pwc-archive/papers-with-abstracts huggingface dataset:
Measuring and Improving Persuasiveness of Large Language Models
Authors: Somesh Singh, Yaman K Singla, Harini SI, Balaji Krishnamurthy
Date: 2024-10-03 00:00:00
Tasks: Marketing, Misinformation, Persuasiveness

LLMs are increasingly being used in workflows involving generating content to be consumed by humans (e.g., marketing) and also in directly interacting with humans (e.g., through chatbots). The development of such systems that are capable of generating verifiably persuasive messages presents both opportunities and challenges for society. On the one hand, such systems could positively impact domains like advertising and social good, such as addressing drug addiction, and on the other, they could be misused for spreading misinformation and shaping political opinions. To channel LLMs' impact on society, we need to develop systems to measure and benchmark their persuasiveness. With this motivation, we introduce PersuasionBench and PersuasionArena, the first large-scale benchmark and arena containing a battery of tasks to measure the persuasion ability of generative models automatically. We investigate to what extent LLMs know and leverage linguistic patterns that can help them generate more persuasive language. Our findings indicate that the persuasiveness of LLMs correlates positively with model size, but smaller models can also be made to have a higher persuasiveness than much larger models. Notably, targeted training using synthetic and natural datasets significantly enhances smaller models' persuasive capabilities, challenging scale-dependent assumptions. Our findings carry key implications for both model developers and policymakers. For instance, while the EU AI Act and California's SB-1047 aim to regulate AI models based on the number of floating point operations, we demonstrate that simple metrics like this alone fail to capture the full scope of AI's societal impact. We invite the community to explore and contribute to PersuasionArena and PersuasionBench, available at https://bit.ly/measure-persuasion, to advance our understanding of AI-driven persuasion and its societal implications.
Learning 3D Perception from Others' Predictions
Authors: Jinsu Yoo, Zhenyang Feng, Tai-Yu Pan, Yihong Sun, Cheng Perng Phoo, Xiangyu Chen, Mark Campbell, Kilian Q. Weinberger, Bharath Hariharan, Wei-Lun Chao
Date: 2024-10-03 00:00:00
Tasks: 3D Object Detection, object-detection, Object Detection, Pseudo Label

Accurate 3D object detection in real-world environments requires a huge amount of annotated data with high quality. Acquiring such data is tedious and expensive, and often needs repeated effort when a new sensor is adopted or when the detector is deployed in a new environment. We investigate a new scenario to construct 3D object detectors: learning from the predictions of a nearby unit that is equipped with an accurate detector. For example, when a self-driving car enters a new area, it may learn from other traffic participants whose detectors have been optimized for that area. This setting is label-efficient, sensor-agnostic, and communication-efficient: nearby units only need to share the predictions with the ego agent (e.g., car). Naively using the received predictions as ground-truths to train the detector for the ego car, however, leads to inferior performance. We systematically study the problem and identify viewpoint mismatches and mislocalization (due to synchronization and GPS errors) as the main causes, which unavoidably result in false positives, false negatives, and inaccurate pseudo labels. We propose a distance-based curriculum, first learning from closer units with similar viewpoints and subsequently improving the quality of other units' predictions via self-training. We further demonstrate that an effective pseudo label refinement module can be trained with a handful of annotated data, largely reducing the data quantity necessary to train an object detector. We validate our approach on the recently released real-world collaborative driving dataset, using reference cars' predictions as pseudo labels for the ego car. Extensive experiments including several scenarios (e.g., different sensors, detectors, and domains) demonstrate the effectiveness of our approach toward label-efficient learning of 3D perception from other units' predictions.
Why Sample Space Matters: Keyframe Sampling Optimization for LiDAR-based Place Recognition
Authors: Nikolaos Stathoulopoulos, Vidya Sumathy, Christoforos Kanellakis, George Nikolakopoulos
Date: 2024-10-03 00:00:00
Tasks: Loop Closure Detection, Pose Estimation

