GenAI4ELab/papercli-papers huggingface dataset:
Context-Aware Transformer for 3D Point Cloud Automatic Annotation
Authors: Xiaoyan Qian; Chang Liu; Xiaojuan Qi; Siew-Chong Tan; Edmund Lam; Ngai Wong
Venue: AAAI 2023 (main)

Abstract: 3D automatic annotation has received increased attention since manually annotating 3D point clouds is laborious. However, existing methods are usually complicated, e.g., pipelined training for 3D foreground/background segmentation, cylindrical object proposals, and point completion. Furthermore, they often overlook the inter-object feature correlation that is particularly informative to hard samples for 3D annotation. 
To this end, we propose a simple yet effective end-to-end Context-Aware Transformer (CAT) as an automated 3D-box labeler to generate precise 3D box annotations from 2D boxes, trained with a small number of human annotations. We adopt the general encoder-decoder architecture, where the CAT encoder consists of an intra-object encoder (local) and an inter-object encoder (global), performing self-attention along the sequence and batch dimensions, respectively. The former models intra-object interactions among points and the latter extracts feature relations among different objects, thus boosting scene-level understanding.
Via local and global encoders, CAT can generate high-quality 3D box annotations with a streamlined workflow, allowing it to outperform existing state-of-the-arts by up to 1.79% 3D AP on the hard task of the KITTI test set.
Data-Efficient Image Quality Assessment with Attention-Panel Decoder
Authors: Guanyi Qin; Runze Hu; Yutao Liu; Xiawu Zheng; Haotian Liu; Xiu Li; Yan Zhang
Venue: AAAI 2023 (main)

Abstract: Blind Image Quality Assessment (BIQA) is a fundamental task in computer vision, which however remains unresolved due to the complex distortion conditions and diversified image contents. To confront this challenge, we in this paper propose a novel BIQA pipeline based on the Transformer architecture, which achieves an efficient quality-aware feature representation with much fewer data. More specifically, we consider the traditional fine-tuning in BIQA as an interpretation of the pre-trained model. In this way, we further introduce a Transformer decoder to refine the perceptual information of the CLS token from different perspectives. This enables our model to establish the quality-aware feature manifold efficiently while attaining a strong generalization capability. Meanwhile, inspired by the subjective evaluation behaviors of human, we introduce a novel attention panel mechanism, which improves the model performance and reduces the prediction uncertainty simultaneously. The proposed BIQA method maintains a light-weight design with only one layer of the decoder, yet extensive experiments on eight standard BIQA datasets (both synthetic and authentic) demonstrate its superior performance to the state-of-the-art BIQA methods, i.e., achieving the SRCC values of 0.875 (vs. 0.859 in LIVEC) and 0.980 (vs. 0.969 in LIVE). Checkpoints, logs and code will be available at https://github.com/narthchin/DEIQT.
FoPro: Few-Shot Guided Robust Webly-Supervised Prototypical Learning
Authors: Yulei Qin; Xingyu Chen; Chao Chen; Yunhang Shen; Bo Ren; Yun Gu; Jie Yang; Chunhua Shen
Venue: AAAI 2023 (main)

Abstract: Recently, webly supervised learning (WSL) has been studied to leverage numerous and accessible data from the Internet. Most existing methods focus on learning noise-robust models from web images while neglecting the performance drop caused by the differences between web domain and real-world domain. However, only by tackling the performance gap above can we fully exploit the practical value of web datasets. To this end, we propose a Few-shot guided Prototypical (FoPro) representation learning method, which only needs a few labeled examples from reality and can significantly improve the performance in the real-world domain. Specifically, we initialize each class center with few-shot real-world data as the ``realistic" prototype. Then, the intra-class distance between web instances and ``realistic" prototypes is narrowed by contrastive learning. Finally, we measure image-prototype distance with a learnable metric. Prototypes are polished by adjacent high-quality web images and involved in removing distant out-of-distribution samples. In experiments, FoPro is trained on web datasets with a few real-world examples guided and evaluated on real-world datasets. Our method achieves the state-of-the-art performance on three fine-grained datasets and two large-scale datasets. Compared with existing WSL methods under the same few-shot settings, FoPro still excels in real-world generalization. Code is available at https://github.com/yuleiqin/fopro.
Exposing the Self-Supervised Space-Time Correspondence Learning via Graph Kernels
Authors: Zheyun Qin; Xiankai Lu; Xiushan Nie; Yilong Yin; Jianbing Shen
Venue: AAAI 2023 (main)

