pwc-archive/papers-with-abstracts huggingface dataset:
CACE-Net: Co-guidance Attention and Contrastive Enhancement for Effective Audio-Visual Event Localization
Authors: Xiang He, Xiangxi Liu, Yang Li, Dongcheng Zhao, Guobin Shen, Qingqun Kong, Xin Yang, Yi Zeng
Date: 2024-08-04 00:00:00
Tasks: audio-visual event localization

The audio-visual event localization task requires identifying concurrent visual and auditory events from unconstrained videos within a network model, locating them, and classifying their category. The efficient extraction and integration of audio and visual modal information have always been challenging in this field. In this paper, we introduce CACE-Net, which differs from most existing methods that solely use audio signals to guide visual information. We propose an audio-visual co-guidance attention mechanism that allows for adaptive bi-directional cross-modal attentional guidance between audio and visual information, thus reducing inconsistencies between modalities. Moreover, we have observed that existing methods have difficulty distinguishing between similar background and event and lack the fine-grained features for event classification. Consequently, we employ background-event contrast enhancement to increase the discrimination of fused feature and fine-tuned pre-trained model to extract more refined and discernible features from complex multimodal inputs. Specifically, we have enhanced the model's ability to discern subtle differences between event and background and improved the accuracy of event classification in our model. Experiments on the AVE dataset demonstrate that CACE-Net sets a new benchmark in the audio-visual event localization task, proving the effectiveness of our proposed methods in handling complex multimodal learning and event localization in unconstrained videos. Code is available at https://github.com/Brain-Cog-Lab/CACE-Net.
Single-Point Supervised High-Resolution Dynamic Network for Infrared Small Target Detection
Authors: Jing Wu, Rixiang Ni, Feng Huang, Zhaobing Qiu, Liqiong Chen, Changhai Luo, Yunxiang Li, Youli Li
Date: 2024-08-04 00:00:00

Infrared small target detection (IRSTD) tasks are extremely challenging for two main reasons: 1) it is difficult to obtain accurate labelling information that is critical to existing methods, and 2) infrared (IR) small target information is easily lost in deep networks. To address these issues, we propose a single-point supervised high-resolution dynamic network (SSHD-Net). In contrast to existing methods, we achieve state-of-the-art (SOTA) detection performance using only single-point supervision. Specifically, we first design a high-resolution cross-feature extraction module (HCEM), that achieves bi-directional feature interaction through stepped feature cascade channels (SFCC). It balances network depth and feature resolution to maintain deep IR small-target information. Secondly, the effective integration of global and local features is achieved through the dynamic coordinate fusion module (DCFM), which enhances the anti-interference ability in complex backgrounds. In addition, we introduce the high-resolution multilevel residual module (HMRM) to enhance the semantic information extraction capability. Finally, we design the adaptive target localization detection head (ATLDH) to improve detection accuracy. Experiments on the publicly available datasets NUDT-SIRST and IRSTD-1k demonstrate the effectiveness of our method. Compared to other SOTA methods, our method can achieve better detection performance with only a single point of supervision.
Label Augmentation for Neural Networks Robustness
Authors: Fatemeh Amerehi, Patrick Healy
Date: 2024-08-04 00:00:00
Tasks: Adversarial Robustness, Data Augmentation, Out-of-Distribution Generalization

Out-of-distribution generalization can be categorized into two types: common perturbations arising from natural variations in the real world and adversarial perturbations that are intentionally crafted to deceive neural networks. While deep neural networks excel in accuracy under the assumption of identical distributions between training and test data, they often encounter out-of-distribution scenarios resulting in a significant decline in accuracy. Data augmentation methods can effectively enhance robustness against common corruptions, but they typically fall short in improving robustness against adversarial perturbations. In this study, we develop Label Augmentation (LA), which enhances robustness against both common and intentional perturbations and improves uncertainty estimation. Our findings indicate a Clean error rate improvement of up to 23.29% when employing LA in comparisons to the baseline. Additionally, it enhances robustness under common corruptions benchmark by up to 24.23%. When tested against FGSM and PGD attacks, improvements in adversarial robustness are noticeable, with enhancements of up to 53.18% for FGSM and 24.46% for PGD attacks.
AdvQDet: Detecting Query-Based Adversarial Attacks with Adversarial Contrastive Prompt Tuning
Authors: Xin Wang, Kai Chen, Xingjun Ma, Zhineng Chen, Jingjing Chen, Yu-Gang Jiang
Date: 2024-08-04 00:00:00

