results on par with SAM-vit-h using only 0.06% of their data (e.g., 600K vs. 1B pixel-level annotated images). Inspired by Wang et al., DICEPTION formulates the outputs of various perception tasks using color encoding; and we show that the strategy of assigning random colors to different instances is highly effective in both entity segmentation and semantic segmentation. Unifying various perception tasks as conditional image generation enables us to fully leverage pre-trained text-to-image models. Thus, DICEPTION can be efficiently trained at a cost of orders of magnitude lower, compared to conventional models that were trained from scratch. When adapting our model to other tasks, it only requires fine-tuning on as few as 50 images and 1% of its parameters. DICEPTION provides valuable insights and a more promising solution for visual generalist models.
Towards Hierarchical Rectified Flow
Authors: Yichi Zhang, Yici Yan, Alex Schwing, Zhizhen Zhao
Date: 2025-02-24 00:00:00

We formulate a hierarchical rectified flow to model data distributions. It hierarchically couples multiple ordinary differential equations (ODEs) and defines a time-differentiable stochastic process that generates a data distribution from a known source distribution. Each ODE resembles the ODE that is solved in a classic rectified flow, but differs in its domain, i.e., location, velocity, acceleration, etc. Unlike the classic rectified flow formulation, which formulates a single ODE in the location domain and only captures the expected velocity field (sufficient to capture a multi-modal data distribution), the hierarchical rectified flow formulation models the multi-modal random velocity field, acceleration field, etc., in their entirety. This more faithful modeling of the random velocity field enables integration paths to intersect when the underlying ODE is solved during data generation. Intersecting paths in turn lead to integration trajectories that are more straight than those obtained in the classic rectified flow formulation, where integration paths cannot intersect. This leads to modeling of data distributions with fewer neural function evaluations. We empirically verify this on synthetic 1D and 2D data as well as MNIST, CIFAR-10, and ImageNet-32 data. Code is available at: https://riccizz.github.io/HRF/.
SalM$2$: An Extremely Lightweight Saliency Mamba Model for Real-Time Cognitive Awareness of Driver Attention
Authors: Chunyu Zhao, Wentao Mu, Xian Zhou, Wenbo Liu, Fei Yan, Tao Deng
Date: 2025-02-22 00:00:00
Tasks: Mamba

Driver attention recognition in driving scenarios is a popular direction in traffic scene perception technology. It aims to understand human driver attention to focus on specific targets/objects in the driving scene. However, traffic scenes contain not only a large amount of visual information but also semantic information related to driving tasks. Existing methods lack attention to the actual semantic information present in driving scenes. Additionally, the traffic scene is a complex and dynamic process that requires constant attention to objects related to the current driving task. Existing models, influenced by their foundational frameworks, tend to have large parameter counts and complex structures. Therefore, this paper proposes a real-time saliency Mamba network based on the latest Mamba framework. As shown in Figure 1, our model uses very few parameters (0.08M, only 0.09~11.16% of other models), while maintaining SOTA performance or achieving over 98% of the SOTA model's performance.
DOEI: Dual Optimization of Embedding Information for Attention-Enhanced Class Activation Maps
Authors: Hongjie Zhu, Zeyu Zhang, Guansong Pang, Xu Wang, Shimin Wen, Yu Bai, Daji Ergu, Ying Cai, Yang Zhao
Date: 2025-02-21 00:00:00
Tasks: Semantic Segmentation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

Weakly supervised semantic segmentation (WSSS) typically utilizes limited semantic annotations to obtain initial Class Activation Maps (CAMs). However, due to the inadequate coupling between class activation responses and semantic information in high-dimensional space, the CAM is prone to object co-occurrence or under-activation, resulting in inferior recognition accuracy. To tackle this issue, we propose DOEI, Dual Optimization of Embedding Information, a novel approach that reconstructs embedding representations through semantic-aware attention weight matrices to optimize the expression capability of embedding information. Specifically, DOEI amplifies tokens with high confidence and suppresses those with low confidence during the class-to-patch interaction. This alignment of activation responses with semantic information strengthens the propagation and decoupling of target features, enabling the generated embeddings to more accurately represent target features in high-level semantic space. In addition, we propose a hybrid-feature alignment module in DOEI that combines RGB values, embedding-guided features, and self-attention weights to increase the reliability of candidate tokens. Comprehensive experiments show that DOEI is an effective plug-and-play module that empowers state-of-the-art visual transformer-based WSSS models to significantly improve the quality of CAMs and segmentation performance on popular benchmarks, including PASCAL VOC (+3.6%, +1.5%, +1.2% mIoU) and MS COCO (+1.2%, +1.6% mIoU). Code will be available at https://github.com/AIGeeksGroup/DOEI.
High-resolution Rainy Image Synthesis: Learning from Rendering
Authors: Kaibin Zhou, Shengjie Zhao, Hao Deng, Lin Zhang
Date: 2025-02-23 00:00:00
Tasks: Image Generation, Rain Removal