Abstract: Self-supervised space-time correspondence learning is emerging as a promising way of leveraging unlabeled video. Currently, most methods adapt contrastive learning with mining negative samples or reconstruction adapted from the image domain, which requires dense affinity across multiple frames or optical flow constraints. Moreover, video correspondence predictive models require mining more inherent properties in videos, such as structural information. In this work, we propose the VideoHiGraph, a space-time correspondence framework based on a learnable graph kernel. Concerning the video as the spatial-temporal graph, the learning objectives of VideoHiGraph are emanated in a self-supervised manner for predicting unobserved hidden graphs via graph kernel manner. We learn a representation of the temporal coherence across frames in which pairwise similarity defines the structured hidden graph, such that a biased random walk graph kernel along the sub-graph can predict long-range correspondence. Then, we learn a refined representation across frames on the node-level via a dense graph kernel. The self-supervision of the model training is formed by the structural and temporal consistency of the graph. VideoHiGraph achieves superior performance and demonstrates its robustness across the benchmark of label propagation tasks involving objects, semantic parts, keypoints, and instances. Our algorithm implementations have been made publicly available at https://github.com/zyqin19/VideoHiGraph.
Exploring Stroke-Level Modifications for Scene Text Editing
Authors: Yadong Qu; Qingfeng Tan; Hongtao Xie; Jianjun Xu; YuXin Wang; Yongdong Zhang
Venue: AAAI 2023 (main)

Abstract: Scene text editing (STE) aims to replace text with the desired one while preserving background and styles of the original text. However, due to the complicated background textures and various text styles, existing methods fall short in generating clear and legible edited text images. In this study, we attribute the poor editing performance to two problems: 1) Implicit decoupling structure. Previous methods of editing the whole image have to learn different translation rules of background and text regions simultaneously. 2) Domain gap. Due to the lack of edited real scene text images, the network can only be well trained on synthetic pairs and performs poorly on real-world images. To handle the above problems, we propose a novel network by MOdifying Scene Text image at strokE Level (MOSTEL). Firstly, we generate stroke guidance maps to explicitly indicate regions to be edited. Different from the implicit one by directly modifying all the pixels at image level, such explicit instructions filter out the distractions from background and guide the network to focus on editing rules of text regions. Secondly, we propose a Semi-supervised Hybrid Learning to train the network with both labeled synthetic images and unpaired real scene text images. Thus, the STE model is adapted to real-world datasets distributions. Moreover, two new datasets (Tamper-Syn2k and Tamper-Scene) are proposed to fill the blank of public evaluation datasets. Extensive experiments demonstrate that our MOSTEL outperforms previous methods both qualitatively and quantitatively. Datasets and code will be available at https://github.com/qqqyd/MOSTEL.
Unsupervised Deep Learning for Phase Retrieval via Teacher-Student Distillation
Authors: Yuhui Quan; Zhile Chen; Tongyao Pang; Hui Ji
Venue: AAAI 2023 (main)

Abstract: Phase retrieval (PR) is a challenging nonlinear inverse problem in scientific imaging that involves reconstructing the phase of a signal from its intensity measurements. Recently, there has been an increasing interest in deep learning-based PR. Motivated by the challenge of collecting ground-truth (GT) images in many domains, this paper proposes a fully-unsupervised learning approach for PR, which trains an end-to-end deep model via a GT-free teacher-student online distillation framework. Specifically, a teacher model is trained using a self-expressive loss with noise resistance, while a student model is trained with a consistency loss on augmented data to exploit the teacher's dark knowledge. Additionally, we develop an enhanced unfolding  network for both the teacher and student models. Extensive experiments show that our proposed approach outperforms existing unsupervised PR methods with higher computational efficiency and performs competitively against supervised methods.
A Learnable Radial Basis Positional Embedding for Coordinate-MLPs
Authors: Sameera Ramasinghe; Simon Lucey
Venue: AAAI 2023 (main)

