pwc-archive/papers-with-abstracts huggingface dataset:
Knowledge-driven Subspace Fusion and Gradient Coordination for Multi-modal Learning
Authors: Yupei Zhang, Xiaofei Wang, Fangliangzi Meng, Jin Tang, Chao Li
Date: 2024-06-20 00:00:00
Tasks: Prognosis, Survival Analysis

Multi-modal learning plays a crucial role in cancer diagnosis and prognosis. Current deep learning based multi-modal approaches are often limited by their abilities to model the complex correlations between genomics and histology data, addressing the intrinsic complexity of tumour ecosystem where both tumour and microenvironment contribute to malignancy. We propose a biologically interpretative and robust multi-modal learning framework to efficiently integrate histology images and genomics by decomposing the feature subspace of histology images and genomics, reflecting distinct tumour and microenvironment features. To enhance cross-modal interactions, we design a knowledge-driven subspace fusion scheme, consisting of a cross-modal deformable attention module and a gene-guided consistency strategy. Additionally, in pursuit of dynamically optimizing the subspace knowledge, we further propose a novel gradient coordination learning strategy. Extensive experiments demonstrate the effectiveness of the proposed method, outperforming state-of-the-art techniques in three downstream tasks of glioma diagnosis, tumour grading, and survival analysis. Our code is available at https://github.com/helenypzhang/Subspace-Multimodal-Learning.
Similarity-aware Syncretic Latent Diffusion Model for Medical Image Translation with Representation Learning
Authors: Tingyi Lin, Pengju Lyu, Jie Zhang, Yuqing Wang, Cheng Wang, Jianjun Zhu
Date: 2024-06-20 00:00:00
Tasks: Diagnostic, Representation Learning, Translation

Non-contrast CT (NCCT) imaging may reduce image contrast and anatomical visibility, potentially increasing diagnostic uncertainty. In contrast, contrast-enhanced CT (CECT) facilitates the observation of regions of interest (ROI). Leading generative models, especially the conditional diffusion model, demonstrate remarkable capabilities in medical image modality transformation. Typical conditional diffusion models commonly generate images with guidance of segmentation labels for medical modal transformation. Limited access to authentic guidance and its low cardinality can pose challenges to the practical clinical application of conditional diffusion models. To achieve an equilibrium of generative quality and clinical practices, we propose a novel Syncretic generative model based on the latent diffusion model for medical image translation (S$^2$LDM), which can realize high-fidelity reconstruction without demand of additional condition during inference. S$^2$LDM enhances the similarity in distinct modal images via syncretic encoding and diffusing, promoting amalgamated information in the latent space and generating medical images with more details in contrast-enhanced regions. However, syncretic latent spaces in the frequency domain tend to favor lower frequencies, commonly locate in identical anatomic structures. Thus, S$^2$LDM applies adaptive similarity loss and dynamic similarity to guide the generation and supplements the shortfall in high-frequency details throughout the training process. Quantitative experiments confirm the effectiveness of our approach in medical image translation. Our code will release lately.
SSAD: Self-supervised Auxiliary Detection Framework for Panoramic X-ray based Dental Disease Diagnosis
Authors: Zijian Cai, Xinquan Yang, Xuguang Li, Xiaoling Luo, Xuechen Li, Linlin Shen, He Meng, Yongqiang Deng
Date: 2024-06-20 00:00:00
Tasks: object-detection, Object Detection, Self-Supervised Learning

