Researchers have applied 3D Lookup Tables (LUTs) in cameras, offering new possibilities for enhancing image quality and achieving various tonal effects. However, these approaches often overlook the non-uniformity of color distribution in the original images, which limits the performance of learnable LUTs. To address this issue, we introduce a lightweight end-to-end image enhancement method called Simulated Infrared Fusion Guided Image-adaptive 3D Lookup Tables (SIRLUT). SIRLUT enhances the adaptability of 3D LUTs by reorganizing the color distribution of images through the integration of simulated infrared imagery. Specifically, SIRLUT consists of an efficient Simulated Infrared Fusion (SIF) module and a Simulated Infrared Guided (SIG) refinement module. The SIF module leverages a cross-modal channel attention mechanism to perceive global information and generate dynamic 3D LUTs, while the SIG refinement module blends simulated infrared images to match image consistency features from both structural and color aspects, achieving local feature fusion. Experimental results demonstrate that SIRLUT outperforms state-of-the-art methods on different tasks by up to 0.88 ~ 2.25dB while reducing the number of parameters. Code is available at https://github.com/riversky2025/SIRLUT.git .
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Indoor scene modification has emerged as a prominent area within computer vision, particularly for its applications in Augmented Reality (AR) and Virtual Reality (VR). Traditional methods often rely on pre-existing object databases and predetermined object positions, limiting their flexibility and adaptability to new scenarios. In response to this challenge, we present a novel end-to-end multi-modal deep neural network capable of generating point cloud objects seamlessly integrated with their surroundings, driven by textual instructions. Our work proposes a novel approach in scene modification by enabling the creation of new environments with previously unseen object layouts, eliminating the need for pre-stored CAD models. Leveraging Point-E as our generative model, we introduce innovative techniques such as quantized position prediction and Top-K estimation to address the issue of false negatives resulting from ambiguous language descriptions. Furthermore, we conduct comprehensive evaluations to showcase the diversity of generated objects, the efficacy of textual instructions, and the quantitative metrics, affirming the realism and versatility of our model in generating indoor objects. To provide a holistic assessment, we incorporate visual grounding as an additional metric, ensuring the quality and coherence of the scenes produced by our model. Through these advancements, our approach not only advances the state-of-the-art in indoor scene modification but also lays the foundation for future innovations in immersive computing and digital environment creation. The project is available at https://ainnovatelab.github.io/Context-aware-Indoor-PCG.
Self-supervised category-level 6D pose estimation stands as a fundamental task in computer vision. However, current self-supervised methods face two major challenges. Firstly, existing networks struggle to reconstruct precise object models due to significant part-level shape variations among specific categories. Secondly, they are impacted by the many-to-one ambiguity in the correspondences between pixels and point clouds. To address these challenges, we propose a novel approach that includes a Part-level Shape Reconstruction (PSR) module and a Coarse-to-Fine Correspondence Optimization (CFCO) module. In the (PSR) module, we introduce a part-level discrete shape memory to capture more fine-grained shape variations of different objects and use it to perform precise reconstruction. In the (CFCO) module, we utilize Hungarian matching to generate one-to-one pseudo labels at both region and pixel levels, which provides explicit supervision for the corresponding similarity matrices. We evaluate our method on the REAL275 and WILD6D datasets. Our extensive experiments show that our self-supervised approach outperforms existing methods and achieves new state-of-the-art results within the self-supervised framework.
The increasing use of smartphones for capturing documents in various real-world conditions has underscored the need for robust document localization technologies. Current challenges in this domain include handling diverse document types, complex backgrounds, and varying photographic conditions such as low contrast and occlusion. However, there currently are no publicly available datasets containing these complex scenarios and few methods demonstrate their capabilities on these complex scenes. To address these issues, we create a new comprehensive real-world document localization benchmark dataset which contains the complex scenarios mentioned above and propose a novel Real-world Document Localization Network (RDLNet) for locating targeted documents in the wild. The RDLNet consists of an innovative light-SAM encoder and a masked attention decoder. Utilizing light-SAM encoder, the RDLNet transfers the mighty generalization capability of SAM to the document localization task. In the decoding stage, the RDLNet exploits the masked attention and object query method to efficiently output the triple-branch predictions consisting of corner point coordinates, instance-level segmentation area and categories of different documents without extra post-processing. We compare the performance of RDLNet with other state-of-the-art approaches for real-world document localization on multiple benchmarks, the results of which reveal that the RDLNet remarkably outperforms contemporary methods, demonstrating its superiority in terms of both accuracy and practicability.
