The constrained data scale in low-level vision often induces the demon overfitting hazard for restoration networks, necessitating the adoption of the pre-training paradigm. Mirroring the success of the high-level pre-training approaches, recent methods in the low-level community aim to derive general visual representation from extensive data with synthesized degradation. In this paper, we propose a new perspective beyond the data-driven image pre-training paradigm for low-level vision, building upon the following examination. First, unlike the semantic extraction prevalent in high-level vision, low-level vision primarily focuses on the continuous and content-agnostic pixel-level regression, indicating that the diversified contents inherent in large-scale data are potentially unnecessary for low-level vision pre-training. Second, considering the low-level degradations are highly relevant to the frequency spectrum, we discern that the low-level pre-training paradigm can be implemented in the Fourier space with fostered degradation sensibility. Therefore, we develop an Image-free Pre-training (IFP) paradigm, a novel low-level pre-training approach with necessity of single randomly sampled Gaussian noise image, streamlining complicated data collection and synthesis procedure. The principle of the IFP involves reconstructing the original Gaussian noise from the randomly perturbed counterpart with partially masked spectrum band, facilitating the capability for robust spectrum representation extraction in response to the capricious downstream degradations. Extensive experiments demonstrate the significant improvements brought by IFP to various downstream tasks, such as 1.31 dB boost in low-light enhancement for Restormer, and improvements of 1.2 dB in deblurring, and 2.42 dB in deraining for Uformer. Code is publicly available at https://github.com/siywang541/IFP.
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Scene Graph Generation(SGG) is a scene understanding task that aims at identifying object entities and reasoning their relationships within a given image. In contrast to prevailing two-stage methods based on a large object detector (e.g., Faster R-CNN), one-stage methods integrate a fixed-size set of learnable queries to jointly reason relational triplets . This paradigm demonstrates robust performance with significantly reduced parameters and computational overhead. However, the challenge in one-stage methods stems from the issue of weak entanglement, wherein entities involved in relationships require both coupled features shared within triplets and decoupled visual features. Previous methods either adopt a single decoder for coupled triplet feature modeling or multiple decoders for separate visual feature extraction but fail to consider both. In this paper, we introduce UniQ, a Unified decoder with task-specific Queries architecture, where task-specific queries generate decoupled visual features for subjects, objects, and predicates respectively, and unified decoder enables coupled feature modeling within relational triplets. Experimental results on the Visual Genome dataset demonstrate that UniQ has superior performance to both one-stage and two-stage methods.
Chain of Visual Perception: Harnessing Multimodal Large Language Models for Zero-shot Camouflaged Object Detection
PDF ↗In this paper, we introduce a novel multimodal camo-perceptive framework (MMCPF) aimed at handling zero-shot Camouflaged Object Detection (COD) by leveraging the powerful capabilities of Multimodal Large Language Models (MLLMs). Recognizing the inherent limitations of current COD methodologies, which predominantly rely on supervised learning models demanding extensive and accurately annotated datasets, resulting in weak generalization, our research proposes a zero-shot MMCPF that circumvents these challenges. Although MLLMs hold significant potential for broad applications, their effectiveness in COD is hindered and they would make misinterpretations of camouflaged objects. To address this challenge, we further propose a strategic enhancement called the Chain of Visual Perception (CoVP), which significantly improves the perceptual capabilities of MLLMs in camouflaged scenes by leveraging both linguistic and visual cues more effectively. We validate the effectiveness of MMCPF on five widely used COD datasets, containing CAMO, COD10K, NC4K, MoCA-Mask and OVCamo. Experiments show that MMCPF can outperform all existing state-of-the-art zero-shot COD methods, and achieve competitive performance compared to weakly-supervised and fully-supervised methods, which demonstrates the potential of MMCPF. The Github link of this paper is https://github.com/luckybird1994/MMCPF.