Deep neural networks (DNNs) are known to be vulnerable to adversarial attacks even under a black-box setting where the adversary can only query the model. Particularly, query-based black-box adversarial attacks estimate adversarial gradients based on the returned probability vectors of the target model for a sequence of queries. During this process, the queries made to the target model are intermediate adversarial examples crafted at the previous attack step, which share high similarities in the pixel space. Motivated by this observation, stateful detection methods have been proposed to detect and reject query-based attacks. While demonstrating promising results, these methods either have been evaded by more advanced attacks or suffer from low efficiency in terms of the number of shots (queries) required to detect different attacks. Arguably, the key challenge here is to assign high similarity scores for any two intermediate adversarial examples perturbed from the same clean image. To address this challenge, we propose a novel Adversarial Contrastive Prompt Tuning (ACPT) method to robustly fine-tune the CLIP image encoder to extract similar embeddings for any two intermediate adversarial queries. With ACPT, we further introduce a detection framework AdvQDet that can detect 7 state-of-the-art query-based attacks with $>99\%$ detection rate within 5 shots. We also show that ACPT is robust to 3 types of adaptive attacks. Code is available at https://github.com/xinwong/AdvQDet.
What Happens Without Background? Constructing Foreground-Only Data for Fine-Grained Tasks
Authors: Yuetian Wang, Wenjin Hou, Qinmu Peng, Xinge You
Date: 2024-08-04 00:00:00

Fine-grained recognition, a pivotal task in visual signal processing, aims to distinguish between similar subclasses based on discriminative information present in samples. However, prevailing methods often erroneously focus on background areas, neglecting the capture of genuinely effective discriminative information from the subject, thus impeding practical application. To facilitate research into the impact of background noise on models and enhance their ability to concentrate on the subject's discriminative features, we propose an engineered pipeline that leverages the capabilities of SAM and Detic to create fine-grained datasets with only foreground subjects, devoid of background. Extensive cross-experiments validate this approach as a preprocessing step prior to training, enhancing algorithmic performance and holding potential for further modal expansion of the data.
AdaCBM: An Adaptive Concept Bottleneck Model for Explainable and Accurate Diagnosis
Authors: Townim F. Chowdhury, Vu Minh Hieu Phan, Kewen Liao, Minh-Son To, Yutong Xie, Anton Van Den Hengel, Johan W. Verjans, Zhibin Liao
Date: 2024-08-04 00:00:00
Tasks: Classification, Diagnostic, Transfer Learning, zero-shot-classification, Zero-Shot Learning

The integration of vision-language models such as CLIP and Concept Bottleneck Models (CBMs) offers a promising approach to explaining deep neural network (DNN) decisions using concepts understandable by humans, addressing the black-box concern of DNNs. While CLIP provides both explainability and zero-shot classification capability, its pre-training on generic image and text data may limit its classification accuracy and applicability to medical image diagnostic tasks, creating a transfer learning problem. To maintain explainability and address transfer learning needs, CBM methods commonly design post-processing modules after the bottleneck module. However, this way has been ineffective. This paper takes an unconventional approach by re-examining the CBM framework through the lens of its geometrical representation as a simple linear classification system. The analysis uncovers that post-CBM fine-tuning modules merely rescale and shift the classification outcome of the system, failing to fully leverage the system's learning potential. We introduce an adaptive module strategically positioned between CLIP and CBM to bridge the gap between source and downstream domains. This simple yet effective approach enhances classification performance while preserving the explainability afforded by the framework. Our work offers a comprehensive solution that encompasses the entire process, from concept discovery to model training, providing a holistic recipe for leveraging the strengths of GPT, CLIP, and CBM.
Faster Diffusion Action Segmentation
Authors: Shuaibing Wang, Shunli Wang, Mingcheng Li, Dingkang Yang, Haopeng Kuang, Ziyun Qian, Lihua Zhang
Date: 2024-08-04 00:00:00
Tasks: Action Segmentation, Computational Efficiency, Segmentation, Temporal Action Segmentation