Cartoon animal parsing aims to segment the body parts such as heads, arms, legs and tails of cartoon animals. Different from previous parsing tasks, cartoon animal parsing faces new challenges, including irregular body structures, abstract drawing styles and diverse animal categories. Existing methods have difficulties when addressing these challenges caused by the spatial and structural properties of cartoon animals. To address these challenges, a novel spatial learning and structural modeling network, named CAPNet, is proposed for cartoon animal parsing. It aims to address the critical problems of spatial perception, structure modeling and spatial-structural consistency learning. A spatial-aware learning module integrates deformable convolutions to learn spatial features of diverse cartoon animals. The multi-task edge and center point prediction mechanism is incorporated to capture the intricate spatial patterns. A structural modeling method is proposed to model the complex structural representations of cartoon animals, which integrates a graph neural network with a shape-aware relation learning module. To mitigate the significant differences among animals, a spatial and structural consistency learning strategy is proposed to capture and learn feature correlations across different animal species. Extensive experiments conducted on benchmark datasets demonstrate the effectiveness of the proposed approach, which outperforms the state-of-the-art methods.
Cross-Domain Recommendation (CDR) has been proposed to improve the recommendation accuracy in the target domain (the sparser dataset) by benefiting from the auxiliary information transferred or the knowledge learned from one or many source domains (the denser datasets). However, most of the existing CDR approaches still suffer from the problem of negative transfer caused by undifferentiated knowledge transfer, and thus the recommendation accuracy in some domains, especially in the sparser domains, is still too low, which is not practical in real application scenarios. To address this problem, we propose a novel Active Masked Attention framework, i.e., AMA-CDR, for many-to-many CDR scenarios. Our AMA-CDR pursues a higher goal for CDR approaches, i.e., improving the recommendation performance in the target domain to achieve a practically usable level, which is meaningful and challenging in real CDR systems. Specifically, AMA-CDR adopts an end-to-end graph embedding to reduce the objective distortion between graph embedding and embedding combination. More importantly, we propose an active mask for the embedding combination to ease negative transfer, which leverages both the prior knowledge, i.e., data density, and the posterior knowledge, i.e., sample uncertainty. Extensive experiments conducted on two public datasets demonstrate that our proposed AMA-CDR models significantly outperform the state-of-the-art approaches and achieve the new goal.
With the popularity of social media, growing number of online chats and comments are presented in the form of multimodal dialogues containing stickers. Automatically summarizing these dialogues can effectively reduce content overload and save reading time. However, existing datasets and works are either text dialogue summarization, or articles with real photos that respectively perform text summaries and key image extraction, and have not simultaneously considered the multimodal dialogue automatic summarization tasks with sticker images and online chat scenarios. To compensate for the lack of datasets and researches in this field, we propose a brand-new Multimodal Chat Dialogue Summarization Containing Stickers (MCDSCS) task and dataset. It consists of 5,527 Chinese multimodal chat dialogues and 14,356 different sticker images, with each dialogue interspersed with stickers in the text to reflect the real social media chat scenario. MCDSCS can also contribute to filling the gap in Chinese multimodal dialogue data. We use the most advanced GPT4 model and carefully design Chain-of-Thoughts (COT) supplemented with manual review to generate dialogues and extract summaries. We also propose a novel method that integrates the visual information of stickers with the text descriptions of emotions and intentions (TEI). Experiments show that our method can effectively improve the performance of various mainstream summary generation models, even better than some other multimodal models, ChatGPT, and Vision Large Language Models (VLMs). Our data and code are publicly available at https://github.com/FakerBoom/MCDSCS.
