Association for the Advancement of Artificial Intelligence: AAAI Publications
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Multi-task Visual Grounding with Coarse-to-Fine Consistency Constraints
No full textMulti-task visual grounding involves the simultaneous execution of localization and segmentation in images based on textual expressions. The majority of advanced methods predominantly focus on transformer-based multimodal fusion, aiming to extract robust multimodal representations. However, ambiguity between referring expression comprehension (REC) and referring image segmentation (RIS) is error-prone, leading to inconsistencies between multi-task predictions. Besides, insufficient multimodal understanding directly contributes to biased target perception. To overcome these challenges, we propose a Coarse-to-fine Consistency Constraints Visual Grounding architecture (C3VG), which integrates implicit and explicit modeling approaches within a two-stage framework. Initially, query and pixel decoders are employed to generate preliminary detection and segmentation outputs, a process referred to as the Rough Semantic Perception (RSP) stage. These coarse predictions are subsequently refined through the proposed Mask-guided Interaction Module (MIM) and a novel explicit bidirectional consistency constraint loss to ensure consistent representations across tasks, which we term the Refined Consistency Interaction (RCI) stage. Furthermore, to address the challenge of insufficient multimodal understanding, we leverage pre-trained models based on visual-linguistic fusion representations. Empirical evaluations on the RefCOCO, RefCOCO+, and RefCOCOg datasets demonstrate the efficacy and soundness of C3VG, which significantly outperforms state-of-the-art REC and RIS methods by a substantial margin
Latent Diffusion-Enhanced Virtual Try-On via Optimized Pseudo-Label Generation
No full textEfficiently applying fully supervised learning to virtual try-on tasks is challenging due to the lack of paired ground truth in available training samples. Recent works have achieved virtual try-ons by employing self-supervised learning-based inpainting paradigms. However, this approach is heavily dependent on the constraints of inpainting masks. An incorrect mask can mislead the generated results, while overly large mask areas can lose essential original information, thereby hindering the synthesis of high-quality results. To address these problems, we propose a latent diffusion model-based virtual try-on network that achieves fully supervised learning using the concept of cycle consistency and knowledge distillation. Specifically, we divide our approach into pretext and downstream tasks. In the pretext task, we generate a pseudo-label (pseudo-person image) to form paired training samples, which enables the downstream task to achieve fully supervised learning. To prevent the unreliable pseudo-person image from introducing irresponsible prior knowledge, we propose a noise-covering strategy, which aims at fully optimizing the pseudo-label to eliminate the impact of the incorrect inpainting mask as much as possible. Additionally, we propose a skin refinement loss to further enhance the generation of details in the skin region. Extended experiments demonstrate that our proposed method is superior to state-of-the-art methods
A Diffusion-Based Framework for Occluded Object Movement
No full textSeamlessly moving objects within a scene is a common requirement for image editing, but it is still a challenge for existing editing methods. Especially for real-world images, the occlusion situation further increases the difficulty. The main difficulty is that the occluded portion needs to be completed before movement can proceed. To leverage the real-world knowledge embedded in the pre-trained diffusion models, we propose a Diffusion-based framework specifically designed for Occluded Object Movement, named DiffOOM. The proposed DiffOOM consists of two parallel branches that perform object de-occlusion and movement simultaneously. The de-occlusion branch utilizes a background color-fill strategy and a continuously updated object mask to focus the diffusion process on completing the obscured portion of the target object. Concurrently, the movement branch employs latent optimization to place the completed object in the target location and adopts local text-conditioned guidance to integrate the object into new surroundings appropriately. Extensive evaluations across various metrics demonstrate the superior performance of our method, which is further validated by a comprehensive user study
AE-NeRF: Augmenting Event-Based Neural Radiance Fields for Non-ideal Conditions and Larger Scenes