Panoramic X-ray is a simple and effective tool for diagnosing dental diseases in clinical practice. When deep learning models are developed to assist dentist in interpreting panoramic X-rays, most of their performance suffers from the limited annotated data, which requires dentist's expertise and a lot of time cost. Although self-supervised learning (SSL) has been proposed to address this challenge, the two-stage process of pretraining and fine-tuning requires even more training time and computational resources. In this paper, we present a self-supervised auxiliary detection (SSAD) framework, which is plug-and-play and compatible with any detectors. It consists of a reconstruction branch and a detection branch. Both branches are trained simultaneously, sharing the same encoder, without the need for finetuning. The reconstruction branch learns to restore the tooth texture of healthy or diseased teeth, while the detection branch utilizes these learned features for diagnosis. To enhance the encoder's ability to capture fine-grained features, we incorporate the image encoder of SAM to construct a texture consistency (TC) loss, which extracts image embedding from the input and output of reconstruction branch, and then enforces both embedding into the same feature space. Extensive experiments on the public DENTEX dataset through three detection tasks demonstrate that the proposed SSAD framework achieves state-of-the-art performance compared to mainstream object detection methods and SSL methods. The code is available at https://github.com/Dylonsword/SSAD
Towards the in-situ Trunk Identification and Length Measurement of Sea Cucumbers via Bézier Curve Modelling
Authors: Shuaixin Liu, Kunqian Li, Yilin Ding, Kuangwei Xu, Qianli Jiang, Q. M. Jonathan Wu, Dalei Song
Date: 2024-06-20 00:00:00
Tasks: object-detection, Object Detection

We introduce a novel vision-based framework for in-situ trunk identification and length measurement of sea cucumbers, which plays a crucial role in the monitoring of marine ranching resources and mechanized harvesting. To model sea cucumber trunk curves with varying degrees of bending, we utilize the parametric B\'{e}zier curve due to its computational simplicity, stability, and extensive range of transformation possibilities. Then, we propose an end-to-end unified framework that combines parametric B\'{e}zier curve modeling with the widely used You-Only-Look-Once (YOLO) pipeline, abbreviated as TISC-Net, and incorporates effective funnel activation and efficient multi-scale attention modules to enhance curve feature perception and learning. Furthermore, we propose incorporating trunk endpoint loss as an additional constraint to effectively mitigate the impact of endpoint deviations on the overall curve. Finally, by utilizing the depth information of pixels located along the trunk curve captured by a binocular camera, we propose accurately estimating the in-situ length of sea cucumbers through space curve integration. We established two challenging benchmark datasets for curve-based in-situ sea cucumber trunk identification. These datasets consist of over 1,000 real-world marine environment images of sea cucumbers, accompanied by B\'{e}zier format annotations. We conduct evaluation on SC-ISTI, for which our method achieves mAP50 above 0.9 on both object detection and trunk identification tasks. Extensive length measurement experiments demonstrate that the average absolute relative error is around 0.15.
2nd Place Solution for MeViS Track in CVPR 2024 PVUW Workshop: Motion Expression guided Video Segmentation
Authors: Bin Cao, Yisi Zhang, Xuanxu Lin, Xingjian He, Bo Zhao, Jing Liu
Date: 2024-06-20 00:00:00
Tasks: Instance Segmentation, Referring Video Object Segmentation, Segmentation, Semantic Segmentation, Video Instance Segmentation, Video Object Segmentation, Video Segmentation, Video Semantic Segmentation

Motion Expression guided Video Segmentation is a challenging task that aims at segmenting objects in the video based on natural language expressions with motion descriptions. Unlike the previous referring video object segmentation (RVOS), this task focuses more on the motion in video content for language-guided video object segmentation, requiring an enhanced ability to model longer temporal, motion-oriented vision-language data. In this report, based on the RVOS methods, we successfully introduce mask information obtained from the video instance segmentation model as preliminary information for temporal enhancement and employ SAM for spatial refinement. Finally, our method achieved a score of 49.92 J &F in the validation phase and 54.20 J &F in the test phase, securing the final ranking of 2nd in the MeViS Track at the CVPR 2024 PVUW Challenge.
From Descriptive Richness to Bias: Unveiling the Dark Side of Generative Image Caption Enrichment
Authors: Yusuke Hirota, Ryo Hachiuma, Chao-Han Huck Yang, Yuta Nakashima
Date: 2024-06-20 00:00:00
Tasks: Descriptive, Hallucination

