Canada

Semantic segmentation is one of the tasks concerned in the field of computer vision. However, the cost of capturing large numbers of pixel-level annotations is expensive. Semi-supervised learning can utilize labeled and unlabeled data, providing new ideas for solving the problem of insufficient labeled data. In this work, we propose a data-reliability weighted multi-phase learning method for semi-supervised segmentation (RWMS). Under the framework of self-training, we train two different teacher models to evaluate the reliability of pseudo labels. By selecting reliable data at the image level and reweighting pseudo labels at the pixel level, multi-phase training is guided to focus on more reliable knowledge. Besides, we also inject strong data augmentations on unlabeled images while training. Through extensive experiments, we demonstrate that our method performs remarkably well compared to baseline methods and substantially outperforms them, more than 3% on VOC and Cityscapes.

AAAI 2024

RWMS: Reliable Weighted Multi-Phase for Semi-supervised Segmentation

ml: semi-supervised learning

cv: applications

cv: segmentation

technical paper

We are pleased to announce the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), which will be held in Vancouver, British Columbia at the Vancouver Convention Centre – West Building from 20-27 February, 2024.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-24 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

We expect for AAAI-24 to be an in-person conference – one author of all accepted papers will be expected to present work in person unless there are exceptional circumstances that prevent this.<br><br><br><br>

In order to access the AAAI-24 event page you need to register [here](https://aaai.org/aaai-conference/registration/)

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. 

Although image captioning models have made significant advancements in recent years, the majority of them heavily depend on high-quality datasets containing paired images and texts which are costly to acquire. Previous works leverage the CLIP's cross-modal association ability for image captioning, relying solely on textual information under unsupervised settings. However, not only does a modality gap exist between CLIP text and image features, but a discrepancy also arises between training and inference due to the unavailability of real-world images, which hinders the cross-modal alignment in text-only captioning. This paper proposes a novel method to address these issues by incorporating synthetic image-text pairs. A pre-trained text-to-image model is deployed to obtain images that correspond to textual data, and the pseudo features of generated images are optimized toward the real ones in the CLIP embedding space. Furthermore, textual information is gathered to represent image features, resulting in the image features with various semantics and the bridged modality gap. To unify training and inference, synthetic image features would serve as the training prefix for the language decoder, while real images are used for inference. Additionally, salient objects in images are detected as assistance to enhance the learning of modality alignment. Experimental results demonstrate that our method obtains the state-of-the-art performance on benchmark datasets.

Improving Cross-Modal Alignment with Synthetic Pairs for Text-Only Image Captioning

In the field of vanishing point detection, previous works commonly relied on extracting and clustering straight lines or classifying candidate points as vanishing points. This paper proposes a novel end-to-end framework, called VPDETR (Vanishing Point DEtection TRansformer), that views vanishing point detection as a set prediction problem, applicable to both Manhattan and non-Manhattan world datasets. By using the positional embedding of anchor points as queries in Transformer decoders and dynamically updating them layer by layer, our method is able to directly input images and output their vanishing points without the need for explicit straight line extraction and candidate points sampling. Additionally, we introduce an orthogonal loss and a cross-prediction loss to improve accuracy on the Manhattan world datasets. Experimental results demonstrate that VPDETR achieves competitive performance compared to state-of-the-art methods, without requiring post-processing.

VPDETR: End-to-End Vanishing Point DEtection TRansformers

A variety of Neural Radiance Fields (NeRF) methods have recently achieved remarkable success in high render speed. However, current accelerating methods are specialized and incompatible with various implicit methods, preventing real-time composition over various types of NeRF works. Because NeRF relies on sampling along rays, it is possible to provide general guidance for acceleration. To that end, we propose a general implicit pipeline for composing NeRF objects quickly. Our method enables the casting of dynamic shadows within or between objects using analytical light sources while allowing multiple NeRF objects to be seamlessly placed and rendered together with any arbitrary rigid transformations. Mainly, our work introduces a new surface representation known as Neural Depth Fields (NeDF) that quickly determines the spatial relationship between objects by allowing direct intersection computation between rays and implicit surfaces. It leverages an intersection neural network to query NeRF for acceleration instead of depending on an explicit spatial structure.Our proposed method is the first to enable both the progressive and interactive composition of NeRF objects. Additionally, it also serves as a previewing plugin for a range of existing NeRF works.