Few-Shot Class-Incremental Learning has shown remarkable efficacy in efficient learning new concepts with limited annotations. Nevertheless, the heuristic few-shot annotations may not always cover the most informative samples, which largely restricts the capability of incremental learner. We aim to start from a pool of large-scale unlabeled data and then annotate the most informative samples for incremental learning. Based on this premise, Based on this purpose, this paper introduces the Active Class-Incremental Learning (ACIL). The objective of ACIL is to select the most informative samples from the unlabeled pool to effectively train an incremental learner, aiming to maximize the performance of the resulting model. Note that vanilla active learning algorithms suffer from class-imbalanced distribution among annotated samples, which restricts the ability of incremental learning. To achieve both class balance and informativeness in chosen samples, we propose Class-Balanced Selection (CBS) strategy. Specifically, we first cluster the features of all unlabeled images into multiple groups. Then for each cluster, we employ greedy selection strategy to ensure that the Gaussian distribution of the sampled features closely matches the Gaussian distribution of all unlabeled features within the cluster.Our CBS can be plugged and played into those CIL methods which are based on pretrained models with prompts tunning technique.Extensive experiments under ACIL protocol across five diverse datasets demonstrate that CBS outperforms both random selection and other SOTA active learning approaches.
Sign language recognition (SLR) refers to interpreting sign language glosses from given videos automatically. This research area presents a complex challenge in computer vision because of the rapid and intricate movements inherent in sign languages, which encompass hand gestures, body postures, and even facial expressions. Recently, skeleton-based action recognition has attracted increasing attention due to its ability to handle variations in subjects and backgrounds independently. However, current skeleton-based SLR methods exhibit three limitations: 1) they often neglect the importance of realistic hand poses, where most studies train SLR models on non-realistic skeletal representations; 2) they tend to assume complete data availability in both training or inference phases, and capture intricate relationships among different body parts collectively; 3) these methods treat all sign glosses uniformly, failing to account for differences in complexity levels regarding skeletal representations. To enhance the realism of hand skeletal representations, we present a kinematic hand pose rectification method for enforcing constraints. Mitigating the impact of missing data, we propose a feature-isolated mechanism to focus on capturing local spatial-temporal context. This method captures the context concurrently and independently from individual features, thus enhancing the robustness of the SLR model. Additionally, to adapt to varying complexity levels of sign glosses, we develop an input-adaptive inference approach to optimise computational efficiency and accuracy. Experimental results demonstrate the effectiveness of our approach, as evidenced by achieving a new state-of-the-art (SOTA) performance on WLASL100 and LSA64. For WLASL100, we achieve a top-1 accuracy of 86.50%, marking a relative improvement of 2.39% over the previous SOTA. For LSA64, we achieve a top-1 accuracy of 99.84%. The artefacts and code related to this study are made publicly online (https://github.com/mpuu00001/Siformer.git).
Novel View Synthesis plays a crucial role by generating new 2D renderings from multi-view images of 3D scenes. However, capturing high-speed scenes with conventional cameras often leads to motion blur, hindering the effectiveness of 3D reconstruction. To address this challenge, high-frame-rate dense 3D reconstruction emerges as a vital technique, enabling detailed and accurate modeling of real-world objects or scenes in various fields, including Virtual Reality or embodied AI. Spike cameras, a novel type of neuromorphic sensor, continuously record scenes with an ultra-high temporal resolution, showing potential for accurate 3D reconstruction. Despite their promise, existing approaches, such as applying Neural Radiance Fields (NeRF) to spike cameras, encounter challenges due to the time-consuming rendering process. To address this issue, we make the first attempt to introduce the 3D Gaussian Splatting (3DGS) into spike cameras in high-speed capture, providing 3DGS as dense and continuous clues of views, then constructing SpikeGS. Specifically, to train SpikeGS, we establish computational equations between the rendering process of 3DGS and the processes of instantaneous imaging and exposing-like imaging of the continuous spike stream. Besides, we build a very lightweight but effective mapping process from spikes to instant images to support training. Furthermore, we introduced a new spike-based 3D rendering dataset for validation. Extensive experiments have demonstrated our method possesses the high quality of novel view rendering, proving the tremendous potential of spike cameras in modeling 3D scenes. Code and data are available at https://github.com/Leozhangjiyuan/SpikeGS.