Large language models (LLMs) have enhanced the capacity of vision-language models to caption visual text. This generative approach to image caption enrichment further makes textual captions more descriptive, improving alignment with the visual context. However, while many studies focus on benefits of generative caption enrichment (GCE), are there any negative side effects? We compare standard-format captions and recent GCE processes from the perspectives of "gender bias" and "hallucination", showing that enriched captions suffer from increased gender bias and hallucination. Furthermore, models trained on these enriched captions amplify gender bias by an average of 30.9% and increase hallucination by 59.5%. This study serves as a caution against the trend of making captions more descriptive.
The Use of Multimodal Large Language Models to Detect Objects from Thermal Images: Transportation Applications
Authors: Huthaifa I. Ashqar, Taqwa I. Alhadidi, Mohammed Elhenawy, Nour O. Khanfar
Date: 2024-06-20 00:00:00
Tasks: Autonomous Driving

The integration of thermal imaging data with Multimodal Large Language Models (MLLMs) constitutes an exciting opportunity for improving the safety and functionality of autonomous driving systems and many Intelligent Transportation Systems (ITS) applications. This study investigates whether MLLMs can understand complex images from RGB and thermal cameras and detect objects directly. Our goals were to 1) assess the ability of the MLLM to learn from information from various sets, 2) detect objects and identify elements in thermal cameras, 3) determine whether two independent modality images show the same scene, and 4) learn all objects using different modalities. The findings showed that both GPT-4 and Gemini were effective in detecting and classifying objects in thermal images. Similarly, the Mean Absolute Percentage Error (MAPE) for pedestrian classification was 70.39% and 81.48%, respectively. Moreover, the MAPE for bike, car, and motorcycle detection were 78.4%, 55.81%, and 96.15%, respectively. Gemini produced MAPE of 66.53%, 59.35% and 78.18% respectively. This finding further demonstrates that MLLM can identify thermal images and can be employed in advanced imaging automation technologies for ITS applications.
SMORE: Simultaneous Map and Object REconstruction
Authors: Nathaniel Chodosh, Anish Madan, Simon Lucey, Deva Ramanan
Date: 2024-06-19 00:00:00
Tasks: Depth Completion, Dynamic Reconstruction, global-optimization, Motion Compensation, Novel View Synthesis, Object, Object Reconstruction, Surface Reconstruction

We present a method for dynamic surface reconstruction of large-scale urban scenes from LiDAR. Depth-based reconstructions tend to focus on small-scale objects or large-scale SLAM reconstructions that treat moving objects as outliers. We take a holistic perspective and optimize a compositional model of a dynamic scene that decomposes the world into rigidly-moving objects and the background. To achieve this, we take inspiration from recent novel view synthesis methods and frame the reconstruction problem as a global optimization over neural surfaces, ego poses, and object poses, which minimizes the error between composed spacetime surfaces and input LiDAR scans. In contrast to view synthesis methods, which typically minimize 2D errors with gradient descent, we minimize a 3D point-to-surface error by coordinate descent, which we decompose into registration and surface reconstruction steps. Each step can be handled well by off-the-shelf methods without any re-training. We analyze the surface reconstruction step for rolling-shutter LiDARs, and show that deskewing operations common in continuous time SLAM can be applied to dynamic objects as well, improving results over prior art by an order of magnitude. Beyond pursuing dynamic reconstruction as a goal in and of itself, we propose that such a system can be used to auto-label partially annotated sequences and produce ground truth annotation for hard-to-label problems such as depth completion and scene flow. Please see https://anishmadan23.github.io/smore/ for more visual results.
Using Multimodal Large Language Models for Automated Detection of Traffic Safety Critical Events
Authors: Mohammad Abu Tami, Huthaifa I. Ashqar, Mohammed Elhenawy
Date: 2024-06-19 00:00:00
Tasks: Few-Shot Learning, Zero-Shot Learning