Recent advances in robotics are driving real-world autonomy for long-term and large-scale missions, where loop closures via place recognition are vital for mitigating pose estimation drift. However, achieving real-time performance remains challenging for resource-constrained mobile robots and multi-robot systems due to the computational burden of high-density sampling, which increases the complexity of comparing and verifying query samples against a growing map database. Conventional methods often retain redundant information or miss critical data by relying on fixed sampling intervals or operating in 3-D space instead of the descriptor feature space. To address these challenges, we introduce the concept of sample space and propose a novel keyframe sampling approach for LiDAR-based place recognition. Our method minimizes redundancy while preserving essential information in the hyper-dimensional descriptor space, supporting both learning-based and handcrafted descriptors. The proposed approach incorporates a sliding window optimization strategy to ensure efficient keyframe selection and real-time performance, enabling seamless integration into robotic pipelines. In sum, our approach demonstrates robust performance across diverse datasets, with the ability to adapt seamlessly from indoor to outdoor scenarios without parameter tuning, reducing loop closure detection times and memory requirements.
RDEIC: Accelerating Diffusion-Based Extreme Image Compression with Relay Residual Diffusion
Authors: Zhiyuan Li, Yanhui Zhou, Hao Wei, Chenyang Ge, Ajmal Mian
Date: 2024-10-03 00:00:00
Tasks: Denoising, Image Compression

Diffusion-based extreme image compression methods have achieved impressive performance at extremely low bitrates. However, constrained by the iterative denoising process that starts from pure noise, these methods are limited in both fidelity and efficiency. To address these two issues, we present Relay Residual Diffusion Extreme Image Compression (RDEIC), which leverages compressed feature initialization and residual diffusion. Specifically, we first use the compressed latent features of the image with added noise, instead of pure noise, as the starting point to eliminate the unnecessary initial stages of the denoising process. Second, we directly derive a novel residual diffusion equation from Stable Diffusion's original diffusion equation that reconstructs the raw image by iteratively removing the added noise and the residual between the compressed and target latent features. In this way, we effectively combine the efficiency of residual diffusion with the powerful generative capability of Stable Diffusion. Third, we propose a fixed-step fine-tuning strategy to eliminate the discrepancy between the training and inference phases, thereby further improving the reconstruction quality. Extensive experiments demonstrate that the proposed RDEIC achieves state-of-the-art visual quality and outperforms existing diffusion-based extreme image compression methods in both fidelity and efficiency. The source code and pre-trained models are available at https://github.com/huai-chang/RDEIC.
Spatial-Temporal Multi-Cuts for Online Multiple-Camera Vehicle Tracking
Authors: Fabian Herzog, Johannes Gilg, Philipp Wolters, Torben Teepe, Gerhard Rigoll
Date: 2024-10-03 00:00:00
Tasks: Autonomous Driving, Multi-Object Tracking, Multiple Object Tracking, Object Tracking

Accurate online multiple-camera vehicle tracking is essential for intelligent transportation systems, autonomous driving, and smart city applications. Like single-camera multiple-object tracking, it is commonly formulated as a graph problem of tracking-by-detection. Within this framework, existing online methods usually consist of two-stage procedures that cluster temporally first, then spatially, or vice versa. This is computationally expensive and prone to error accumulation. We introduce a graph representation that allows spatial-temporal clustering in a single, combined step: New detections are spatially and temporally connected with existing clusters. By keeping sparse appearance and positional cues of all detections in a cluster, our method can compare clusters based on the strongest available evidence. The final tracks are obtained online using a simple multicut assignment procedure. Our method does not require any training on the target scene, pre-extraction of single-camera tracks, or additional annotations. Notably, we outperform the online state-of-the-art on the CityFlow dataset in terms of IDF1 by more than 14%, and on the Synthehicle dataset by more than 25%, respectively. The code is publicly available.
Metrics Revolutions: Groundbreaking Insights into the Implementation of Metrics for Biomedical Image Segmentation
Authors: Gašper Podobnik, Tomaž Vrtovec
Date: 2024-10-03 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

