pwc-archive/papers-with-abstracts huggingface dataset:
Accelerating Multimodal Large Language Models by Searching Optimal Vision Token Reduction
Authors: Shiyu Zhao, Zhenting Wang, Felix Juefei-Xu, Xide Xia, Miao Liu, Xiaofang Wang, Mingfu Liang, Ning Zhang, Dimitris N. Metaxas, Licheng Yu
Date: 2024-11-30 00:00:00
Proceeding: CVPR 2025 1
Tasks: Bayesian Optimization, Token Reduction

Prevailing Multimodal Large Language Models (MLLMs) encode the input image(s) as vision tokens and feed them into the language backbone, similar to how Large Language Models (LLMs) process the text tokens. However, the number of vision tokens increases quadratically as the image resolutions, leading to huge computational costs. In this paper, we consider improving MLLM's efficiency from two scenarios, (I) Reducing computational cost without degrading the performance. (II) Improving the performance with given budgets. We start with our main finding that the ranking of each vision token sorted by attention scores is similar in each layer except the first layer. Based on it, we assume that the number of essential top vision tokens does not increase along layers. Accordingly, for Scenario I, we propose a greedy search algorithm (G-Search) to find the least number of vision tokens to keep at each layer from the shallow to the deep. Interestingly, G-Search is able to reach the optimal reduction strategy based on our assumption. For Scenario II, based on the reduction strategy from G-Search, we design a parametric sigmoid function (P-Sigmoid) to guide the reduction at each layer of the MLLM, whose parameters are optimized by Bayesian Optimization. Extensive experiments demonstrate that our approach can significantly accelerate those popular MLLMs, e.g. LLaVA, and InternVL2 models, by more than $2 \times$ without performance drops. Our approach also far outperforms other token reduction methods when budgets are limited, achieving a better trade-off between efficiency and effectiveness.
SizeGS: Size-aware Compression of 3D Gaussians with Hierarchical Mixed Precision Quantization
Authors: Shuzhao Xie, Jiahang Liu, Weixiang Zhang, Shijia Ge, Sicheng Pan, Chen Tang, Yunpeng Bai, Zhi Wang
Date: 2024-12-08 00:00:00
Tasks: 3DGS, Attribute, Combinatorial Optimization, Quantization

Effective compression technology is crucial for 3DGS to adapt to varying storage and transmission conditions. However, existing methods fail to address size constraints while maintaining optimal quality. In this paper, we introduce SizeGS, a framework that compresses 3DGS within a specified size budget while optimizing visual quality. We start with a size estimator to establish a clear relationship between file size and hyperparameters. Leveraging this estimator, we incorporate mixed precision quantization (MPQ) into 3DGS attributes, structuring MPQ in two hierarchical level -- inter-attribute and intra-attribute -- to optimize visual quality under the size constraint. At the inter-attribute level, we assign bit-widths to each attribute channel by formulating the combinatorial optimization as a 0-1 integer linear program, which can be efficiently solved. At the intra-attribute level, we divide each attribute channel into blocks of vectors, quantizing each vector based on the optimal bit-width derived at the inter-attribute level. Dynamic programming determines block lengths. Using the size estimator and MPQ, we develop a calibrated algorithm to identify optimal hyperparameters in just 10 minutes, achieving a 1.69$\times$ efficiency increase with quality comparable to state-of-the-art methods.
Prism: Semi-Supervised Multi-View Stereo with Monocular Structure Priors
Authors: Alex Rich, Noah Stier, Pradeep Sen, Tobias Höllerer
Date: 2024-12-08 00:00:00