Black-box domain adaptation treats the source domain model as a black box. During the transfer process, the only available information about the target domain is the noisy labels output by the black-box model. This poses significant challenges for domain adaptation. Conventional approaches typically tackle the black-box noisy label problem from two aspects: self-knowledge distillation and pseudo-label denoising, both achieving limited performance due to limited knowledge information. To mitigate this issue, we explore the potential of off-the-shelf vision-language (ViL) multimodal models with rich semantic information for black-box domain adaptation by introducing an Adversarial Experts Model (AEM). Specifically, our target domain model is designed as one feature extractor and two classifiers, trained over two stages: In the knowledge transferring stage, with a shared feature extractor, the black-box source model and the ViL model act as two distinct experts for joint knowledge contribution, guiding the learning of one classifier each. While contributing their respective knowledge, the experts are also updated due to their own limitation and bias. In the adversarial alignment stage, to further distill expert knowledge to the target domain model, adversarial learning is conducted between the feature extractor and the two classifiers. A new consistency-max loss function is proposed to measure two classifier consistency and further improve classifier prediction certainty. Extensive experiments on multiple datasets demonstrate the effectiveness of our approach. Code is available at https://github.com/singinger/AEM.
Detecting out-of-distribution (OOD) inputs is a principal task for ensuring the safety of deploying deep-neural-network classifiers in open-set scenarios. OOD samples can be drawn from arbitrary distributions and exhibit deviations from in-distribution (ID) data in various dimensions, such as foreground features (e.g., objects in CIFAR100 images vs. those in CIFAR10 images) and background features (e.g., textural images vs. objects in CIFAR10). Existing methods can confound foreground and background features in training, failing to utilize the background features for OOD detection. This paper considers the importance of feature disentanglement in out-of-distribution detection and proposes the simultaneous exploitation of both foreground and background features to support the detection of OOD inputs in in out-of-distribution detection. To this end, we propose a novel framework that first disentangles foreground and background features from ID training samples via a dense prediction approach, and then learns a new classifier that can evaluate the OOD scores of test images from both foreground and background features. It is a generic framework that allows for a seamless combination with various existing OOD detection methods. Extensive experiments show that our approach 1) can substantially enhance the performance of four different state-of-the-art (SotA) OOD detection methods on multiple widely-used OOD datasets with diverse background features, and 2) achieves new SotA performance on these benchmarks. Code is available at https://github.com/mala-lab/DFB.
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.
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.
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.
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.
Remarkable progresses have been made in hyperspectral image (HSI) denoising. However, the majority of existing methods are predominantly confined to the spatial-spectral domain, overlooking the untapped potential inherent in the Fourier domain. This paper presents a novel approach to address HSI denoising by bridging the information from the Fourier and spatial-spectral domains. Our method highlights key insights into the Fourier properties within spatial and spectral domains through the Fourier transform. Specifically, we note that the amplitude inherently embody noise and photon reflection characteristics, while the phase holds structural information. These insights unveil new perspectives on the physical properties of HSIs, motivating us to leverage complementary information exchange between Fourier and spatial-spectral domains. To this end, we introduce the Fourier-prior Integration Denoising Network (FIDNet), a potent yet straightforward approach that utilizes Fourier insights to synergistically interact with spatial-spectral domains for superior HSI denoising. In FIDNet, we independently extract spatial and Fourier features through dual branches and merge these representations to enhance spectral evolution modeling through the inherent structure consistency constraints and continuing reflection variation revealed in Fourier prior. Our proposed method demonstrates robust generalization across synthetic and real-world benchmark datasets, achieves comparable results with state-of-the-art methods in both quantitative quality and visual results. The code is available at https://github.com/MIV-XJTU/FIDNet.