Temporal Action Segmentation (TAS) is an essential task in video analysis, aiming to segment and classify continuous frames into distinct action segments. However, the ambiguous boundaries between actions pose a significant challenge for high-precision segmentation. Recent advances in diffusion models have demonstrated substantial success in TAS tasks due to their stable training process and high-quality generation capabilities. However, the heavy sampling steps required by diffusion models pose a substantial computational burden, limiting their practicality in real-time applications. Additionally, most related works utilize Transformer-based encoder architectures. Although these architectures excel at capturing long-range dependencies, they incur high computational costs and face feature-smoothing issues when processing long video sequences. To address these challenges, we propose EffiDiffAct, an efficient and high-performance TAS algorithm. Specifically, we develop a lightweight temporal feature encoder that reduces computational overhead and mitigates the rank collapse phenomenon associated with traditional self-attention mechanisms. Furthermore, we introduce an adaptive skip strategy that allows for dynamic adjustment of timestep lengths based on computed similarity metrics during inference, thereby further enhancing computational efficiency. Comprehensive experiments on the 50Salads, Breakfast, and GTEA datasets demonstrated the effectiveness of the proposed algorithm.
Mini-Monkey: Alleviating the Semantic Sawtooth Effect for Lightweight MLLMs via Complementary Image Pyramid
Authors: Mingxin Huang, Yuliang Liu, Dingkang Liang, Lianwen Jin, Xiang Bai
Date: 2024-08-04 00:00:00
Tasks: document understanding

Recently, scaling images to high resolution has received much attention in multimodal large language models (MLLMs). Most existing practices adopt a sliding-window-style cropping strategy to adapt to resolution increase. Such a cropping strategy, however, can easily cut off objects and connected regions, which introduces semantic discontinuity and therefore impedes MLLMs from recognizing small or irregularly shaped objects or text, leading to a phenomenon we call the semantic sawtooth effect. This effect is particularly evident in lightweight MLLMs. To address this issue, we introduce a Complementary Image Pyramid (CIP), a simple, effective, and plug-and-play solution designed to mitigate semantic discontinuity during high-resolution image processing. In particular, CIP dynamically constructs an image pyramid to provide complementary semantic information for the cropping-based MLLMs, enabling them to richly acquire semantics at all levels. Furthermore, we introduce a Scale Compression Mechanism (SCM) to reduce the additional computational overhead by compressing the redundant visual tokens. Our experiments demonstrate that CIP can consistently enhance the performance across diverse architectures (e.g., MiniCPM-V-2, InternVL2, and LLaVA-OneVision), various model capacity (1B$\rightarrow$8B), and different usage configurations (training-free and fine-tuning). Leveraging the proposed CIP and SCM, we introduce a lightweight MLLM, Mini-Monkey, which achieves remarkable performance in both general multimodal understanding and document understanding. On the OCRBench, the 2B-version Mini-Monkey even surpasses the 8B model InternVL2-8B by 12 score. Additionally, training Mini-Monkey is cheap, requiring only eight RTX 3090 GPUs. The code is available at https://github.com/Yuliang-Liu/Monkey.
LEGO: Self-Supervised Representation Learning for Scene Text Images
Authors: Yujin Ren, Jiaxin Zhang, Lianwen Jin
Date: 2024-08-04 00:00:00
Tasks: Representation Learning, Scene Text Recognition