Image-to-Video adaptation is proposed to train a model using labeled images and unlabeled videos to facilitate the classification of unlabeled videos. The latest work synthesizes videos using still images to mitigate the modality gap between images and videos. However, the synthesized videos are not realistic due to the camera movements are only simulated in 2D space. Therefore, we generate realistic videos by simulating arbitrary camera movements in 3D scenes, and then the model can be trained using the generated source videos. Unfortunately, the optical flows from the generated videos have unexpected negative impacts, resulting in suboptimal performance. To address this issue, we propose the Category-aware Flow Memory Bank, which replaces optical flows in source videos with real target flows, and the new composed videos are beneficial for training. In addition, we leverage the video pace prediction task to enhance the model's perception of speed. Our method achieves state-of-the-art performance and comparable performance on three widely used benchmarks. Our code is available at https://github.com/KenanHuang/mm2024\_cfb4i2v.
Existing studies for gait recognition primarily utilized sequences of either binary silhouette or human parsing to encode the shapes and dynamics of persons during walking. Silhouettes exhibit accurate segmentation quality and robustness to environmental variations, but their low information entropy may result in sub-optimal performance. In contrast, human parsing provides fine-grained part segmentation with higher information entropy, but the segmentation quality may deteriorate due to the complex environments. To discover the advantages of silhouette and parsing and overcome their limitations, this paper proposes a novel cross-granularity alignment gait recognition method, named XGait, to unleash the power of gait representations of different granularity. To achieve this goal, the XGait first contains two branches of backbone encoders to map the silhouette sequences and the parsing sequences into two latent spaces, respectively. Moreover, to explore the complementary knowledge across the features of two representations, we design the Global Cross-granularity Module (GCM) and the Part Cross-granularity Module (PCM) after the two encoders. In particular, the GCM aims to enhance the quality of parsing features by leveraging global features from silhouettes, while the PCM aligns the dynamics of human parts between silhouette and parsing features using the high information entropy in parsing sequences. In addition, to effectively guide the alignment of two representations with different granularity at the part level, an elaborate-designed learnable division mechanism is proposed for the parsing features. Finally, comprehensive experiments on two large-scale gait datasets not only show the superior performance of XGait with the Rank-1 accuracy of 80.5% on Gait3D and 88.3% CCPG but also reflect the robustness of the learned features even under challenging conditions like occlusions and cloth changes
Cross-modal 2D-3D point cloud semantic segmentation using few-shot-based learning provides a practical approach for borrowing matured 2D domain knowledge into the 3D segmentation model, which reduces the reliance on laborious 3D annotation work and improves generalization to new categories. However, previous methods use single-view point cloud generation algorithms to bridge the gap between 2D images and 3D point clouds, leaving the incomplete geometry of an object or scene due to occlusions. To address this issue, we propose a novel view synthesis cross-modal few-shot point cloud semantic segmentation network. It introduces the color and depth inpainting to generate multi-view images and masks, which compensate for the absent depth information of generated point clouds. Additionally, we propose a Co-embedding Network to bridge the domain features between synthesized and original, collected 3D data, and a weighted prototype network is employed to balance the impact of multi-view images and enhance the segmentation performance. Extensive experiments on two benchmarks show the superiority of our method by outperforming the existing cross-modal few-shot 3D segmentation methods.
The rise of mobile devices has spurred advancements in camera technology and image quality. However, mobile photography still faces issues like scattering and reflective flares. While previous research has acknowledged the negative impact of the mobile devices' internal image signal processing pipeline (ISP) on image quality, the specific ISP operations that hinder flare removal have not been fully identified. In addition, current solutions only partially address ISP-related deterioration due to a lack of comprehensive raw image datasets for flare study. To bridge these research gaps, we introduce a new raw image dataset tailored for mobile camera systems, focusing on eliminating flare. This dataset encompasses over 2,000 high-quality, full-resolution raw image pairs for scattering flare, and 1,200 for reflective flare, captured across various real-world scenarios, mobile devices, and camera settings. It is designed to enhance the generalizability of flare removal algorithms across a wide spectrum of conditions. Through detailed experiments, we have identified that ISP operations, such as denoising, compression, and sharpening, may either improve or obstruct flare removal, offering critical insights into optimizing ISP configurations for better flare mitigation. Our dataset is poised to advance the understanding of flare-related challenges, enabling more precise incorporation of flare removal steps into the ISP. Ultimately, this work paves the way for significant improvements in mobile image quality, benefiting both enthusiasts and professional mobile photographers alike.