The evaluation of segmentation performance is a common task in biomedical image analysis, with its importance emphasized in the recently released metrics selection guidelines and computing frameworks. To quantitatively evaluate the alignment of two segmentations, researchers commonly resort to counting metrics, such as the Dice similarity coefficient, or distance-based metrics, such as the Hausdorff distance, which are usually computed by publicly available open-source tools with an inherent assumption that these tools provide consistent results. In this study we questioned this assumption, and performed a systematic implementation analysis along with quantitative experiments on real-world clinical data to compare 11 open-source tools for distance-based metrics computation against our highly accurate mesh-based reference implementation. The results revealed that statistically significant differences among all open-source tools are both surprising and concerning, since they question the validity of existing studies. Besides identifying the main sources of variation, we also provide recommendations for distance-based metrics computation.
GI-GS: Global Illumination Decomposition on Gaussian Splatting for Inverse Rendering
Authors: Hongze Chen, Zehong Lin, Jun Zhang
Date: 2024-10-03 00:00:00
Tasks: 3DGS, Inverse Rendering, Novel View Synthesis

We present GI-GS, a novel inverse rendering framework that leverages 3D Gaussian Splatting (3DGS) and deferred shading to achieve photo-realistic novel view synthesis and relighting. In inverse rendering, accurately modeling the shading processes of objects is essential for achieving high-fidelity results. Therefore, it is critical to incorporate global illumination to account for indirect lighting that reaches an object after multiple bounces across the scene. Previous 3DGS-based methods have attempted to model indirect lighting by characterizing indirect illumination as learnable lighting volumes or additional attributes of each Gaussian, while using baked occlusion to represent shadow effects. These methods, however, fail to accurately model the complex physical interactions between light and objects, making it impossible to construct realistic indirect illumination during relighting. To address this limitation, we propose to calculate indirect lighting using efficient path tracing with deferred shading. In our framework, we first render a G-buffer to capture the detailed geometry and material properties of the scene. Then, we perform physically-based rendering (PBR) only for direct lighting. With the G-buffer and previous rendering results, the indirect lighting can be calculated through a lightweight path tracing. Our method effectively models indirect lighting under any given lighting conditions, thereby achieving better novel view synthesis and competitive relighting. Quantitative and qualitative results show that our GI-GS outperforms existing baselines in both rendering quality and efficiency.
High-Efficiency Neural Video Compression via Hierarchical Predictive Learning
Authors: Ming Lu, Zhihao Duan, Wuyang Cong, Dandan Ding, Fengqing Zhu, Zhan Ma
Date: 2024-10-03 00:00:00
Tasks: Motion Estimation, Video Compression

The enhanced Deep Hierarchical Video Compression-DHVC 2.0-has been introduced. This single-model neural video codec operates across a broad range of bitrates, delivering not only superior compression performance to representative methods but also impressive complexity efficiency, enabling real-time processing with a significantly smaller memory footprint on standard GPUs. These remarkable advancements stem from the use of hierarchical predictive coding. Each video frame is uniformly transformed into multiscale representations through hierarchical variational autoencoders. For a specific scale's feature representation of a frame, its corresponding latent residual variables are generated by referencing lower-scale spatial features from the same frame and then conditionally entropy-encoded using a probabilistic model whose parameters are predicted using same-scale temporal reference from previous frames and lower-scale spatial reference of the current frame. This feature-space processing operates from the lowest to the highest scale of each frame, completely eliminating the need for the complexity-intensive motion estimation and compensation techniques that have been standard in video codecs for decades. The hierarchical approach facilitates parallel processing, accelerating both encoding and decoding, and supports transmission-friendly progressive decoding, making it particularly advantageous for networked video applications in the presence of packet loss. Source codes will be made available.
An Improved Variational Method for Image Denoising
Authors: Jing-En Huang, Jia-Wei Liao, Ku-Te Lin, Yu-Ju Tsai, Mei-Heng Yueh
Date: 2024-10-03 00:00:00
Tasks: Denoising, Image Denoising