Abstract: We propose a novel method to enhance the performance of coordinate-MLPs (also referred to as neural fields) by learning instance-specific positional embeddings. End-to-end optimization of positional embedding parameters along with network weights leads to poor generalization performance. Instead, we develop a generic framework to learn the positional embedding based on the classic graph-Laplacian regularization, which can implicitly balance the trade-off between memorization and generalization. This framework is then used to propose a novel positional embedding scheme, where the hyperparameters are learned per coordinate (i.e instance)  to deliver optimal performance. We show that the proposed embedding achieves better performance with higher stability compared to the well-established random Fourier features (RFF). Further, we demonstrate that the proposed embedding scheme yields stable gradients, enabling seamless integration into deep architectures as intermediate layers.
Action-Conditioned Generation of Bimanual Object Manipulation Sequences
Authors: Haziq Razali; Yiannis Demiris
Venue: AAAI 2023 (main)

Abstract: The generation of bimanual object manipulation sequences given a semantic action label has broad applications in collaborative robots or augmented reality. This relatively new problem differs from existing works that generate whole-body motions without any object interaction as it now requires the model to additionally learn the spatio-temporal relationship that exists between the human joints and object motion given said label. To tackle this task, we leverage the varying degree each muscle or joint is involved during object manipulation. For instance, the wrists act as the prime movers for the objects while the finger joints are angled to provide a firm grip. The remaining body joints are the least involved in that they are positioned as naturally and comfortably as possible. We thus design an architecture that comprises 3 main components: (i) a graph recurrent network that generates the wrist and object motion, (ii) an attention-based recurrent network that estimates the required finger joint angles given the graph configuration, and (iii) a recurrent network that reconstructs the body pose given the locations of the wrist. We evaluate our approach on the KIT Motion Capture and KIT RGBD Bimanual Manipulation datasets and show improvements over a simplified approach that treats the entire body as a single entity, and existing whole-body-only methods.
Mean-Shifted Contrastive Loss for Anomaly Detection
Authors: Tal Reiss; Yedid Hoshen
Venue: AAAI 2023 (main)

Abstract: Deep anomaly detection methods learn representations that separate between normal and anomalous images. Although self-supervised representation learning is commonly used, small dataset sizes limit its effectiveness. It was previously shown that utilizing external, generic datasets (e.g. ImageNet classification) can significantly improve anomaly detection performance. One approach is outlier exposure, which fails when the external datasets do not resemble the anomalies. We take the approach of transferring representations pre-trained on external datasets for anomaly detection. Anomaly detection performance can be significantly improved by fine-tuning the pre-trained representations on the normal training images. In this paper, we first demonstrate and analyze that contrastive learning, the most popular self-supervised learning paradigm cannot be naively applied to pre-trained features.  The reason is that pre-trained feature initialization causes poor conditioning for standard contrastive objectives, resulting in bad optimization dynamics. Based on our analysis, we provide a modified contrastive objective, the Mean-Shifted Contrastive Loss. Our method is highly effective and achieves a new state-of-the-art anomaly detection performance including 98.6% ROC-AUC on the CIFAR-10 dataset.
Two Heads Are Better than One: Image-Point Cloud Network for Depth-Based 3D Hand Pose Estimation
Authors: Pengfei Ren; Yuchen Chen; Jiachang Hao; Haifeng Sun; Qi Qi; Jingyu Wang; Jianxin Liao
Venue: AAAI 2023 (main)