Traditional approaches to safety event analysis in autonomous systems have relied on complex machine learning models and extensive datasets for high accuracy and reliability. However, the advent of Multimodal Large Language Models (MLLMs) offers a novel approach by integrating textual, visual, and audio modalities, thereby providing automated analyses of driving videos. Our framework leverages the reasoning power of MLLMs, directing their output through context-specific prompts to ensure accurate, reliable, and actionable insights for hazard detection. By incorporating models like Gemini-Pro-Vision 1.5 and Llava, our methodology aims to automate the safety critical events and mitigate common issues such as hallucinations in MLLM outputs. Preliminary results demonstrate the framework's potential in zero-shot learning and accurate scenario analysis, though further validation on larger datasets is necessary. Furthermore, more investigations are required to explore the performance enhancements of the proposed framework through few-shot learning and fine-tuned models. This research underscores the significance of MLLMs in advancing the analysis of the naturalistic driving videos by improving safety-critical event detecting and understanding the interaction with complex environments.
WATT: Weight Average Test-Time Adaptation of CLIP
Authors: David Osowiechi, Mehrdad Noori, Gustavo Adolfo Vargas Hakim, Moslem Yazdanpanah, Ali Bahri, Milad Cheraghalikhani, Sahar Dastani, Farzad Beizaee, Ismail Ben Ayed, Christian Desrosiers
Date: 2024-06-19 00:00:00
Tasks: image-classification, Image Classification, Overall - Test, Test-time Adaptation, Zero-Shot Image Classification

Vision-Language Models (VLMs) such as CLIP have yielded unprecedented performance for zero-shot image classification, yet their generalization capability may still be seriously challenged when confronted to domain shifts. In response, we present Weight Average Test-Time Adaptation (WATT) of CLIP, a pioneering approach facilitating full test-time adaptation (TTA) of this VLM. Our method employs a diverse set of templates for text prompts, augmenting the existing framework of CLIP. Predictions are utilized as pseudo labels for model updates, followed by weight averaging to consolidate the learned information globally. Furthermore, we introduce a text ensemble strategy, enhancing overall test performance by aggregating diverse textual cues. Our findings underscore the efficacy of WATT in enhancing performance across diverse datasets, including CIFAR-10-C, CIFAR-10.1, CIFAR-100-C, VisDA-C, and several other challenging datasets, effectively covering a wide range of domain shifts. Notably, these enhancements are achieved without necessitating additional model transformations or trainable modules. Moreover, compared to other Test-Time Adaptation methods, our approach can operate effectively with just a single image. Highlighting the potential of innovative test-time strategies, this research emphasizes their role in fortifying the adaptability of VLMs. The implementation is available at: \url{https://github.com/Mehrdad-Noori/WATT.git}.
Splatter a Video: Video Gaussian Representation for Versatile Processing
Authors: Yang-tian Sun, Yi-Hua Huang, Lin Ma, Xiaoyang Lyu, Yan-Pei Cao, Xiaojuan Qi
Date: 2024-06-19 00:00:00
Tasks: Depth Estimation, Depth Prediction, Optical Flow Estimation, Video Generation

Video representation is a long-standing problem that is crucial for various down-stream tasks, such as tracking,depth prediction,segmentation,view synthesis,and editing. However, current methods either struggle to model complex motions due to the absence of 3D structure or rely on implicit 3D representations that are ill-suited for manipulation tasks. To address these challenges, we introduce a novel explicit 3D representation-video Gaussian representation -- that embeds a video into 3D Gaussians. Our proposed representation models video appearance in a 3D canonical space using explicit Gaussians as proxies and associates each Gaussian with 3D motions for video motion. This approach offers a more intrinsic and explicit representation than layered atlas or volumetric pixel matrices. To obtain such a representation, we distill 2D priors, such as optical flow and depth, from foundation models to regularize learning in this ill-posed setting. Extensive applications demonstrate the versatility of our new video representation. It has been proven effective in numerous video processing tasks, including tracking, consistent video depth and feature refinement, motion and appearance editing, and stereoscopic video generation. Project page: https://sunyangtian.github.io/spatter_a_video_web/
Liveness Detection in Computer Vision: Transformer-based Self-Supervised Learning for Face Anti-Spoofing
Authors: Arman Keresh, Pakizar Shamoi
Date: 2024-06-19 00:00:00
Tasks: Face Anti-Spoofing, Face Recognition, Self-Supervised Learning