All along, KG completion relied on link prediction has always been the focus of researchers. However, overwhelming majority of them can only serve 2-ary KGs. While in practice, knowledge hypergraphs (KH) covering facts beyond binary relations are far more ubiquitous. When confronted with them, massive studies for KGs show inadaptability. The several work towards N-ary KHs generally simply extend KG methods. And they usually transform N-ary knowledge into role-value pairs or triples, largely simplifying inherent association within each piece of knowledge. Furthermore, previous models study each N-ary knowledge independently, resulting in structural correlations among them being completely neglected. Motivated by these, avoiding breaking knowledge structure in KHs like previous studies do, we propose the first KH reasoning model based on original knowledge formats, RHKH. Challenged by complicated compositions indicated by the original format of N-ary tuples, association within and among each tuple is discovered through an innovative relational hypergraph neural network, RHNN. It considers complex interactions between relation and entities involved in the same knowledge as well. To refine such interactions, semantic components at each arity-position of relations are distinguished, along with introducing position-specific shift. Extensive experiments demonstrate the effectiveness of RHKH.
Multilingual text recognition (MLTR) is increasingly essential for facilitating cultural communication. However, existing methods often struggle with retaining previous knowledge when learning new languages. A straightforward solution is performing incremental learning (IL) on MLTR tasks. However, it ignores the shared words and characters across incremental languages, which we first term as an incremental sharing problem. Motivated by this observation, we propose HierArchicalMulti-label learning framework for Multilingual tExt Recognition, termed HAMMER. An online knowledge analysis is designed to identify shared knowledge and provide corresponding multi-label language supervision. Specifically, only words and characters appearing simultaneously in multiple languages are considered shared knowledge. Additionally, to further capture language dependencies, we introduce a hierarchical language evaluation mechanism to predict language scores at word and character levels. These scores, supervised by the knowledge analysis, guide the specific recognizers to effectively utilize both old and new language knowledge, thereby mitigating catastrophic forgetting caused by imbalanced rehearsal sets. Extensive experiments conducted on benchmark datasets, MLT17 and MLT19, show that HAMMER exhibits remarkable results and outperforms other state-of-the-art approaches.
Current state-of-the-art flow methods are mostly based on dense all-pairs cost volumes. However, as image resolution increases, the computational and spatial complexity of constructing these cost volumes grows at a quartic rate, making these methods impractical for high-resolution images. In this paper, we propose a novel Hybrid Cost Volume for memory-efficient optical flow, named HCV. To construct HCV, we first propose a Top-k strategy to separate the 4D cost volume into two global 3D cost volumes. These volumes significantly reduce memory usage while retaining a substantial amount of matching information. We further introduce a local 4D cost volume with a local search space to supplement the local information for HCV. Based on HCV, we design a memory-efficient optical flow network, named HCVFlow. Compared to the recurrent flow methods based the all-pairs cost volumes, our HCVFlow significantly reduces memory consumption while ensuring high accuracy. We validate the effectiveness and efficiency of our method on the Sintel and KITTI datasets and real-world 4K (2160 × 3840) resolution images. Extensive experiments show that our HCVFlow has very low memory usage and outperforms other memory-efficient methods in terms of accuracy. The code is publicly available at https://github.com/gangweiX/HCVFlow.
