Canada

Point cloud completion is an indispensable task for recovering complete point clouds due to incompleteness caused by occlusion, limited sensor resolution, etc. The family of coarse-to-fine generation architectures has recently exhibited great success in point cloud completion and gradually became mainstream. In this work, we unveil one of the key ingredients behind these methods: meticulously devised feature extraction operations with explicit cross-resolution aggregation. We present Cross-Resolution Transformer that efficiently performs cross-resolution aggregation with local attention mechanisms. With the help of our recursive designs, the proposed operation can capture more scales of features than common aggregation operations, which is beneficial for capturing fine geometric characteristics. While prior methodologies have ventured into various manifestations of inter-level cross-resolution aggregation, the effectiveness of intra-level one and their combination has not been analyzed. With unified designs, Cross-Resolution Transformer can perform intra- or inter-level cross-resolution aggregation by switching inputs. We integrate two forms of Cross-Resolution Transformers into one up-sampling block for point generation, and following the coarse-to-fine manner, we construct CRA-PCN to incrementally predict complete shapes with stacked up-sampling blocks. Extensive experiments demonstrate that our method outperforms state-of-the-art methods by a large margin on several widely used benchmarks. Codes are available at https://github.com/EasyRy/CRA-PCN.

AAAI 2024

CRA-PCN: Point Cloud Completion with Intra- and Inter-level Cross-Resolution Transformers

deep neural architectures

3d computer vision

computer vision

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


ICPR 2020

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. 

Research on emergent communication has recently gained significant traction as a promising avenue for the linguistic community to unravel human language's origins and explore artificial intelligence's generalization capabilities. Current research has predominantly concentrated on recognizing qualitative patterns of object attributes(e.g., shape and color) and paid little attention to the quantitative relationship among object quantities which is known as the part of numerical concepts. The ability to generalize numerical concepts, i.e., counting and calculations with unseen quantities, is essential, as it mirrors humans' foundational abstract reasoning abilities. In this work, we introduce the NumGame, leveraging the referential game framework, forcing agents to communicate and generalize the numerical concepts effectively. Inspired by the human learning process of numbers, we present a two-stage training approach that sequentially fosters a rudimentary numerical sense followed by the ability of arithmetic calculation, ultimately aiding agents in generating semantically stable and unambiguous language for numerical concepts. The experimental results indicate the impressive generalization capabilities to unseen quantities and regularity of the language emergence from communication.

Emergent Communication for Numerical Concepts Generalization

While coresets have been growing in terms of their application, barring few exceptions, they have mostly been limited to unsupervised settings. We consider supervised classification problems, and non-decomposable evaluation measures in such settings. We show that stratified uniform sampling based coresets have excellent empirical performance that are backed by theoretical guarantees too. We focus on the F1 score and Matthews Correlation Coefficient, two widely used non-decomposable objective functions that are nontrivial to optimize for and show that uniform coresets attain a lower bound for coreset size, and have good empirical performance, comparable with ``smarter'' coreset construction strategies.

Simple Weak Coresets for Non-decomposable Classification Measures | VIDEO

Recently, instance contrastive learning achieves good results in unsupervised domain adaptation. It reduces the distances between positive samples and the anchor, increases the distances between negative samples and the anchor, and learns discriminative feature representations for target samples. However, most recent methods for identifying positive and negative samples are based on whether the pseudo-labels of samples and the pseudo-label of the anchor correspond to the same class. Due to the lack of target labels, many uncertain data are mistakenly labeled during the training process, and many low training potential data are also utilized. To address these problems, we propose Low Category Uncertainty and High Training Potential Instance Learning for Unsupervised Domain Adaptation (LUHP). We first propose a weight to measure the category uncertainty of the target sample. We can effectively filter the samples near the decision boundary through category uncertainty thresholds which are calculated by weights. Then we propose a new loss to focus on samples with high training potential. Finally, for anchors with low category uncertainty, we propose a sample reuse strategy to make the model more robust. We demonstrate the effectiveness of LUHP by showing the results of four datasets widely used in unsupervised domain adaptation.