Face recognition systems are increasingly used in biometric security for convenience and effectiveness. However, they remain vulnerable to spoofing attacks, where attackers use photos, videos, or masks to impersonate legitimate users. This research addresses these vulnerabilities by exploring the Vision Transformer (ViT) architecture, fine-tuned with the DINO framework. The DINO framework facilitates self-supervised learning, enabling the model to learn distinguishing features from unlabeled data. We compared the performance of the proposed fine-tuned ViT model using the DINO framework against a traditional CNN model, EfficientNet b2, on the face anti-spoofing task. Numerous tests on standard datasets show that the ViT model performs better than the CNN model in terms of accuracy and resistance to different spoofing methods. Additionally, we collected our own dataset from a biometric application to validate our findings further. This study highlights the superior performance of transformer-based architecture in identifying complex spoofing cues, leading to significant advancements in biometric security.
Locating and measuring marine aquaculture production from space: a computer vision approach in the French Mediterranean
Authors: Sebastian Quaade, Andrea Vallebueno, Olivia D. N. Alcabes, Kit T. Rodolfa, Daniel E. Ho
Date: 2024-06-19 00:00:00

Aquaculture production -- the cultivation of aquatic plants and animals -- has grown rapidly since the 1990s, but sparse, self-reported and aggregate production data limits the effective understanding and monitoring of the industry's trends and potential risks. Building on a manual survey of aquaculture production from remote sensing imagery, we train a computer vision model to identify marine aquaculture cages from aerial and satellite imagery, and generate a spatially explicit dataset of finfish production locations in the French Mediterranean from 2000-2021 that includes 4,010 cages (69m2 average cage area). We demonstrate the value of our method as an easily adaptable, cost-effective approach that can improve the speed and reliability of aquaculture surveys, and enables downstream analyses relevant to researchers and regulators. We illustrate its use to compute independent estimates of production, and develop a flexible framework to quantify uncertainty in these estimates. Overall, our study presents an efficient, scalable and highly adaptable method for monitoring aquaculture production from remote sensing imagery.
IG-CFAT: An Improved GAN-Based Framework for Effectively Exploiting Transformers in Real-World Image Super-Resolution
Authors: Alireza Aghelan, Ali Amiryan, Abolfazl Zarghani, Modjtaba Rouhani
Date: 2024-06-19 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

In the field of single image super-resolution (SISR), transformer-based models, have demonstrated significant advancements. However, the potential and efficiency of these models in applied fields such as real-world image super-resolution have been less noticed and there are substantial opportunities for improvement. Recently, composite fusion attention transformer (CFAT), outperformed previous state-of-the-art (SOTA) models in classic image super-resolution. In this paper, we propose a novel GAN-based framework by incorporating the CFAT model to effectively exploit the performance of transformers in real-world image super-resolution. In our proposed approach, we integrate a semantic-aware discriminator to reconstruct fine details more accurately and employ an adaptive degradation model to better simulate real-world degradations. Moreover, we introduce a new combination of loss functions by adding wavelet loss to loss functions of GAN-based models to better recover high-frequency details. Empirical results demonstrate that IG-CFAT significantly outperforms existing SOTA models in both quantitative and qualitative metrics. Our proposed model revolutionizes the field of real-world image super-resolution and demonstrates substantially better performance in recovering fine details and generating realistic textures. The introduction of IG-CFAT offers a robust and adaptable solution for real-world image super-resolution tasks.
Towards Holistic Language-video Representation: the language model-enhanced MSR-Video to Text Dataset
Authors: Yuchen Yang, Yingxuan Duan
Date: 2024-06-19 00:00:00
Tasks: Language Modeling, Language Modelling, Representation Learning, Retrieval, Video Captioning, Video Retrieval, Video Understanding

A more robust and holistic language-video representation is the key to pushing video understanding forward. Despite the improvement in training strategies, the quality of the language-video dataset is less attention to. The current plain and simple text descriptions and the visual-only focus for the language-video tasks result in a limited capacity in real-world natural language video retrieval tasks where queries are much more complex. This paper introduces a method to automatically enhance video-language datasets, making them more modality and context-aware for more sophisticated representation learning needs, hence helping all downstream tasks. Our multifaceted video captioning method captures entities, actions, speech transcripts, aesthetics, and emotional cues, providing detailed and correlating information from the text side to the video side for training. We also develop an agent-like strategy using language models to generate high-quality, factual textual descriptions, reducing human intervention and enabling scalability. The method's effectiveness in improving language-video representation is evaluated through text-video retrieval using the MSR-VTT dataset and several multi-modal retrieval models.