In recent years, significant progress has been made in scene text recognition by data-driven methods. However, due to the scarcity of annotated real-world data, the training of these methods predominantly relies on synthetic data. The distribution gap between synthetic and real data constrains the further performance improvement of these methods in real-world applications. To tackle this problem, a highly promising approach is to utilize massive amounts of unlabeled real data for self-supervised training, which has been widely proven effective in many NLP and CV tasks. Nevertheless, generic self-supervised methods are unsuitable for scene text images due to their sequential nature. To address this issue, we propose a Local Explicit and Global Order-aware self-supervised representation learning method (LEGO) that accounts for the characteristics of scene text images. Inspired by the human cognitive process of learning words, which involves spelling, reading, and writing, we propose three novel pre-text tasks for LEGO to model sequential, semantic, and structural features, respectively. The entire pre-training process is optimized by using a consistent Text Knowledge Codebook. Extensive experiments validate that LEGO outperforms previous scene text self-supervised methods. The recognizer incorporated with our pre-trained model achieves superior or comparable performance compared to state-of-the-art scene text recognition methods on six benchmarks. Furthermore, we demonstrate that LEGO can achieve superior performance in other text-related tasks.
Pixel-Level Domain Adaptation: A New Perspective for Enhancing Weakly Supervised Semantic Segmentation
Authors: Ye Du, Zehua Fu, Qingjie Liu
Date: 2024-08-04 00:00:00
Tasks: Domain Adaptation, Object, Segmentation, Semantic Segmentation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

Recent attention has been devoted to the pursuit of learning semantic segmentation models exclusively from image tags, a paradigm known as image-level Weakly Supervised Semantic Segmentation (WSSS). Existing attempts adopt the Class Activation Maps (CAMs) as priors to mine object regions yet observe the imbalanced activation issue, where only the most discriminative object parts are located. In this paper, we argue that the distribution discrepancy between the discriminative and the non-discriminative parts of objects prevents the model from producing complete and precise pseudo masks as ground truths. For this purpose, we propose a Pixel-Level Domain Adaptation (PLDA) method to encourage the model in learning pixel-wise domain-invariant features. Specifically, a multi-head domain classifier trained adversarially with the feature extraction is introduced to promote the emergence of pixel features that are invariant with respect to the shift between the source (i.e., the discriminative object parts) and the target (\textit{i.e.}, the non-discriminative object parts) domains. In addition, we come up with a Confident Pseudo-Supervision strategy to guarantee the discriminative ability of each pixel for the segmentation task, which serves as a complement to the intra-image domain adversarial training. Our method is conceptually simple, intuitive and can be easily integrated into existing WSSS methods. Taking several strong baseline models as instances, we experimentally demonstrate the effectiveness of our approach under a wide range of settings.
Deep Spectral Methods for Unsupervised Ultrasound Image Interpretation
Authors: Oleksandra Tmenova, Yordanka Velikova, Mahdi Saleh, Nassir Navab
Date: 2024-08-04 00:00:00
Tasks: Anatomy, Clustering, Deep Learning

Ultrasound imaging is challenging to interpret due to non-uniform intensities, low contrast, and inherent artifacts, necessitating extensive training for non-specialists. Advanced representation with clear tissue structure separation could greatly assist clinicians in mapping underlying anatomy and distinguishing between tissue layers. Decomposing an image into semantically meaningful segments is mainly achieved using supervised segmentation algorithms. Unsupervised methods are beneficial, as acquiring large labeled datasets is difficult and costly, but despite their advantages, they still need to be explored in ultrasound. This paper proposes a novel unsupervised deep learning strategy tailored to ultrasound to obtain easily interpretable tissue separations. We integrate key concepts from unsupervised deep spectral methods, which combine spectral graph theory with deep learning methods. We utilize self-supervised transformer features for spectral clustering to generate meaningful segments based on ultrasound-specific metrics and shape and positional priors, ensuring semantic consistency across the dataset. We evaluate our unsupervised deep learning strategy on three ultrasound datasets, showcasing qualitative results across anatomical contexts without label requirements. We also conduct a comparative analysis against other clustering algorithms to demonstrate superior segmentation performance, boundary preservation, and label consistency.
PanicleNeRF: low-cost, high-precision in-field phenotypingof rice panicles with smartphone
Authors: Xin Yang, Xuqi Lu, Pengyao Xie, Ziyue Guo, Hui Fang, Haowei Fu, Xiaochun Hu, Zhenbiao Sun, Haiyan Cen
Date: 2024-08-04 00:00:00
Tasks: 3D Reconstruction, Image Segmentation, NeRF, Semantic Segmentation