Currently, there are few effective methods for synthesizing a mass of high-resolution rainy images in complex illumination conditions. However, these methods are essential for synthesizing large-scale high-quality paired rainy-clean image datasets, which can train deep learning-based single image rain removal models capable of generalizing to various illumination conditions. Therefore, we propose a practical two-stage learning-from-rendering pipeline for high-resolution rainy image synthesis. The pipeline combines the benefits of the realism of rendering-based methods and the high-efficiency of learning-based methods, providing the possibility of creating large-scale high-quality paired rainy-clean image datasets. In the rendering stage, we use a rendering-based method to create a High-resolution Rainy Image (HRI) dataset, which contains realistic high-resolution paired rainy-clean images of multiple scenes and various illumination conditions. In the learning stage, to learn illumination information from background images for high-resolution rainy image generation, we propose a High-resolution Rainy Image Generation Network (HRIGNet). HRIGNet is designed to introduce a guiding diffusion model in the Latent Diffusion Model, which provides additional guidance information for high-resolution image synthesis. In our experiments, HRIGNet is able to synthesize high-resolution rainy images up to 2048x1024 resolution. Rain removal experiments on real dataset validate that our method can help improve the robustness of deep derainers to real rainy images. To make our work reproducible, source codes and the dataset have been released at https://kb824999404.github.io/HRIG/.
SFLD: Reducing the content bias for AI-generated Image Detection
Authors: Seoyeon Gye, Junwon Ko, Hyounguk Shon, Minchan Kwon, Junmo Kim
Date: 2025-02-24 00:00:00
Tasks: Diversity

Identifying AI-generated content is critical for the safe and ethical use of generative AI. Recent research has focused on developing detectors that generalize to unknown generators, with popular methods relying either on high-level features or low-level fingerprints. However, these methods have clear limitations: biased towards unseen content, or vulnerable to common image degradations, such as JPEG compression. To address these issues, we propose a novel approach, SFLD, which incorporates PatchShuffle to integrate high-level semantic and low-level textural information. SFLD applies PatchShuffle at multiple levels, improving robustness and generalization across various generative models. Additionally, current benchmarks face challenges such as low image quality, insufficient content preservation, and limited class diversity. In response, we introduce TwinSynths, a new benchmark generation methodology that constructs visually near-identical pairs of real and synthetic images to ensure high quality and content preservation. Our extensive experiments and analysis show that SFLD outperforms existing methods on detecting a wide variety of fake images sourced from GANs, diffusion models, and TwinSynths, demonstrating the state-of-the-art performance and generalization capabilities to novel generative models.
Toward Dependency Dynamics in Multi-Agent Reinforcement Learning for Traffic Signal Control
Authors: Yuli Zhang, Shangbo Wang, Dongyao Jia, Pengfei Fan, Ruiyuan Jiang, Hankang Gu, Andy H. F. Chow
Date: 2025-02-23 00:00:00
Tasks: Multi-agent Reinforcement Learning, Reinforcement Learning (RL), Traffic Signal Control