Low Category Uncertainty and High Training Potential Instance Learning for Unsupervised Domain Adaptation

The knowledge distillation-based approaches have recently yielded state-of-the-art (SOTA) results for cross-lingual NER tasks in zero-shot scenarios. 
These approaches typically employ a teacher network trained with the rich-resource (labelled source) language to infer pseudo-soft labels for the zero-shot (unlabelled  target) language, and force a student network to approximate these pseudo labels to achieve knowledge transfer.
However, previous works have rarely discussed the issue of pseudo-label noise caused by the source-target language gap, which can mislead the training of the student network and result in negative knowledge transfer. 
This paper proposes an discrepancy and uncertainty aware Denoising Knowledge Distillation model (DenKD) to tackle this issue. 
Specifically, DenKD presents a discrepancy-aware denoising representation learning method to optimize the class representations of the target language produced by the teacher network, thus enhancing the quality of pseudo labels and reducing noisy predictions. Further, DenKD employs an uncertainty-aware denoising method to quantify the pseudo-label noise and adjust the focus of the student network on different samples during knowledge distillation, thereby mitigating the noise's adverse effects. We conduct extensive experiments on $28$  languages including not covered by the pre-trained models, and the results demonstrate the effectiveness of our   DenKD.

Discrepancy and Uncertainty Aware Denoising Knowledge Distillation for Zero-Shot Cross-Lingual Named Entity Recognition

Benefiting from instrumental global dependency modeling of self-attention (SA), transformer-based approaches have become the pivotal choices for numerous downstream visual reasoning tasks, such as visual question answering (VQA)
and referring expression comprehension (REC). However, some studies have recently suggested that SA tends to suffer from rank-collapse thereby inevitably leads to representation degradation as the transformer layer goes deeper. Inspired by social network theory, we attempt to make an analogy between social behavior and regional information interaction in SA, and harness two crucial phenomenon of structural hole and degree centrality in social network to explore the possible optimization on SA learning, which naturaly deduces two plug-and-play social-like modules. Based on structural hole, the former module allows to make information interaction in SA more structured, which effectively avoids redundant information aggregation and global feature homogenization for better rank remedy, followed by latter module to comprehensively characterize and refine the representation discrimination via considering degree centrality of regions and transitivity of relations. Without bells and whistles, our model outperforms a bunch of baselines by a noticeable margin when considering our social-like prior on five benchmarks in VQA and REC tasks, and a series of explanatory results are showcased to sufficiently reveal the social-like behaviors in SA.

Exploiting the Social-Like Prior in Transformer for Visual Reasoning

Most of existing correspondence pruning methods only concentrate on gathering the context information as much as possible while neglecting effective ways to utilize such information. In order to tackle this dilemma, in this paper we propose a novel Graph Context Transformation Network (GCT-Net) enhancing context information to conduct consensus guidance for progressive correspondence pruning. Specifically, we design the Graph Context Enhance Transformer which first generates the graph network and then transforms it into multi-branch graph contexts. Moreover, it employs self-attention and cross-attention to magnify characteristics of each graph context for emphasizing the unique as well as shared essential information. To further apply the recalibrated graph contexts to the global domain, we propose the Graph Context Guidance Transformer. This module adopts a confident-based sampling strategy to temporarily screen high-confidence vertices for guiding accurate classification by searching global consensus between screened vertices and remaining ones. The extensive experimental results on outlier removal and relative pose estimation clearly demonstrate the superior performance of GCT-Net compared to state-of-the-art methods across outdoor and indoor datasets. The source code can be found at: https://github.com/guobaoxiao/GCT-Net.

Graph Context Transformation Learning for Progressive Correspondence Pruning

Referring 3D instance segmentation is a challenging task aimed at accurately segmenting a target instance within a 3D scene based on a given referring expression. However, previous methods have overlooked the distinct roles played by different words in referring expressions. Additionally, they have failed to incorporate the positional relationship within referring expressions with the spatial correlations in 3D scenes. To alleviate these issues, we present a novel model called X-RefSeg3D, which constructs a cross-modal graph for the input 3D scene and unites textual and spatial relationships for reasoning via graph neural networks. Our approach begins by capturing object-specific text features, which are then fused with the instance features to construct a comprehensive cross-modal scene graph. Subsequently, we integrate the obtained cross-modal features into graph neural networks, leveraging the K-nearest algorithm to derive explicit instructions from expressions and factual relationships in scenes. This enables the effective capture of higher-order relationships among instances, thereby enhancing feature fusion and facilitating reasoning. Finally, the refined feature undergoes a matching module to compute the ultimate matching score. Experimental results on ScanRefer demonstrate the effectiveness of our method, surpassing previous approaches by a substantial margin of +3.67% in terms of mIOU.