No full textCompared to frame-based methods, computational neuromorphic imaging using event cameras offers significant advantages, such as minimal motion blur, enhanced temporal resolution, and high dynamic range. The multi-view consistency of Neural Radiance Fields combined with the unique benefits of event cameras, has spurred recent research into reconstructing NeRF from data captured by moving event cameras. While showing impressive performance, existing methods rely on ideal conditions with the availability of uniform and high-quality event sequences and accurate camera poses, and mainly focus on the object level reconstruction, thus limiting their practical applications. In this work, we propose AE-NeRF to address the challenges of learning event-based NeRF from non-ideal conditions, including non-uniform event sequences, noisy poses, and various scales of scenes. Our method exploits the density of event streams and jointly learn a pose correction module with an event-based NeRF (e-NeRF) framework for robust 3D reconstruction from inaccurate camera poses. To generalize to larger scenes, we propose hierarchical event distillation with a proposal e-NeRF network and a vanilla e-NeRF network to resample and refine the reconstruction process. We further propose an event reconstruction loss and a temporal loss to improve the view consistency of the reconstructed scene. We established a comprehensive benchmark that includes large-scale scenes to simulate practical non-ideal conditions, incorporating both synthetic and challenging real-world event datasets. The experimental results show that our method achieves a new state-of-the-art in event-based 3D reconstruction
DFDNet: Disentangling and Filtering Dynamics for Enhanced Video Prediction
No full textVideos inherently contain complex temporal dynamics across various spatial directions, often entangled in ways that obscure effective dynamic extraction. Previous studies typically process video spatiotemporal features without disentangling, which hampers their ability to extract dynamic information. Additionally, the extraction of dynamics is disrupted by transient high-dynamic information in video sequences, e.g., noise or flicker, which has received limited attention in the literature. To tackle those problems, this paper proposes the Disentangling and Filtering Dynamics Network (DFDNet). Firstly, to disentangle the interwoven dynamics, DFDNet decomposes the spatially encoded video sequences into lower dimensional sequences. Secondly, a learnable threshold filter is proposed to eliminate the transient high-dynamic information. Thirdly, the model incorporates an MLP to extract the temporal dependencies from the disentangled and filtered sequences. DFDNet demonstrates competitive performance across four chosen datasets, including both low and high-resolution videos. Specifically, on the low-resolution Moving MNIST dataset, DFDNet achieves a 19% improvement on MSE over the previous state-of-the-art model. On the high-resolution SJTU4K dataset, it outperforms the previous state-of-the-art model by 10% on the LPIPS metric under similar inference time
Learning 2D Invariant Affordance Knowledge for 3D Affordance Grounding
No full text3D Object Affordance Grounding aims to predict the functional regions on a 3D object and has laid the foundation for a wide range of applications in robotics. Recent advances tackle this problem via learning a mapping between 3D regions and a single human-object interaction image. However, the geometric structure of the 3D object and the object in the human-object interaction image are not always consistent, leading to poor generalization. To address this issue, we propose to learn generalizable invariant affordance knowledge from multiple human-object interaction images within the same affordance category. Specifically, we introduce the Multi-Image Guided Invariant-Feature-Aware 3D Affordance Grounding (MIFAG) framework. It grounds 3D object affordance regions by identifying common interaction patterns across multiple human-object interaction images. First, the Invariant Affordance Knowledge Extraction Module (IAM) utilizes an iterative updating strategy to gradually extract aligned affordance knowledge from multiple images and integrate it into an affordance dictionary. Then, the Affordance Dictionary Adaptive Fusion Module (ADM) learns comprehensive point cloud representations that consider all affordance candidates in multiple images. Besides, the Multi-Image and Point Affordance (MIPA) benchmark is constructed and our method outperforms existing state-of-the-art methods on various experimental comparisons
Domain Generalized Medical Landmark Detection via Robust Boundary-Aware Pre-Training
No full textIn recent years, deep learning has revenue in automated medical landmark detection. Nonetheless, prevailing research in this field predominantly addresses single-center scenarios or domain adaptation settings. In practical environments, the acquisition of multi-center data faces privacy concerns, coupled with the time-intensive and costly nature of data collection and annotation. These challenges substantially impede the broader application of deep learning-based medical landmark detection. To mitigate these issues, we propose a novel domain-generalized medical landmark detection framework that relies solely on single-center data for training. Considering the availability of numerous public medical segmentation datasets, we design a simple yet effective method that utilizes single-center segmentation to enhance the domain generalization capabilities of the landmark detection task. Specifically, we introduce a novel boundary-aware pre-training approach to focus the model on regions pertinent to landmarks. To further enhance the robustness and generalization capabilities during pre-training, we have derived a mixing loss term and proved its effectiveness in theory and practice. Extensive experiments conducted on our new domain generalization benchmark for medical landmark detection demonstrate the superiority of our approach
Queryable Prototype Multiple Instance Learning with Vision-Language Models for Incremental Whole Slide Image Classification
No full textWhole Slide Image (WSI) classification has very significant applications in clinical pathology, e.g., tumor identification and cancer diagnosis. Currently, most research attention is focused on Multiple Instance Learning (MIL) using static datasets. One of the most obvious weaknesses of these methods is that they cannot efficiently preserve and utilize previously learned knowledge. With any new data arriving, classification models are required to be re-trained on both previous and current new data. To overcome this shortcoming and break through traditional vision modality, this paper proposes the first Vision-Language-based framework with Queryable Prototype Multiple Instance Learning (QPMIL-VL) specially designed for incremental WSI classification. This framework mainly consists of two information processing branches: one is for generating bag-level features by prototype-guided aggregation of instance features, while the other is for enhancing class features through a combination of class ensemble, tunable vector and class similarity loss. The experiments on four public WSI datasets demonstrate that our QPMIL-VL framework is effective for incremental WSI classification and often significantly outperforms other compared methods, achieving state-of-the-art (SOTA) performance
Cross-Spectral Gaussian Splatting with Spatial Occupancy Consistency
No full textUsing images captured by cameras with different light spectrum sensitivities, training a unified model for cross-spectral scene representation is challenging. Recent advances have shown the possibility of jointly optimizing cross-spectral relative poses and neural radiance fields using normalized cross-device coordinates. However, such method suffers from cross-spectral misalignment when collecting data asynchronously from devices and lacks the capability to render in real-time or handle large scenes. We address these issues by proposing cross-spectral Gaussian Splatting with spatial occupancy consistency, strictly aligns cross-spectral scene representation by sharing explicit Gaussian surfaces across spectra and separately optimizing each view's extrinsic using a matching-optimizing pose estimation method. Additionally, to address field-of-view differences in cross-spectral cameras, we improve the adaptive densify controller to fill non-overlapping areas. Comprehensive experiments demonstrate that SOC-GS achieves superior performance in novel view synthesis and real-time cross-spectral rendering
Towards a Comprehensive, Efficient and Promptable Anatomic Structure Segmentation Model Using 3D Whole-Body CT Scans
No full textSegment anything model (SAM) demonstrates strong generalization ability on natural image segmentation. However, its direct adaptation in medical image segmentation tasks shows significant performance drops. It also requires an excessive number of prompt points to obtain a reasonable accuracy. Although quite a few studies explore adapting SAM into medical image volumes, the efficiency of 2D adaptation methods is unsatisfactory and 3D adaptation methods are only capable of segmenting specific organs/tumors. In this work, we propose a comprehensive and scalable 3D SAM model for whole-body CT segmentation, named CT-SAM3D. Instead of adapting SAM, we propose a 3D promptable segmentation model using a (nearly) fully labeled CT dataset. To train CT-SAM3D effectively, ensuring the model's accurate responses to higher-dimensional spatial prompts is crucial, and 3D patch-wise training is required due to GPU memory constraints. Therefore, we propose two key technical developments: 1) a progressively and spatially aligned prompt encoding method to effectively encode click prompts in local 3D space; and 2) a cross-patch prompt scheme to capture more 3D spatial context, which is beneficial for reducing the editing workloads when interactively prompting on large organs. CT-SAM3D is trained using a curated dataset of 1204 CT scans containing 107 whole-body anatomies and extensively validated using five datasets, achieving significantly better results against all previous SAM-derived models