Detecting Multimodal Situations with Insufficient Context and Abstaining from Baseless Predictions
PDF ↗Despite the widespread adoption of Vision-Language Understanding (VLU) benchmarks such as VQA v2, OKVQA, A-OKVQA, GQA, VCR, SWAG, and VisualCOMET, our analysis reveals a pervasive issue affecting their integrity: these benchmarks contain samples where answers rely on assumptions unsupported by the provided context. Training models on such data fosters biased learning and hallucinations as models tend to make similar unwarranted assumptions. To address this issue, we collect contextual data for each sample whenever available and train a context selection module to facilitate evidence-based model predictions. Strong improvements across multiple benchmarks demonstrate the effectiveness of our approach. Further, we develop a general-purpose Context-AwaRe Abstention (CARA) detector to identify samples lacking sufficient context and enhance model accuracy by abstaining from responding if the required context is absent. CARA exhibits generalization to new benchmarks it wasn't trained on, underscoring its utility for future VLU benchmarks in detecting or cleaning samples with inadequate context. Finally, we curate a Context Ambiguity and Sufficiency Evaluation (CASE) set to benchmark the performance of insufficient context detectors. Overall, our work represents a significant advancement in ensuring that vision-language models generate trustworthy and evidence-based outputs in complex real-world scenarios. GitHub link: https://github.com/JunzhangLiu/CARA
Large pre-trained vision-language models like CLIP have shown amazing zero-shot recognition performance. To adapt pre-trained vision-language models to downstream tasks, recent studies have focused on the learnable context + class name paradigm, which learns continuous prompt contexts on downstream datasets. In practice, the learned prompt context tends to overfit the base categories and cannot generalize well to novel categories out of the training data. Recent works have also noticed this problem and have proposed several improvements. In this work, we draw a new insight based on empirical analysis, that is, uninformative class names lead to degraded base-to-novel generalization performance in prompt learning, which is usually overlooked by existing works. Under this motivation, we advocate to improve the base-to-novel generalization performance of prompt learning by enhancing the semantic richness of class names. We coin our approach as the Information Disengagement based Associative Prompt Learning (IDAPL) mechanism which considers the associative, meanwhile, decoupled learning of prompt context and class name embedding. IDAPL can effectively alleviate the phenomenon of learnable context overfitting to base classes, meanwhile, learning more informative semantic representation of base classes by fine-tuning the class name embedding, leading to improved performance on both base and novel classes. Experimental results on eleven widely used few-shot learning benchmarks clearly validate the effectiveness of our proposed approach. Code is available at https://github.com/tiggers23/IDAPL
3D Referring Expression Segmentation (3D-RES) is dedicated to segmenting a specific instance within a 3D space based on a natural language description. However, current approaches are limited to segmenting a single target, restricting the versatility of the task. To overcome this limitation, we introduce Generalized 3D Referring Expression Segmentation (3D-GRES), which extends the capability to segment any number of instances based on natural language instructions. In addressing this broader task, we propose the Multi-Query Decoupled Interaction Network (MDIN), designed to break down multi-object segmentation tasks into simpler, individual segmentations. MDIN comprises two fundamental components: Text-driven Sparse Queries (TSQ) and Multi-object Decoupling Optimization (MDO). TSQ generates sparse point cloud features distributed over key targets as the initialization for queries. Meanwhile, MDO is tasked with assigning each target in multi-object scenarios to different queries while maintaining their semantic consistency. To adapt to this new task, we build a new dataset, namely Multi3DRes. Our comprehensive evaluations on this dataset demonstrate substantial enhancements over existing models, thus charting a new path for intricate multi-object 3D scene comprehension. The benchmark and code are available at https://github.com/sosppxo/MDIN.