The total variation (TV) method is an image denoising technique that aims to reduce noise by minimizing the total variation of the image, which measures the variation in pixel intensities. The TV method has been widely applied in image processing and computer vision for its ability to preserve edges and enhance image quality. In this paper, we propose an improved TV model for image denoising and the associated numerical algorithm to carry out the procedure, which is particularly effective in removing several types of noises and their combinations. Our improved model admits a unique solution and the associated numerical algorithm guarantees the convergence. Numerical experiments are demonstrated to show improved effectiveness and denoising quality compared to other TV models. Such encouraging results further enhance the utility of the TV method in image processing.
Combining Pre- and Post-Demosaicking Noise Removal for RAW Video
Authors: Marco Sánchez-Beeckman, Antoni Buades, Nicola Brandonisio, Bilel Kanoun
Date: 2024-10-03 00:00:00
Tasks: Demosaicking, Denoising

Denoising is one of the fundamental steps of the processing pipeline that converts data captured by a camera sensor into a display-ready image or video. It is generally performed early in the pipeline, usually before demosaicking, although studies swapping their order or even conducting them jointly have been proposed. With the advent of deep learning, the quality of denoising algorithms has steadily increased. Even so, modern neural networks still have a hard time adapting to new noise levels and scenes, which is indispensable for real-world applications. With those in mind, we propose a self-similarity-based denoising scheme that weights both a pre- and a post-demosaicking denoiser for Bayer-patterned CFA video data. We show that a balance between the two leads to better image quality, and we empirically find that higher noise levels benefit from a higher influence pre-demosaicking. We also integrate temporal trajectory prefiltering steps before each denoiser, which further improve texture reconstruction. The proposed method only requires an estimation of the noise model at the sensor, accurately adapts to any noise level, and is competitive with the state of the art, making it suitable for real-world videography.
SuperGS: Super-Resolution 3D Gaussian Splatting Enhanced by Variational Residual Features and Uncertainty-Augmented Learning
Authors: Shiyun Xie, Zhiru Wang, Xu Wang, Yinghao Zhu, Chengwei Pan, Xiwang Dong
Date: 2024-10-03 00:00:00
Tasks: 3DGS, Novel View Synthesis, Super-Resolution

Recently, 3D Gaussian Splatting (3DGS) has exceled in novel view synthesis (NVS) with its real-time rendering capabilities and superior quality. However, it faces challenges for high-resolution novel view synthesis (HRNVS) due to the coarse nature of primitives derived from low-resolution input views. To address this issue, we propose Super-Resolution 3DGS (SuperGS), which is an expansion of 3DGS designed with a two-stage coarse-to-fine training framework. In this framework, we use a latent feature field to represent the low-resolution scene, serving as both the initialization and foundational information for super-resolution optimization. Additionally, we introduce variational residual features to enhance high-resolution details, using their variance as uncertainty estimates to guide the densification process and loss computation. Furthermore, the introduction of a multi-view joint learning approach helps mitigate ambiguities caused by multi-view inconsistencies in the pseudo labels. Extensive experiments demonstrate that SuperGS surpasses state-of-the-art HRNVS methods on both real-world and synthetic datasets using only low-resolution inputs. Code is available at https://github.com/SYXieee/SuperGS.
MedVisionLlama: Leveraging Pre-Trained Large Language Model Layers to Enhance Medical Image Segmentation
Authors: Gurucharan Marthi Krishna Kumar, Aman Chadha, Janine Mendola, Amir Shmuel
Date: 2024-10-03 00:00:00
Tasks: Diagnostic, Image Segmentation, Language Modeling, Language Modelling, Large Language Model, Medical Image Segmentation, Segmentation, Semantic Segmentation