The rice panicle traits significantly influence grain yield, making them a primary target for rice phenotyping studies. However, most existing techniques are limited to controlled indoor environments and difficult to capture the rice panicle traits under natural growth conditions. Here, we developed PanicleNeRF, a novel method that enables high-precision and low-cost reconstruction of rice panicle three-dimensional (3D) models in the field using smartphone. The proposed method combined the large model Segment Anything Model (SAM) and the small model You Only Look Once version 8 (YOLOv8) to achieve high-precision segmentation of rice panicle images. The NeRF technique was then employed for 3D reconstruction using the images with 2D segmentation. Finally, the resulting point clouds are processed to successfully extract panicle traits. The results show that PanicleNeRF effectively addressed the 2D image segmentation task, achieving a mean F1 Score of 86.9% and a mean Intersection over Union (IoU) of 79.8%, with nearly double the boundary overlap (BO) performance compared to YOLOv8. As for point cloud quality, PanicleNeRF significantly outperformed traditional SfM-MVS (structure-from-motion and multi-view stereo) methods, such as COLMAP and Metashape. The panicle length was then accurately extracted with the rRMSE of 2.94% for indica and 1.75% for japonica rice. The panicle volume estimated from 3D point clouds strongly correlated with the grain number (R2 = 0.85 for indica and 0.82 for japonica) and grain mass (0.80 for indica and 0.76 for japonica). This method provides a low-cost solution for high-throughput in-field phenotyping of rice panicles, accelerating the efficiency of rice breeding.
Step Saver: Predicting Minimum Denoising Steps for Diffusion Model Image Generation
Authors: Jean Yu, Haim Barad
Date: 2024-08-04 00:00:00
Tasks: Denoising, Image Generation

In this paper, we introduce an innovative NLP model specifically fine-tuned to determine the minimal number of denoising steps required for any given text prompt. This advanced model serves as a real-time tool that recommends the ideal denoise steps for generating high-quality images efficiently. It is designed to work seamlessly with the Diffusion model, ensuring that images are produced with superior quality in the shortest possible time. Although our explanation focuses on the DDIM scheduler, the methodology is adaptable and can be applied to various other schedulers like Euler, Euler Ancestral, Heun, DPM2 Karras, UniPC, and more. This model allows our customers to conserve costly computing resources by executing the fewest necessary denoising steps to achieve optimal quality in the produced images.
FDiff-Fusion:Denoising diffusion fusion network based on fuzzy learning for 3D medical image segmentation
Authors: Weiping Ding, Sheng Geng, Haipeng Wang, Jiashuang Huang, Tianyi Zhou
Date: 2024-07-22 00:00:00
Tasks: Denoising, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

In recent years, the denoising diffusion model has achieved remarkable success in image segmentation modeling. With its powerful nonlinear modeling capabilities and superior generalization performance, denoising diffusion models have gradually been applied to medical image segmentation tasks, bringing new perspectives and methods to this field. However, existing methods overlook the uncertainty of segmentation boundaries and the fuzziness of regions, resulting in the instability and inaccuracy of the segmentation results. To solve this problem, a denoising diffusion fusion network based on fuzzy learning for 3D medical image segmentation (FDiff-Fusion) is proposed in this paper. By integrating the denoising diffusion model into the classical U-Net network, this model can effectively extract rich semantic information from input medical images, thus providing excellent pixel-level representation for medical image segmentation. ... Finally, to validate the effectiveness of FDiff-Fusion, we compare it with existing advanced segmentation networks on the BRATS 2020 brain tumor dataset and the BTCV abdominal multi-organ dataset. The results show that FDiff-Fusion significantly improves the Dice scores and HD95 distance on these two datasets, demonstrating its superiority in medical image segmentation tasks.