Contrastive multi-view clustering is widely recognized for its effectiveness in mining feature representation across views via contrastive learning (CL), gaining significant attention in recent years. Most existing methods mainly focus on the feature-level or/and cluster-level CL, but there are still two shortcomings. Firstly, feature-level CL is limited by the influence of anomalies and large noise data, resulting in insufficient mining of discriminative feature representation. Secondly, cluster-level CL lacks the guidance of global information and is always restricted by the local diversity information. We in this paper Learn dUal enhanCed rEpresentation for Contrastive Multi-view Clustering (LUCE-CMC) to effectively addresses the above challenges, and it mainly contains two parts, i.e., enhanced feature-level CL (En-FeaCL) and enhanced cluster-level CL (En-CluCL). Specifically, we first adopt a shared encoder to learn shared feature representations between multiple views and then obtain cluster-relevant information that is beneficial to the clustering results. Moreover, we design a reconstitution approach to force the model to concentrate on learning features that are critical to reconstructing the input data, reducing the impact of noisy data and maximizing the sufficient discriminative information of different views in helping the En-FeaCL part. Finally, instead of contrasting the view-specific clustering result like most existing methods do, we in the En-CluCL part make the information at the cluster-level more richer by contrasting the cluster assignment from each view and the cluster assignment obtained from the shared fused features. The end-to-end training methods of the proposed model are mutually reinforcing and beneficial. Extensive experiments conducted on multi-view datasets show that the proposed LUCE-CMC outperforms established baselines to a considerable extent. The source code is released at https://github.com/ShizheHu.
Medical report generation (MRG) has emerged as a pivotal research topic in the medical multi-modal field, given its potential to alleviate the heavy workloads of radiologists. Recently, advancements have been made with MRG systems that leverage large multimodal models (LMMs) to generate high-quality reports. To address the challenge of collecting large amounts of paired medical image-report data for training, this paper proposes a zero-shot report generation model based on in-context learning, we call it MCVGen. Departing from traditional in-context learning approaches that directly feed all demonstrations to a pre-trained large model, this work innovates by employing a multi-modal contextual vector (MCV) to represent the contextual information of demonstrations. Initially, we pre-train a medical large multi-modal model (Med-LMM) and secure the last hidden state of each demonstration through the forward pass in Med-LMM. Benefits from the auto-regressive mechanism, the last hidden state garners critical information to the targeted scenarios. Subsequently, we average the multiple MCVs and integrate them with the first hidden state on the new query, thereby shifting the latent states and guiding the model toward acquiring previously unlearned multi-modal contextual information. This approach has the advantage of regulating the number of prompts, thus reducing computational costs. We tested our model on the publicly available IU X-ray and MIMIC datasets, demonstrating its exceptional zero-shot capability on both cross-center and cross-disease evaluations. We hope it could be a viable solution for practical clinical applications.
In recent years, multi-view outlier detection (MVOD) methods have advanced significantly, aiming to identify outliers within multi-view datasets. A key point is to better detect class outliers and class-attribute outliers, which only exist in multi-view data. However, existing methods either is not able to reduce the impact of outliers when learning view-consistent information, or struggle in cases with varying neighborhood structures. Moreover, most of them do not apply to partial multi-view data in real-world scenarios. To overcome these drawbacks, we propose a novel method named Regularized Contrastive Partial Multi-view Outlier Detection (RCPMOD). In this framework, we utilize contrastive learning to learn view-consistent information and distinguish outliers by the degree of consistency. Specifically, we propose (1) An outlier-aware contrastive loss with a potential outlier memory bank to eliminate their bias motivated by a theoretical analysis. (2) A neighbor alignment contrastive loss to capture the view-shared local structural correlation. (3) A spreading regularization loss to prevent the model from overfitting over outliers. With the Cross-view Relation Transfer technique, we could easily impute the missing view samples based on the features of neighbors. Experimental results on four benchmark datasets demonstrate that our proposed approach could outperform state-of-the-art competitors under different settings.