Reinforcement learning (RL) emerges as a promising data-driven approach for adaptive traffic signal control (ATSC) in complex urban traffic networks, with deep neural networks substantially augmenting its learning capabilities. However, centralized RL becomes impractical for ATSC involving multiple agents due to the exceedingly high dimensionality of the joint action space. Multi-agent RL (MARL) mitigates this scalability issue by decentralizing control to local RL agents. Nevertheless, this decentralized method introduces new challenges: the environment becomes partially observable from the perspective of each local agent due to constrained inter-agent communication. Both centralized RL and MARL exhibit distinct strengths and weaknesses, particularly under heavy intersectional traffic conditions. In this paper, we justify that MARL can achieve the optimal global Q-value by separating into multiple IRL (Independent Reinforcement Learning) processes when no spill-back congestion occurs (no agent dependency) among agents (intersections). In the presence of spill-back congestion (with agent dependency), the maximum global Q-value can be achieved by using centralized RL. Building upon the conclusions, we propose a novel Dynamic Parameter Update Strategy for Deep Q-Network (DQN-DPUS), which updates the weights and bias based on the dependency dynamics among agents, i.e. updating only the diagonal sub-matrices for the scenario without spill-back congestion. We validate the DQN-DPUS in a simple network with two intersections under varying traffic, and show that the proposed strategy can speed up the convergence rate without sacrificing optimal exploration. The results corroborate our theoretical findings, demonstrating the efficacy of DQN-DPUS in optimizing traffic signal control.
SpecDM: Hyperspectral Dataset Synthesis with Pixel-level Semantic Annotations
Authors: Wendi Liu, Pei Yang, Wenhui Hong, Xiaoguang Mei, Jiayi Ma
Date: 2025-02-24 00:00:00
Tasks: Change Detection, Dataset Generation, Semantic Segmentation

In hyperspectral remote sensing field, some downstream dense prediction tasks, such as semantic segmentation (SS) and change detection (CD), rely on supervised learning to improve model performance and require a large amount of manually annotated data for training. However, due to the needs of specific equipment and special application scenarios, the acquisition and annotation of hyperspectral images (HSIs) are often costly and time-consuming. To this end, our work explores the potential of generative diffusion model in synthesizing HSIs with pixel-level annotations. The main idea is to utilize a two-stream VAE to learn the latent representations of images and corresponding masks respectively, learn their joint distribution during the diffusion model training, and finally obtain the image and mask through their respective decoders. To the best of our knowledge, it is the first work to generate high-dimensional HSIs with annotations. Our proposed approach can be applied in various kinds of dataset generation. We select two of the most widely used dense prediction tasks: semantic segmentation and change detection, and generate datasets suitable for these tasks. Experiments demonstrate that our synthetic datasets have a positive impact on the improvement of these downstream tasks.
PAPI: Exploiting Dynamic Parallelism in Large Language Model Decoding with a Processing-In-Memory-Enabled Computing System
Authors: Yintao He, Haiyu Mao, Christina Giannoula, Mohammad Sadrosadati, Juan Gómez-Luna, Huawei Li, Xiaowei Li, Ying Wang, Onur Mutlu
Date: 2025-02-21 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Natural Language Understanding, Scheduling, Text Generation

Large language models (LLMs) are widely used for natural language understanding and text generation. An LLM model relies on a time-consuming step called LLM decoding to generate output tokens. Several prior works focus on improving the performance of LLM decoding using parallelism techniques, such as batching and speculative decoding. State-of-the-art LLM decoding has both compute-bound and memory-bound kernels. Some prior works statically identify and map these different kernels to a heterogeneous architecture consisting of both processing-in-memory (PIM) units and computation-centric accelerators. We observe that characteristics of LLM decoding kernels (e.g., whether or not a kernel is memory-bound) can change dynamically due to parameter changes to meet user and/or system demands, making (1) static kernel mapping to PIM units and computation-centric accelerators suboptimal, and (2) one-size-fits-all approach of designing PIM units inefficient due to a large degree of heterogeneity even in memory-bound kernels. In this paper, we aim to accelerate LLM decoding while considering the dynamically changing characteristics of the kernels involved. We propose PAPI (PArallel Decoding with PIM), a PIM-enabled heterogeneous architecture that exploits dynamic scheduling of compute-bound or memory-bound kernels to suitable hardware units. PAPI has two key mechanisms: (1) online kernel characterization to dynamically schedule kernels to the most suitable hardware units at runtime and (2) a PIM-enabled heterogeneous computing system that harmoniously orchestrates both computation-centric processing units and hybrid PIM units with different computing capabilities. Our experimental results on three broadly-used LLMs show that PAPI achieves 1.8$\times$ and 11.1$\times$ speedups over a state-of-the-art heterogeneous LLM accelerator and a state-of-the-art PIM-only LLM accelerator, respectively.