The promise of unsupervised multi-view-stereo (MVS) is to leverage large unlabeled datasets, yet current methods underperform when training on difficult data, such as handheld smartphone videos of indoor scenes. Meanwhile, high-quality synthetic datasets are available but MVS networks trained on these datasets fail to generalize to real-world examples. To bridge this gap, we propose a semi-supervised learning framework that allows us to train on real and rendered images jointly, capturing structural priors from synthetic data while ensuring parity with the real-world domain. Central to our framework is a novel set of losses that leverages powerful existing monocular relative-depth estimators trained on the synthetic dataset, transferring the rich structure of this relative depth to the MVS predictions on unlabeled data. Inspired by perceptual image metrics, we compare the MVS and monocular predictions via a deep feature loss and a multi-scale statistical loss. Our full framework, which we call Prism, achieves large quantitative and qualitative improvements over current unsupervised and synthetic-supervised MVS networks. This is a best-case-scenario result, opening the door to using both unlabeled smartphone videos and photorealistic synthetic datasets for training MVS networks.
Emulating Clinical Quality Muscle B-mode Ultrasound Images from Plane Wave Images Using a Two-Stage Machine Learning Model
Authors: Reed Chen, Courtney Trutna Paley, Wren Wightman, Lisa Hobson-Webb, Yohei Harada, Felix Jin, Ouwen Huang, Mark Palmeri, Kathryn Nightingale
Date: 2024-12-07 00:00:00

Research ultrasound scanners such as the Verasonics Vantage often lack the advanced image processing algorithms used by clinical systems. Image quality is even lower in plane wave imaging - often used for shear wave elasticity imaging (SWEI) - which sacrifices spatial resolution for temporal resolution. As a result, delay-and-summed images acquired from SWEI have limited interpretability. In this project, a two-stage machine learning model was trained to enhance single plane wave images of muscle acquired with a Verasonics Vantage system. The first stage of the model consists of a U-Net trained to emulate plane wave compounding, histogram matching, and unsharp masking using paired images. The second stage consists of a CycleGAN trained to emulate clinical muscle B-modes using unpaired images. This two-stage model was implemented on the Verasonics Vantage research ultrasound scanner, and its ability to provide high-speed image formation at a frame rate of 28.5 +/- 0.6 FPS from a single plane wave transmit was demonstrated. A reader study with two physicians demonstrated that these processed images had significantly greater structural fidelity and less speckle than the original plane wave images.
Compositional Image Retrieval via Instruction-Aware Contrastive Learning
Authors: Wenliang Zhong, Weizhi An, Feng Jiang, Hehuan Ma, Yuzhi Guo, Junzhou Huang
Date: 2024-12-07 00:00:00
Tasks: Contrastive Learning, Image Retrieval, Instruction Following, Retrieval, Text Generation, Triplet

Composed Image Retrieval (CIR) involves retrieving a target image based on a composed query of an image paired with text that specifies modifications or changes to the visual reference. CIR is inherently an instruction-following task, as the model needs to interpret and apply modifications to the image. In practice, due to the scarcity of annotated data in downstream tasks, Zero-Shot CIR (ZS-CIR) is desirable. While existing ZS-CIR models based on CLIP have shown promising results, their capability in interpreting and following modification instructions remains limited. Some research attempts to address this by incorporating Large Language Models (LLMs). However, these approaches still face challenges in effectively integrating multimodal information and instruction understanding. To tackle above challenges, we propose a novel embedding method utilizing an instruction-tuned Multimodal LLM (MLLM) to generate composed representation, which significantly enhance the instruction following capability for a comprehensive integration between images and instructions. Nevertheless, directly applying MLLMs introduces a new challenge since MLLMs are primarily designed for text generation rather than embedding extraction as required in CIR. To address this, we introduce a two-stage training strategy to efficiently learn a joint multimodal embedding space and further refining the ability to follow modification instructions by tuning the model in a triplet dataset similar to the CIR format. Extensive experiments on four public datasets: FashionIQ, CIRR, GeneCIS, and CIRCO demonstrates the superior performance of our model, outperforming state-of-the-art baselines by a significant margin. Codes are available at the GitHub repository.
Integrating YOLO11 and Convolution Block Attention Module for Multi-Season Segmentation of Tree Trunks and Branches in Commercial Apple Orchards
Authors: Ranjan Sapkota, Manoj Karkee
Date: 2024-12-07 00:00:00
Tasks: Instance Segmentation, Semantic Segmentation