Abstract: Depth images and point clouds are the two most commonly used data representations for depth-based 3D hand pose estimation. Benefiting from the structuring of image data and the inherent inductive biases of the 2D Convolutional Neural Network (CNN), image-based methods are highly efficient and effective. However, treating the depth data as a 2D image inevitably ignores the 3D nature of depth data. Point cloud-based methods can better mine the 3D geometric structure of depth data. However, these methods suffer from the disorder and non-structure of point cloud data, which is computationally inefficient. In this paper, we propose an Image-Point cloud Network (IPNet) for accurate and robust 3D hand pose estimation. IPNet utilizes 2D CNN to extract visual representations in 2D image space and performs iterative correction in 3D point cloud space to exploit the 3D geometry information of depth data. In particular, we propose a sparse anchor-based "aggregation-interaction-propagation'' paradigm to enhance point cloud features and refine the hand pose, which reduces irregular data access. Furthermore, we introduce a 3D hand model to the iterative correction process, which significantly improves the robustness of IPNet to occlusion and depth holes. Experiments show that IPNet outperforms state-of-the-art methods on three challenging hand datasets.
MAGIC: Mask-Guided Image Synthesis by Inverting a Quasi-robust Classifier
Authors: Mozhdeh Rouhsedaghat; Masoud Monajatipoor; C.-C. Jay Kuo; Iacopo Masi
Venue: AAAI 2023 (main)

Abstract: We offer a method for one-shot mask-guided image synthesis that allows controlling manipulations of a single image by inverting a quasi-robust classifier equipped with strong regularizers. Our proposed method, entitled MAGIC, leverages structured gradients from a pre-trained quasi-robust classifier to better preserve the input semantics while preserving its classification accuracy, thereby guaranteeing credibility in the synthesis.
Unlike current methods that use complex primitives to supervise the process or use attention maps as a weak supervisory signal, MAGIC aggregates gradients over the input, driven by a guide binary mask that enforces a strong, spatial prior. MAGIC implements a series of manipulations with a single framework achieving shape and location control, intense non-rigid shape deformations, and copy/move operations in the presence of repeating objects and gives users firm control over the synthesis by requiring to simply specify binary guide masks. 
Our study and findings are supported by various qualitative comparisons with the state-of-the-art on the same images sampled from ImageNet and quantitative analysis using machine perception along with a user survey of 100+ participants that endorse our synthesis quality.
Domain Generalised Faster R-CNN
Authors: Karthik Seemakurthy; Charles Fox; Erchan Aptoula; Petra Bosilj
Venue: AAAI 2023 (main)

Abstract: Domain generalisation (i.e. out-of-distribution generalisation)
is an open problem in machine learning, where the goal is
to train a model via one or more source domains, that will
generalise well to unknown target domains. While the topic
is attracting increasing interest, it has not been studied in
detail in the context of object detection. The established approaches
all operate under the covariate shift assumption,
where the conditional distributions are assumed to be approximately
equal across source domains. This is the first
paper to address domain generalisation in the context of object
detection, with a rigorous mathematical analysis of domain
shift, without the covariate shift assumption. We focus on
improving the generalisation ability of object detection by
proposing new regularisation terms to address the domain
shift that arises due to both classification and bounding box
regression. Also, we include an additional consistency regularisation
term to align the local and global level predictions.
The proposed approach is implemented as a Domain
Generalised Faster R-CNN and evaluated using four object
detection datasets which provide domain metadata (GWHD,
Cityscapes, BDD100K, Sim10K) where it exhibits a consistent
performance improvement over the baselines. All the
codes for replicating the results in this paper can be found at
https://github.com/karthikiitm87/domain-generalisation.git
MIDMs: Matching Interleaved Diffusion Models for Exemplar-Based Image Translation
Authors: Junyoung Seo; Gyuseong Lee; Seokju Cho; Jiyoung Lee; Seungryong Kim
Venue: AAAI 2023 (main)

Abstract: We present a novel method for exemplar-based image translation, called matching interleaved diffusion models (MIDMs). Most existing methods for this task were formulated as GAN-based matching-then-generation framework. However, in this framework, matching errors induced by the difficulty of semantic matching across cross-domain, e.g., sketch and photo, can be easily propagated to the generation step, which in turn leads to the degenerated results. Motivated by the recent success of diffusion models, overcoming the shortcomings of GANs, we incorporate the diffusion models to overcome these limitations. Specifically, we formulate a diffusion-based matching-and-generation framework that interleaves cross-domain matching and diffusion steps in the latent space by iteratively feeding the intermediate warp into the noising process and denoising it to generate a translated image. In addition, to improve the reliability of diffusion process, we design confidence-aware process using cycle-consistency to consider only confident regions during translation. Experimental results show that our MIDMs generate more plausible images than state-of-the-art methods.
JR2Net: Joint Monocular 3D Face Reconstruction and Reenactment
Authors: Jiaxiang Shang; Yu Zeng; Xin Qiao; Xin Wang; Runze Zhang; Guangyuan Sun; Vishal Patel; Hongbo Fu
Venue: AAAI 2023 (main)