X-RefSeg3D: Enhancing Referring 3D Instance Segmentation via Structured Cross-Modal Graph Neural Networks

Text-guided image manipulation has attracted significant attention recently. Prevailing techniques concentrate on image attribute editing for individual objects, however, encountering challenges when it comes to multi-object editing. The main reason is the lack of consistency constraints on the spatial layout. This work presents a multi-region guided image manipulation framework, enabling manipulation through region-level textual prompts. With MultiDiffusion as a baseline, we are dedicated to the automatic generation of a rational multi-object spatial distribution, where disparate regions are fused as a unified entity. To mitigate interference from regional fusion, we employ an off-the-shelf model (CLIP) to impose region-aware spatial guidance on multi-object manipulation. Moreover, when applied to the StableDiffusion, the presence of quality-related yet object-agnostic lengthy words hampers the manipulation. To ensure focus on meaningful object-specific words for efficient guidance and generation, we introduce a keyword selection method. Furthermore, we demonstrate a downstream application of our method for multi-region inversion, which is tailored for manipulating multiple objects in real images. Our approach, compatible with variants of Stable Diffusion models, is readily applicable for manipulating diverse objects in extensive images with high-quality generation, showing superb image control capabilities. Code is available at https://github.com/liyiming09/multi-region-guided-diffusion.

Multi-Region Text-Driven Manipulation of Diffusion Imagery

Human body orientation estimation (HBOE) aims to estimate the orientation of a human body relative to the camera’s frontal view. Despite recent advancements in this field, there still exist limitations in achieving fine-grained results. We identify certain defects and propose corresponding approaches as follows: 1). Existing datasets suffer from non-uniform angle distributions, resulting in sparse image data for certain angles. To provide comprehensive and high-quality data, we introduce RMOS (Rendered Model Orientation Set), a rendered dataset comprising 150K accurately labeled human instances with a wide range of orientations. 2). Directly using one-hot vector as labels may overlook the similarity between angle labels, leading to poor supervision.  And converting the predictions from radians to degrees enlarges the regression error. To enhance supervision, we employ Laplace smoothing to vectorize the label, which contains more information. For fine-grained predictions, we adopt weighted Smooth-L1-loss to align predictions with the smoothed-label, thus providing robust supervision. 3). Previous works ignore body-part-specific information, resulting in coarse predictions. By employing local-window self-attention, our model could utilize different body part information for more precise orientation estimations. We validate the effectiveness of our method in the benchmarks with extensive experiments and show that our method outperforms state-of-the-art. Project is available at: https://github.com/Whalesong-zrs/Towards-Fine-grained-HBOE.

Towards Fine-Grained HBOE with Rendered Orientation Set and Laplace Smoothing

Deep neural networks face limitations in the area of visual abstract reasoning. In this work, we present a neural network model that addresses the challenges posed by Raven's Progressive Matrices (RPM). Inspired by the two-stream hypothesis, we introduce the Dual-stream Reasoning Network (DRNet), which utilizes two parallel branches to capture image features. The reasoning module first learns to merge the high-level features of the same image. Then, it employs a rule extractor to handle combinations involving the 8 context images and each candidate image, extracting discrete abstract rules and utilizing an MLP to make predictions. Empirical results demonstrate that our proposed DRNet achieves state-of-the-art average performance on several RPM benchmarks. Additionally, DRNet exhibits strong generalization capabilities, even extending to different out-of-distribution scenarios. The dual streams within DRNet fulfill distinct roles, addressing local and spatial information, and are subsequently integrated into the reasoning module, leveraging the abstract rules to facilitate the execution of visual reasoning tasks. These findings suggest that the dual-stream architecture could serve as a crucial component of visual abstract reasoning.

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Emergent Communication for Numerical Concepts Generalization

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Cross-Regional Attention Network for Point Cloud Completion