Few-Shot Joint Multimodal Entity-Relation Extraction via Knowledge-Enhanced Cross-modal Prompt Model
Joint Multimodal Entity-Relation Extraction (JMERE) is a challenging task that aims to extract entities and their relations from textimage pairs in social media posts. Existing methods for JMERE require large amounts of labeled data. However, gathering and annotating fine-grained multimodal data for JMERE poses significant challenges. Initially, we construct diverse and comprehensive multimodal few-shot datasets fitted to the original data distribution. To address the insufficient information in the few-shot setting, we introduce the Knowledge-Enhanced Cross-modal Prompt Model (KECPM) for JMERE. This method can effectively address the problem of insufficient information in the few-shot setting by guiding a large language model to generate supplementary background knowledge. Our proposed method comprises two stages: (1) a knowledge ingestion stage that dynamically formulates prompts based on semantic similarity guide ChatGPT generating relevant knowledge and employs self-reflection to refine the knowledge; (2) a knowledge-enhanced language model stage that merges the auxiliary knowledge with the original input and utilizes a transformerbased model to align with JMERE's required output format. We extensively evaluate our approach on a few-shot dataset derived from the JMERE dataset, demonstrating its superiority over strong baselines in terms of both micro and macro F1 scores. Additionally, we present qualitative analyses and case studies to elucidate the effectiveness of our model. Code and Data are released at https://github.com/YuanLi95/KECPM.
In recent years, Few-Shot Object Detection (FSOD) has gained widespread attention and made significant progress due to its ability to build models with a good generalization power using extremely limited annotated data. The fine-tuning based paradigm is currently dominating this field, where detectors are initially pre-trained on base classes with sufficient samples and then fine-tuned on novel ones with few samples, but the scarcity of labeled samples of novel classes greatly interferes precisely fitting their data distribution, thus hampering the performance. To address this issue, we propose a new framework for FSOD, namely Prototype-based Soft-labels and Test-Time Learning (PS-TTL). Specifically, we design a Test-Time Learning (TTL) module that employs a mean-teacher network for self-training to discover novel instances from test data, allowing detectors to learn better representations and classifiers for novel classes. Furthermore, we notice that even though relatively low-confidence pseudo-labels exhibit classification confusion, they still tend to recall foreground. We thus develop a Prototype-based Soft-labels (PS) strategy through assessing similarities between low-confidence pseudo-labels and category prototypes as soft-labels to unleash their potential, which substantially mitigates the constraints posed by few-shot samples. Extensive experiments on both the VOC and COCO benchmarks show that PS-TTL achieves the state-of-the-art, highlighting its effectiveness. The code and model are available at https://github.com/gaoyingjay/PS-TTL.
The core of active semi-supervised crowd counting is the sample selection criteria. However, the scale factor has been neglected in active learning approaches despite the fact that the scale of heads varies drastically in the crowd images. In this paper, we propose a simple yet effective active labeling strategy to explicitly select informative unlabeled images, guided by the intra-scale uncertainty and inter-scale inconsistency metrics. The intra-scale uncertainty is quantified through the sum of the query-level entropy of images at different scales. Images are initially ranked based on this uncertainty for preselection. Inter-scale inconsistency is measured by the divergence between the query-level predictions of upscaled and downscaled images, allowing for the identification of the most informative images exhibiting the highest inconsistency. Additionally, we implement a progressive updating scheme for the semi-supervised crowd counting framework, in which the pseudo-labels for unlabeled images are refined iteratively. It further improves the counting accuracy. Through extensive experiments on widely used benchmarks, the proposed approach has demonstrated superior performance compared to previous state-of-the-art semi-supervised and active semi-supervised crowd counting methods.
Although current prompt learning methods have successfully been designed to effectively reuse the large pre-trained models without fine-tuning their large number of parameters, they still have limitations to be addressed, i.e., without considering the adverse impact of meaningless patches in every image and without simultaneously considering in-sample generalization and out-of-sample generalization. In this paper, we propose an adaptive multi-modality prompt learning to address the above issues. To do this, we employ previous text prompt learning and propose a new image prompt learning. The image prompt learning achieves in-sample and out-of-sample generalization, by first masking meaningless patches and then padding them with the learnable parameters and the information from texts. Moreover, each of the prompts provides auxiliary information to each other, further strengthening these two kinds of generalization. Experimental results on real datasets demonstrate that our method outperforms SOTA methods, in terms of different downstream tasks.
Multi-modal Unsupervised Domain Adaptation (MM-UDA) for large-scale 3D semantic segmentation involves adapting 2D and 3D models to a target domain without labels, which significantly reduces the labor-intensive annotations. Existing MM-UDA methods have often attempted to mitigate the domain discrepancy by aligning features between the source and target data. However, this implementation falls short when applied to image perception due to the susceptibility of images to environmental changes compared to point clouds. To mitigate this limitation, in this work, we explore the potentials of an off-the-shelf Contrastive Language-Image Pre-training (CLIP) model with rich whilst heterogeneous knowledge. To make CLIP task-specific, we propose a top-performing method, dubbed CLIP2UDA, which makes frozen CLIP reward unsupervised domain adaptation in 3D semantic segmentation. Specifically, CLIP2UDA alternates between two steps during adaptation: (a) Learning task-specific prompt. 2D features response from the visual encoder are employed to initiate the learning of adaptive text prompt of each domain, and (b) Learning multi-modal domain-invariant representations. These representations interact hierarchically in the shared decoder to obtain unified 2D visual predictions. This enhancement allows for effective alignment between the modality-specific 3D and unified feature space via cross-modal mutual learning. Extensive experimental results demonstrate that our method outperforms state-of-the-art competitors in several widely-recognized adaptation scenarios. Code is available at: https://github.com/Barcaaaa/CLIP2UDA.
The rapid development of multi-media techniques boosts the emergence of multi-view data, and how to uncover its intrinsic structure and utilize it to conduct the subsequent downstream tasks is crucial in data analysis. Multi-view clustering is representative of handling multi-view data. The anchor-based method has received widespread attention for excellent performance and low time complexity. However, existing methods encounter two drawbacks, cutting down their performance, i.e., the assumption of the availability of all views and limited interaction of anchor generation among views. In some scenes, views arrive sequentially, and storing them is challenging owing to the limited space/privacy considerations, and the existing anchor-based MVC is unsuitable for this. Additionally, recent works fail to generate anchors with the guidance of other views, and it is tough to align the anchor graphs. To this end, we propose A Lightweight Anchor-Based Incremental Framework for Multi-view Clustering. Specifically, we first initialize an anchor graph with the assistance of k-means when a new view arrives. Then, the consensus one of the anchor graph is updated by the newly collected view with a permutation matrix. Our proposed method is more capable of anchor alignment because, in incremental MVC, the anchor graphs of previous views could be listed as a reference to guide the generation of anchor graphs of the coming view. Furthermore, we design a three-step iterative and convergent algorithm to address the resultant problem. Notably, the proposed algorithm shows outstanding effectiveness and time/space efficiency in extensive experiments.
Enhancing Images with Coupled Low-Resolution and Ultra-Dark Degradations: A Tri-level Learning Framework
PDF ↗Due to device constraints and lighting conditions, captured images frequently exhibit coupled low-resolution and ultra-dark degradations. Enhancing the visibility and resolution of ultra-dark images simultaneously is crucial for practical applications. Current approaches often address both tasks in isolation or through simplistic cascading strategies, while also relying heavily on empirical and manually designed composite loss constraints, which inevitably results in compromised training efficacy, increased artifacts, and diminished detail fidelity. To address these issues, we propose TriCo, the first to adopt a Tri -level learning framework that explicitly formulates the bidirectional Co operative relationship and devises algorithms to tackle coupled degradation factors. In the optimization across Upper (U)-Middle (M)-Lower (L) levels, we model the synergistic dependencies between illumination learning and super-resolution tasks within the M-L levels. Moving to the U-M levels, we introduce hyper-variables to automate the learning of beneficial constraints for both learning tasks, moving beyond the traditional trial-and-error pitfalls of the learning process. Algorithmically, we establish a Phased Gradient-Response (PGR) algorithm as our training mechanism, which facilitates a dynamic, inter-variable gradient feedback and ensures efficient and rapid convergence. Moreover, we merge inherent illumination priors with universal semantic model features to adaptively guide pixel-level high-frequency detail recovery. Extensive experimentation validates the framework's broad generalizability across challenging ultra-dark scenarios, outperforming current state-of-the-art methods across 4 real and synthetic benchmark datasets over 6 metrics (e.g., 5.8%← in PSNR and 26.6%← in LPIPS).