Abstract: Face reenactment and reconstruction benefit various applications in self-media, VR, etc. Recent face reenactment methods use 2D facial landmarks to implicitly retarget facial expressions and poses from driving videos to source images,
while they suffer from pose and expression preservation issues for cross-identity scenarios, i.e., when the source and the driving subjects are different. Current self-supervised face reconstruction methods also demonstrate impressive results.
However, these methods do not handle large expressions well, since their training data lacks samples of large expressions, and 2D facial attributes are inaccurate on such samples. To mitigate the above problems, we propose to explore the inner connection between the two tasks, i.e., using face reconstruction to provide 
sufficient 3D information for reenactment, and synthesizing videos paired with captured face model parameters through face reenactment to enhance the
expression module of face reconstruction. In particular, we propose a novel cascade framework named JR2Net for Joint Face Reconstruction and Reenactment, which begins with the training of a coarse reconstruction network, followed by a 3D-aware face reenactment network based on the coarse reconstruction results. In the end, we train an expression tracking network based on our synthesized videos composed by image-face model parameter pairs. Such an expression tracking network can further enhance the coarse face reconstruction. Extensive experiments show that our JR2Net outperforms the state-of-the-art methods on several face reconstruction and reenactment benchmarks.
HVTSurv: Hierarchical Vision Transformer for Patient-Level Survival Prediction from Whole Slide Image
Authors: Zhuchen Shao; Yang Chen; Hao Bian; Jian Zhang; Guojun Liu; Yongbing Zhang
Venue: AAAI 2023 (main)

Abstract: Survival prediction based on whole slide images (WSIs) is a challenging task for patient-level multiple instance learning (MIL). Due to the vast amount of data for a patient (one or multiple gigapixels WSIs) and the irregularly shaped property of WSI, it is difficult to fully explore spatial, contextual, and hierarchical interaction in the patient-level bag. Many studies adopt random sampling pre-processing strategy and WSI-level aggregation models, which inevitably lose critical prognostic information in the patient-level bag. In this work, we propose a hierarchical vision Transformer framework named HVTSurv, which can encode the local-level relative spatial information, strengthen WSI-level context-aware communication, and establish patient-level hierarchical interaction. Firstly, we design a feature pre-processing strategy, including feature rearrangement and random window masking. Then, we devise three layers to progressively obtain patient-level representation, including a local-level interaction layer adopting Manhattan distance, a WSI-level interaction layer employing spatial shuffle, and a patient-level interaction layer using attention pooling. Moreover, the design of hierarchical network helps the model become more computationally efficient. Finally, we validate HVTSurv with 3,104 patients and 3,752 WSIs across 6 cancer types from The Cancer Genome Atlas (TCGA). The average C-Index is  2.50-11.30% higher than all the prior weakly supervised methods over 6 TCGA datasets. Ablation study and attention visualization further verify the superiority of the proposed HVTSurv. Implementation is available at: https://github.com/szc19990412/HVTSurv.
Channel Regeneration: Improving Channel Utilization for Compact DNNs
Authors: Ankit Sharma; Hassan Foroosh
Venue: AAAI 2023 (main)

Abstract: Overparameterized deep neural networks have redundant neurons that do not contribute to the network's accuracy. In this paper, we introduce a novel channel regeneration technique that reinvigorates these redundant channels by re-initializing its batch normalization scaling factor gamma. This re-initialization of BN gamma promotes regular weight updates during training. Furthermore, we show that channel regeneration encourages the channels to contribute equally to the learned representation and further boosts the generalization accuracy. We apply our technique at regular intervals of the training cycle to improve channel utilization. The solutions proposed in previous works either raise the total computational cost or increase the model complexity. Integrating the proposed channel regeneration technique into the training methodology of efficient architectures requires minimal effort and comes at no additional cost in size or memory. Extensive experiments on several image classification and semantic segmentation benchmarks demonstrate the effectiveness of applying the channel regeneration technique to compact architectures.