In this study, we developed a customized instance segmentation model by integrating the Convolutional Block Attention Module (CBAM) with the YOLO11 architecture. This model, trained on a mixed dataset of dormant and canopy season apple orchard images, aimed to enhance the segmentation of tree trunks and branches under varying seasonal conditions throughout the year. The model was individually validated across dormant and canopy season images after training the YOLO11-CBAM on the mixed dataset collected over the two seasons. Additional testing of the model during pre-bloom, flower bloom, fruit thinning, and harvest season was performed. The highest recall and precision metrics were observed in the YOLO11x-seg-CBAM and YOLO11m-seg-CBAM respectively. Particularly, YOLO11m-seg with CBAM showed the highest precision of 0.83 as performed for the Trunk class in training, while without the CBAM, YOLO11m-seg achieved 0.80 precision score for the Trunk class. Likewise, for branch class, YOLO11m-seg with CBAM achieved the highest precision score value of 0.75 while without the CBAM, the YOLO11m-seg achieved a precision of 0.73. For dormant season validation, YOLO11x-seg exhibited the highest precision at 0.91. Canopy season validation highlighted YOLO11s-seg with superior precision across all classes, achieving 0.516 for Branch, and 0.64 for Trunk. The modeling approach, trained on two season datasets as dormant and canopy season images, demonstrated the potential of the YOLO11-CBAM integration to effectively detect and segment tree trunks and branches year-round across all seasonal variations. Keywords: YOLOv11, YOLOv11 Tree Detection, YOLOv11 Branch Detection and Segmentation, Machine Vision, Deep Learning, Machine Learning
Evaluating Hallucination in Text-to-Image Diffusion Models with Scene-Graph based Question-Answering Agent
Authors: Ziyuan Qin, Dongjie Cheng, Haoyu Wang, Huahui Yi, Yuting Shao, Zhiyuan Fan, Kang Li, Qicheng Lao
Date: 2024-12-07 00:00:00
Tasks: Hallucination, Question Answering

Contemporary Text-to-Image (T2I) models frequently depend on qualitative human evaluations to assess the consistency between synthesized images and the text prompts. There is a demand for quantitative and automatic evaluation tools, given that human evaluation lacks reproducibility. We believe that an effective T2I evaluation metric should accomplish the following: detect instances where the generated images do not align with the textual prompts, a discrepancy we define as the `hallucination problem' in T2I tasks; record the types and frequency of hallucination issues, aiding users in understanding the causes of errors; and provide a comprehensive and intuitive scoring that close to human standard. To achieve these objectives, we propose a method based on large language models (LLMs) for conducting question-answering with an extracted scene-graph and created a dataset with human-rated scores for generated images. From the methodology perspective, we combine knowledge-enhanced question-answering tasks with image evaluation tasks, making the evaluation metrics more controllable and easier to interpret. For the contribution on the dataset side, we generated 12,000 synthesized images based on 1,000 composited prompts using three advanced T2I models. Subsequently, we conduct human scoring on all synthesized images and prompt pairs to validate the accuracy and effectiveness of our method as an evaluation metric. All generated images and the human-labeled scores will be made publicly available in the future to facilitate ongoing research on this crucial issue. Extensive experiments show that our method aligns more closely with human scoring patterns than other evaluation metrics.
Impact of Sunglasses on One-to-Many Facial Identification Accuracy
Authors: Sicong Tian, Haiyu Wu, Michael C. King, Kevin W. Bowyer
Date: 2024-12-07 00:00:00

One-to-many facial identification is documented to achieve high accuracy in the case where both the probe and the gallery are "mugshot quality" images. However, an increasing number of documented instances of wrongful arrest following one-to-many facial identification have raised questions about its accuracy. Probe images used in one-to-many facial identification are often cropped from frames of surveillance video and deviate from "mugshot quality" in various ways. This paper systematically explores how the accuracy of one-to-many facial identification is degraded by the person in the probe image choosing to wear dark sunglasses. We show that sunglasses degrade accuracy for mugshot-quality images by an amount similar to strong blur or noticeably lower resolution. Further, we demonstrate that the combination of sunglasses with blur or lower resolution results in even more pronounced loss in accuracy. These results have important implications for developing objective criteria to qualify a probe image for the level of accuracy to be expected if it used for one-to-many identification. To ameliorate the accuracy degradation caused by dark sunglasses, we show that it is possible to recover about 38% of the lost accuracy by synthetically adding sunglasses to all the gallery images, without model re-training. We also show that the frequency of wearing-sunglasses images is very low in existing training sets, and that increasing the representation of wearing-sunglasses images can greatly reduce the error rate. The image set assembled for this research is available at https://cvrl.nd.edu/projects/data/ to support replication and further research.
Segment-Level Road Obstacle Detection Using Visual Foundation Model Priors and Likelihood Ratios
Authors: Youssef Shoeb, Nazir Nayal, Azarm Nowzard, Fatma Güney, Hanno Gottschalk
Date: 2024-12-07 00:00:00
Tasks: Autonomous Vehicles, Navigate