A General Implicit Framework for Fast NeRF Composition and Rendering | VIDEO

Facial attribute editing has garnered significant attention, yet prevailing methods struggle with achieving precise attribute manipulation while preserving irrelevant details and controlling attribute styles. This challenge primarily arises from the strong correlations between different attributes and the interplay between attributes and identity. In this paper, we propose Semantic Disentangled GAN (SDGAN), a novel method addressing this challenge. SDGAN introduces two key concepts: a semantic disentanglement generator that assigns facial representations to distinct attribute-specific editing modules, enabling the decoupling of the facial attribute editing process, and a semantic mask alignment strategy that confines attribute editing to appropriate regions, thereby avoiding undesired modifications. Leveraging these concepts, SDGAN demonstrates accurate attribute editing and achieves high-quality attribute style manipulation through both latent-guided and reference-guided manners. We extensively evaluate our method on the CelebA-HQ database, providing both qualitative and quantitative analyses. Our results establish that SDGAN significantly outperforms state-of-the-art techniques, showcasing the effectiveness of our approach. To foster reproducibility and further research, we will provide the code for our method.

SDGAN: Disentangling Semantic Manipulation for Facial Attribute Editing

Rendering photorealistic dynamic scenes has been a focus of recent research, with applications in virtual and augmented reality. While the Neural Radiance Field (NeRF) has shown remarkable rendering quality for static scenes, achieving real-time rendering of dynamic scenes remains challenging due to expansive computation for the time dimension. The incorporation of explicit-based methods, specifically voxel grids, has been proposed to accelerate the training and rendering of neural radiance fields with hybrid representation. However, employing a hybrid representation for dynamic scenes results in overfitting due to fast convergence, which can result in artifacts (e.g., floaters, noisy geometric) on novel views. To address this, we propose a compact and efficient method for dynamic neural radiance fields, namely Ced-NeRF which only require a small number of additional parameters to construct a hybrid representation of dynamic NeRF. Evaluation of dynamic scene datasets shows that our Ced-NeRF achieves fast rendering speeds while maintaining high-quality rendering results. Our method outperforms the current state-of-the-art methods in terms of quality, training and rendering speed.

Ced-NeRF: A Compact and Efficient Method for Dynamic Neural Radiance Fields

The forecasting of Multivariate Time Series (MTS) has long been an important but challenging task. Due to the non-stationary problem across long-distance time steps, previous studies primarily adopt stationarization method to attenuate the non-stationary problem of original series for better predictability. However, existed methods always adopt the stationarized series, which ignore the inherent non-stationarity, and have difficulty in modeling MTS with complex distributions due to the lack of stochasticity. To tackle these problems, we first develop a powerful hierarchical probabilistic generative module to consider the non-stationarity and stochastity characteristics within MTS, and then combine it with transformer for a well-defined variational generative dynamic model named Hierarchical Time series Variational Transformer (HTV-Trans), which recovers the intrinsic non-stationary information into temporal dependencies. Being an powerful probabilistic model, HTV-Trans is utilized to learn expressive representations of MTS and applied to the forecasting tasks. Extensive experiments on diverse datasets show the efficiency of HTV-Trans on MTS forecasting tasks.