Large Language Models (LLMs), known for their versatility in textual data, are increasingly being explored for their potential to enhance medical image segmentation, a crucial task for accurate diagnostic imaging. This study explores enhancing Vision Transformers (ViTs) for medical image segmentation by integrating pre-trained LLM transformer blocks. Our approach, which incorporates a frozen LLM transformer block into the encoder of a ViT-based model, leads to substantial improvements in segmentation performance across various medical imaging modalities. We propose a Hybrid Attention Mechanism that combines global and local feature learning with a Multi-Scale Fusion Block for aggregating features across different scales. The enhanced model shows significant performance gains, including an average Dice score increase from 0.74 to 0.79 and improvements in accuracy, precision, and the Jaccard Index. These results demonstrate the effectiveness of LLM-based transformers in refining medical image segmentation, highlighting their potential to significantly boost model accuracy and robustness. The source code and our implementation are available at: https://bit.ly/3zf2CVs
Pseudo-Stereo Inputs: A Solution to the Occlusion Challenge in Self-Supervised Stereo Matching
Authors: Ruizhi Yang, Xingqiang Li, Jiajun Bai, Jinsong Du
Date: 2024-10-03 00:00:00
Tasks: Stereo Matching

Self-supervised stereo matching holds great promise for application and research due to its independence from expensive labeled data. However, direct self-supervised stereo matching paradigms based on photometric loss functions have consistently struggled with performance issues due to the occlusion challenge. The crux of the occlusion challenge lies in the fact that the positions of occluded pixels consistently align with the epipolar search direction defined by the input stereo images, leading to persistent information loss and erroneous feedback at fixed locations during self-supervised training. In this work, we propose a simple yet highly effective pseudo-stereo inputs strategy to address the core occlusion challenge. This strategy decouples the input and feedback images, compelling the network to probabilistically sample information from both sides of the occluding objects. As a result, the persistent lack of information in the aforementioned fixed occlusion areas is mitigated. Building upon this, we further address feedback conflicts and overfitting issues arising from the strategy. By integrating these components, our method achieves stable and significant performance improvements compared to existing methods. Quantitative experiments are conducted to evaluate the performance. Qualitative experiments further demonstrate accurate disparity inference even at occluded regions. These results demonstrate a significant advancement over previous methods in the field of direct self-supervised stereo matching based on photometric loss. The proposed pseudo-stereo inputs strategy, due to its simplicity and effectiveness, has the potential to serve as a new paradigm for direct self-supervised stereo matching. Code is available at https://github.com/qrzyang/Pseudo-Stereo.
A Foundation Model for the Solar Dynamics Observatory
Authors: James Walsh, Daniel G. Gass, Raul Ramos Pollan, Paul J. Wright, Richard Galvez, Noah Kasmanoff, Jason Naradowsky, Anne Spalding, James Parr, Atılım Güneş Baydin
Date: 2024-10-03 00:00:00
Tasks: model

SDO-FM is a foundation model using data from NASA's Solar Dynamics Observatory (SDO) spacecraft; integrating three separate instruments to encapsulate the Sun's complex physical interactions into a multi-modal embedding space. This model can be used to streamline scientific investigations involving SDO by making the enormous datasets more computationally accessible for heliophysics research and enable investigations that require instrument fusion. We discuss four key components: an ingestion pipeline to create machine learning ready datasets, the model architecture and training approach, resultant embeddings and fine-tunable models, and finally downstream fine-tuned applications. A key component of this effort has been to include subject matter specialists at each stage of development; reviewing the scientific value and providing guidance for model architecture, dataset, and training paradigm decisions. This paper marks release of our pretrained models and embedding datasets, available to the community on Hugging Face and sdofm.org.