Detecting road obstacles is essential for autonomous vehicles to navigate dynamic and complex traffic environments safely. Current road obstacle detection methods typically assign a score to each pixel and apply a threshold to generate final predictions. However, selecting an appropriate threshold is challenging, and the per-pixel classification approach often leads to fragmented predictions with numerous false positives. In this work, we propose a novel method that leverages segment-level features from visual foundation models and likelihood ratios to predict road obstacles directly. By focusing on segments rather than individual pixels, our approach enhances detection accuracy, reduces false positives, and offers increased robustness to scene variability. We benchmark our approach against existing methods on the RoadObstacle and LostAndFound datasets, achieving state-of-the-art performance without needing a predefined threshold.
Temporally Compressed 3D Gaussian Splatting for Dynamic Scenes
Authors: Saqib Javed, Ahmad Jarrar Khan, Corentin Dumery, Chen Zhao, Mathieu Salzmann
Date: 2024-12-07 00:00:00
Tasks: Quantization

Recent advancements in high-fidelity dynamic scene reconstruction have leveraged dynamic 3D Gaussians and 4D Gaussian Splatting for realistic scene representation. However, to make these methods viable for real-time applications such as AR/VR, gaming, and rendering on low-power devices, substantial reductions in memory usage and improvements in rendering efficiency are required. While many state-of-the-art methods prioritize lightweight implementations, they struggle in handling scenes with complex motions or long sequences. In this work, we introduce Temporally Compressed 3D Gaussian Splatting (TC3DGS), a novel technique designed specifically to effectively compress dynamic 3D Gaussian representations. TC3DGS selectively prunes Gaussians based on their temporal relevance and employs gradient-aware mixed-precision quantization to dynamically compress Gaussian parameters. It additionally relies on a variation of the Ramer-Douglas-Peucker algorithm in a post-processing step to further reduce storage by interpolating Gaussian trajectories across frames. Our experiments across multiple datasets demonstrate that TC3DGS achieves up to 67$\times$ compression with minimal or no degradation in visual quality.
Jointly RS Image Deblurring and Super-Resolution with Adjustable-Kernel and Multi-Domain Attention
Authors: Yan Zhang, Pengcheng Zheng, Chengxiao Zeng, Bin Xiao, Zhenghao Li, Xinbo Gao
Date: 2024-12-07 00:00:00
Tasks: Deblurring, Image Deblurring, Super-Resolution

Remote Sensing (RS) image deblurring and Super-Resolution (SR) are common tasks in computer vision that aim at restoring RS image detail and spatial scale, respectively. However, real-world RS images often suffer from a complex combination of global low-resolution (LR) degeneration and local blurring degeneration. Although carefully designed deblurring and SR models perform well on these two tasks individually, a unified model that performs jointly RS image deblurring and super-resolution (JRSIDSR) task is still challenging due to the vital dilemma of reconstructing the global and local degeneration simultaneously. Additionally, existing methods struggle to capture the interrelationship between deblurring and SR processes, leading to suboptimal results. To tackle these issues, we give a unified theoretical analysis of RS images' spatial and blur degeneration processes and propose a dual-branch parallel network named AKMD-Net for the JRSIDSR task. AKMD-Net consists of two main branches: deblurring and super-resolution branches. In the deblurring branch, we design a pixel-adjustable kernel block (PAKB) to estimate the local and spatial-varying blur kernels. In the SR branch, a multi-domain attention block (MDAB) is proposed to capture the global contextual information enhanced with high-frequency details. Furthermore, we develop an adaptive feature fusion (AFF) module to model the contextual relationships between the deblurring and SR branches. Finally, we design an adaptive Wiener loss (AW Loss) to depress the prior noise in the reconstructed images. Extensive experiments demonstrate that the proposed AKMD-Net achieves state-of-the-art (SOTA) quantitative and qualitative performance on commonly used RS image datasets. The source code is publicly available at https://github.com/zpc456/AKMD-Net.
RSUniVLM: A Unified Vision Language Model for Remote Sensing via Granularity-oriented Mixture of Experts
Authors: Xu Liu, Zhouhui Lian
Date: 2024-12-07 00:00:00
Tasks: Change Detection, Image Comprehension, Instruction Following, Language Modeling, Language Modelling, Mixture-of-Experts, Object Localization, Question Answering, Semantic Segmentation, Visual Question Answering

Remote Sensing Vision-Language Models (RS VLMs) have made much progress in the tasks of remote sensing (RS) image comprehension. While performing well in multi-modal reasoning and multi-turn conversations, the existing models lack pixel-level understanding and struggle with multi-image inputs. In this work, we propose RSUniVLM, a unified, end-to-end RS VLM designed for comprehensive vision understanding across multiple granularity, including image-level, region-level, and pixel-level tasks. RSUniVLM also performs effectively in multi-image analysis, with instances of change detection and change captioning. To enhance the model's ability to capture visual information at different levels without increasing model size, we design a novel architecture called Granularity-oriented Mixture of Experts to constraint the model to about 1 billion parameters. We also construct a large-scale RS instruction-following dataset based on a variety of existing datasets in both RS and general domain, encompassing various tasks such as object localization, visual question answering, and semantic segmentation. Substantial experiments have been conducted to validate the superiority of the proposed RSUniVLM up to state-of-the-art across various RS tasks. Code and model will be available at \href{https://github.com/xuliu-cyber/RSUniVLM}{here}.
Nearly Solved? Robust Deepfake Detection Requires More than Visual Forensics
Authors: Guy Levy, Nathan Liebmann
Date: 2024-12-07 00:00:00
Tasks: Adversarial Robustness, DeepFake Detection, Face Swapping

Deepfakes are on the rise, with increased sophistication and prevalence allowing for high-profile social engineering attacks. Detecting them in the wild is therefore important as ever, giving rise to new approaches breaking benchmark records in this task. In line with previous work, we show that recently developed state-of-the-art detectors are susceptible to classical adversarial attacks, even in a highly-realistic black-box setting, putting their usability in question. We argue that crucial 'robust features' of deepfakes are in their higher semantics, and follow that with evidence that a detector based on a semantic embedding model is less susceptible to black-box perturbation attacks. We show that large visuo-lingual models like GPT-4o can perform zero-shot deepfake detection better than current state-of-the-art methods, and introduce a novel attack based on high-level semantic manipulation. Finally, we argue that hybridising low- and high-level detectors can improve adversarial robustness, based on their complementary strengths and weaknesses.
Multimodal Biometric Authentication Using Camera-Based PPG and Fingerprint Fusion
Authors: Xue Xian Zheng, M. M. Ur Rahma, Bilal Taha, Mudassir Masood, Dimitrios Hatzinakos, Tareq Al-Naffouri
Date: 2024-12-07 00:00:00
Tasks: Photoplethysmography (PPG)

Camera-based photoplethysmography (PPG) obtained from smartphones has shown great promise for personalized healthcare and secure authentication. This paper presents a multimodal biometric system that integrates PPG signals extracted from videos with fingerprint data to enhance the accuracy of user verification. The system requires users to place their fingertip on the camera lens for a few seconds, allowing the capture and processing of unique biometric characteristics. Our approach employs a neural network with two structured state-space model (SSM) encoders to manage the distinct modalities. Fingerprint images are transformed into pixel sequences, and along with segmented PPG waveforms, they are input into the encoders. A cross-modal attention mechanism then extracts refined feature representations, and a distribution-oriented contrastive loss function aligns these features within a unified latent space. Experimental results demonstrate the system's superior performance across various evaluation metrics in both single-session and dual-session authentication scenarios.