Considering Nonstationary within Multivariate Time Series with Variational Hierarchical Transformer for Forecasting

Correspondence pruning aims to find correct matches (inliers) from an initial set of putative correspondences, which is a fundamental task for many applications. The process of finding is challenging, given the varying inlier ratios between scenes due to significant visual differences. However, the performance of the existing methods is usually limited by the problem of lacking visual cues (e.g., texture, illumination, structure) of scenes. In this paper, we propose a Visual-Spatial Fusion Transformer (VSFormer) to accurately identify inliers and recover camera poses. Firstly, we obtain highly abstract visual cues of a scene with the cross attention between local features of two-view images. Then, we model these visual cues and correspondences by a jointly visual-spatial fusion module, simultaneously embedding visual cues into correspondences for pruning. Additionally, to mine the consistency of correspondences, we also design a novel module that combines knn-based graph and transformer, effectively capturing both local and global contexts. Extensive experiments have demonstrated that the proposed VSFormer outperforms state-of-the-art methods on outdoor and indoor benchmarks.

VSFormer: Visual-Spatial Fusion Transformer for Correspondence Pruning

In the existing spectral GNNs, polynomial based methods occupy the mainstream in designing a filter through the Laplacian matrix. However, polynomial combinations factored by the Laplacian matrix naturally have limitations in message passing (e.g., over-smoothing). Furthermore, most existing spectral GNNs are based on polynomial bases, which struggle to capture the high frequency parts of the graph spectral signal. Additionally, we also find that even increasing the polynomial order does not change this situation, which means polynomial based models have a natural deficiency when facing high frequency signals.
To tackle these problems, we propose WaveNet, which aims to effectively capture the high frequency part of the graph spectral signal from the perspective of wavelet bases through reconstructing the message propagation matrix. We utilize MultiResolution Analysis (MRA) to model this question, and our proposed method can reconstruct arbitrary filters theoretically. We also conduct node classification experiments on real world graph benchmarks and achieve superior performance on most datasets.

WaveNet: Tackling Non-stationary Graph Signals via Graph Spectral Wavelets

Device-free localization (DFL) using easily-obtained Wi-Fi received signal strength (RSS) has wide real-world applications for not requiring people to carry trackable devices.
However, accurate multitarget DFL remains challenging due to the unknown number of targets, multipath interference (MPI), especially between nearby targets, and limited real-world data. 
In this study, we pioneeringly propose a transformer-based learning method with Wi-Fi RSS as input, and an attentional prior fusion module, to simultaneously locate an unknown number of people at random positions. 
To overcome the multitarget data collection challenges, we contribute a large-scale cross-domain real-simulation-augmentation training dataset with one and two real-world nearby non-person objects at limited positions and up to five simulated and augmented randomly distributed targets.
Experimental results demonstrate our method's improved accuracy, generalization ability, and robustness with fewer Wi-Fi nodes than previous methods.

Multitarget Device-Free Localization via Cross-Domain Wi-Fi RSS Training Data and Attentional Prior Fusion

Datasets often include noisy labels, but learning from them is difficult. Since mislabeled examples usually have larger loss values in training, the small-loss trick is regarded as a standard metric to identify the clean example from the training set for better performance. Nonetheless, this proposal ignores that some clean but hard-to-learn examples also generate large losses. They could be misidentified by this criterion. In this paper, we propose a new metric called the Integrated Area Margin (IAM), which is superior to the traditional small-loss trick, particularly in recognizing the clean but hard-to-learn examples. According to the IAM, we further offer the Hyperspherical Margin Weighting (HMW) approach. It is a new sample weighting strategy that restructures the importance of each example. It should be highlighted that our approach is universal and can strengthen various methods in this field. Experiments on both benchmark and real-world datasets indicate that our HMW outperforms many state-of-the-art approaches in learning with noisy label tasks. Codes are available at https://github.com/Zhangshuojackpot/HMW.

RWMS: Reliable Weighted Multi-Phase for Semi-supervised Segmentation

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Improving Cross-Modal Alignment with Synthetic Pairs for Text-Only Image Captioning

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RWMS: Reliable Weighted Multi-Phase for Semi-supervised Segmentation

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Next from AAAI 2024

Improving Cross-Modal Alignment with Synthetic Pairs for Text-Only Image Captioning

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads