GenAI4ELab/papercli-papers huggingface dataset:
One Is All: Bridging the Gap between Neural Radiance Fields Architectures with Progressive Volume Distillation
Authors: Shuangkang Fang; Weixin Xu; Heng Wang; Yi Yang; Yufeng Wang; Shuchang Zhou
Venue: AAAI 2023 (main)

Abstract: Neural Radiance Fields (NeRF) methods have proved effective as compact, high-quality and versatile representations for 3D scenes, and enable downstream tasks such as editing, retrieval, navigation, etc.  Various neural architectures are vying for the core structure of NeRF, including the plain Multi-Layer Perceptron (MLP), sparse tensors, low-rank tensors, hashtables and their compositions. Each of these representations has its particular set of trade-offs. For example, the hashtable-based representations admit faster training and rendering but their lack of clear geometric meaning hampers downstream tasks like spatial-relation-aware editing. In this paper, we propose Progressive Volume Distillation (PVD), a systematic distillation method that allows any-to-any conversions between different architectures, including MLP, sparse or low-rank tensors, hashtables and their compositions. PVD consequently empowers downstream applications to optimally adapt the neural representations for the task at hand in a post hoc fashion. The conversions are fast, as distillation is progressively performed on different levels of volume representations, from shallower to deeper. We also employ special treatment of density to deal with its specific numerical instability problem. Empirical evidence is presented to validate our method on the NeRF-Synthetic, LLFF and TanksAndTemples datasets. For example, with PVD, an MLP-based NeRF model can be distilled from a hashtable-based Instant-NGP model at a 10~20X faster speed than being trained the original NeRF from scratch, while achieving a superior level of synthesis quality. Code is available at https://github.com/megvii-research/AAAI2023-PVD.
Weakly-Supervised Semantic Segmentation for Histopathology Images Based on Dataset Synthesis and Feature Consistency Constraint
Authors: Zijie Fang; Yang Chen; Yifeng Wang; Zhi Wang; Xiangyang Ji; Yongbing Zhang
Venue: AAAI 2023 (main)

Abstract: Tissue segmentation is a critical task in computational pathology due to its desirable ability to indicate the prognosis of cancer patients. Currently, numerous studies attempt to use image-level labels to achieve pixel-level segmentation to reduce the need for fine annotations. However, most of these methods are based on class activation map, which suffers from inaccurate segmentation boundaries. To address this problem, we propose a novel weakly-supervised tissue segmentation framework named PistoSeg, which is implemented under a fully-supervised manner by transferring tissue category labels to pixel-level masks. Firstly, a dataset synthesis method is proposed based on Mosaic transformation to generate synthesized images with pixel-level masks. Next, considering the difference between synthesized and real images, this paper devises an attention-based feature consistency, which directs the training process of a proposed pseudo-mask refining module. Finally, the refined pseudo-masks are used to train a precise segmentation model for testing. Experiments based on WSSS4LUAD and BCSS-WSSS validate that PistoSeg outperforms the state-of-the-art methods. The code is released at https://github.com/Vison307/PistoSeg.
Uncertainty-Aware Image Captioning
Authors: Zhengcong Fei; Mingyuan Fan; Li Zhu; Junshi Huang; Xiaoming Wei; Xiaolin Wei
Venue: AAAI 2023 (main)

Abstract: It is well believed that the higher uncertainty in a word of the caption, the more inter-correlated context information is required to determine it. However, current image captioning methods usually consider the generation of all words in a sentence sequentially and equally. In this paper, we propose an uncertainty-aware image captioning framework, which parallelly and iteratively operates insertion of discontinuous candidate words between existing words from easy to difficult until converged. We hypothesize that high-uncertainty words in a sentence need more prior information to make a correct decision and should be produced at a later stage. The resulting non-autoregressive hierarchy makes the caption generation explainable and intuitive. Specifically, we utilize an image-conditioned bag-of-word model to measure the word uncertainty and apply a dynamic programming algorithm to construct the training pairs. During inference, we devise an uncertainty-adaptive parallel beam search technique that yields an empirically logarithmic time complexity. Extensive experiments on the MS COCO benchmark reveal that our approach outperforms the strong baseline and related methods on both captioning quality as well as decoding speed.
Unsupervised Domain Adaptation for Medical Image Segmentation by Selective Entropy Constraints and Adaptive Semantic Alignment
Authors: Wei Feng; Lie Ju; Lin Wang; Kaimin Song; Xin Zhao; Zongyuan Ge
Venue: AAAI 2023 (main)

Abstract: Generalizing a deep learning model to new domains is crucial for computer-aided medical diagnosis systems. Most existing unsupervised domain adaptation methods have made significant progress in reducing the domain distribution gap through adversarial training. However, these methods may still produce overconfident but erroneous results on unseen target images. This paper proposes a new unsupervised domain adaptation framework for cross-modality medical image segmentation. Specifically, We first introduce two data augmentation approaches to generate two sets of semantics-preserving augmented images. Based on the model's predictive consistency on these two sets of augmented images, we identify reliable and unreliable pixels. We then perform a selective entropy constraint: we minimize the entropy of reliable pixels to increase their confidence while maximizing the entropy of unreliable pixels to reduce their confidence. Based on the identified reliable and unreliable pixels, we further propose an adaptive semantic alignment module which performs class-level distribution adaptation by minimizing the distance between same class prototypes between domains, where unreliable pixels are removed to derive more accurate prototypes. We have conducted extensive experiments on the cross-modality cardiac structure segmentation task. The experimental results show that the proposed method significantly outperforms the state-of-the-art comparison algorithms. Our code and data are available at https://github.com/fengweie/SE_ASA.
SEFormer: Structure Embedding Transformer for 3D Object Detection
Authors: Xiaoyu Feng; Heming Du; Hehe Fan; Yueqi Duan; Yongpan Liu
Venue: AAAI 2023 (main)

Abstract: Effectively  preserving and encoding structure features from objects in irregular and sparse LiDAR points is a crucial challenge to 3D object detection on the point cloud. Recently, Transformer has demonstrated promising performance on many 2D and even 3D vision tasks. Compared with the fixed and rigid convolution kernels, the self-attention mechanism in Transformer can adaptively exclude the unrelated or noisy points and is thus suitable for preserving the local spatial structure in the irregular LiDAR point cloud. However, Transformer only performs a simple sum on the point features, based on the self-attention mechanism, and all the points share the same transformation for value.  A such isotropic operation cannot capture the direction-distance-oriented local structure, which is essential for 3D object detection. In this work, we propose a Structure-Embedding transFormer (SEFormer), which can not only preserve the local structure as a traditional Transformer but also have the ability to encode the local structure. Compared to the self-attention mechanism in traditional Transformer, SEFormer learns different feature transformations for value points based on the relative directions and distances to the query point. Then we propose a SEFormer-based network for high-performance 3D object detection. Extensive experiments show that the proposed architecture can achieve SOTA results on the Waymo Open Dataset, one of the most significant 3D detection benchmarks for autonomous driving. Specifically, SEFormer achieves 79.02% mAP, which is 1.2% higher than existing works. https://github.com/tdzdog/SEFormer.
Exploit Domain-Robust Optical Flow in Domain Adaptive Video Semantic Segmentation
Authors: Yuan Gao; Zilei Wang; Jiafan Zhuang; Yixin Zhang; Junjie Li
Venue: AAAI 2023 (main)

Abstract: Domain adaptive semantic segmentation aims to exploit the pixel-level annotated samples on source domain to assist the segmentation of unlabeled samples on target domain. For such a task, the key is to construct reliable supervision signals on target domain. However, existing methods can only provide unreliable supervision signals constructed by segmentation model (SegNet) that are generally domain-sensitive. In this work, we try to find a domain-robust clue to construct more reliable supervision signals. Particularly, we experimentally observe the domain-robustness of optical flow in video tasks as it mainly represents the motion characteristics of scenes. However, optical flow cannot be directly used as supervision signals of semantic segmentation since both of them essentially represent different information. To tackle this issue, we first propose a novel Segmentation-to-Flow Module (SFM) that converts semantic segmentation maps to optical flows, named the segmentation-based flow (SF), and then propose a Segmentation-based Flow Consistency (SFC) method to impose consistency between SF and optical flow, which can implicitly supervise the training of segmentation model. The extensive experiments on two challenging benchmarks demonstrate the effectiveness of our method, and it outperforms previous state-of-the-art methods with considerable performance improvement. Our code is available at https://github.com/EdenHazardan/SFC.
Scene-Level Sketch-Based Image Retrieval with Minimal Pairwise Supervision
Authors: Ce Ge; Jingyu Wang; Qi Qi; Haifeng Sun; Tong Xu; Jianxin Liao
Venue: AAAI 2023 (main)

Abstract: The sketch-based image retrieval (SBIR) task has long been researched at the instance level, where both query sketches and candidate images are assumed to contain only one dominant object. This strong assumption constrains its application, especially with the increasingly popular intelligent terminals and human-computer interaction technology. In this work, a more general scene-level SBIR task is explored, where sketches and images can both contain multiple object instances. The new general task is extremely challenging due to several factors: (i) scene-level SBIR inherently shares sketch-specific difficulties with instance-level SBIR (e.g., sparsity, abstractness, and diversity), (ii) the cross-modal similarity is measured between two partially aligned domains (i.e., not all objects in images are drawn in scene sketches), and (iii) besides instance-level visual similarity, a more complex multi-dimensional scene-level feature matching problem is imposed (including appearance, semantics, layout, etc.). Addressing these challenges, a novel Conditional Graph Autoencoder model is proposed to deal with scene-level sketch-images retrieval. More importantly, the model can be trained with only pairwise supervision, which distinguishes our study from others in that elaborate instance-level annotations (for example, bounding boxes) are no longer required. Extensive experiments confirm the ability of our model to robustly retrieve multiple related objects at the scene level and exhibit superior performance beyond strong competitors.
Causal Intervention for Human Trajectory Prediction with Cross Attention Mechanism
Authors: Chunjiang Ge; Shiji Song; Gao Huang
Venue: AAAI 2023 (main)

Abstract: Human trajectory Prediction (HTP) in complex social environments plays a crucial and fundamental role in artificial intelligence systems. Conventional methods make use of both history behaviors and social interactions to forecast future trajectories. However, we demonstrate that the social environment is a confounder that misleads the model to learn spurious correlations between history and future trajectories. To end this, we first formulate the social environment, history and future trajectory variables into a structural causal model to analyze the causalities among them. Based on causal intervention rather than conventional likelihood, we propose a Social Environment ADjustment (SEAD) method, to remove the confounding effect of the social environment. The core of our method is implemented by a Social Cross Attention (SCA) module, which is universal, simple and effective. Our method has consistent improvements on ETH-UCY datasets with three baseline models and achieves competitive performances with existing methods.
Point-Teaching: Weakly Semi-supervised Object Detection with Point Annotations
Authors: Yongtao Ge; Qiang Zhou; Xinlong Wang; Chunhua Shen; Zhibin Wang; Hao Li
Venue: AAAI 2023 (main)

Abstract: Point annotations are considerably more time-efficient than bounding box annotations. However, how to use cheap point annotations to boost the performance of semi-supervised object detection is still an open question. In this work, we present Point-Teaching, a weakly- and semi-supervised object detection framework to fully utilize the point annotations. Specifically, we propose a Hungarian-based point-matching method to generate pseudo labels for point-annotated images. We further propose multiple instance learning (MIL) approaches at the level of images and points to supervise the object detector with point annotations. Finally, we propose a simple data augmentation, named Point-Guided Copy-Paste, to reduce the impact of those unmatched points. Experiments demonstrate the effectiveness of our method on a few datasets and various data regimes. In particular, Point-Teaching outperforms the previous best method Group R-CNN by 3.1 AP with 5% fully labeled data and 2.3 AP with 30% fully labeled data on the MS COCO dataset. We believe that our proposed framework can largely lower the bar of learning accurate object detectors and pave the way for its broader applications. The code is available at https://github.com/YongtaoGe/Point-Teaching.
Progressive Multi-View Human Mesh Recovery with Self-Supervision
Authors: Xuan Gong; Liangchen Song; Meng Zheng; Benjamin Planche; Terrence Chen; Junsong Yuan; David Doermann; Ziyan Wu
Venue: AAAI 2023 (main)

Abstract: To date, little attention has been given to multi-view 3D human mesh estimation, despite real-life applicability (e.g., motion capture, sport analysis) and robustness to single-view ambiguities. Existing solutions typically suffer from poor generalization performance to new settings, largely due to the limited diversity of image/3D-mesh pairs in multi-view training data. To address this shortcoming, people have explored the use of synthetic images. But besides the usual impact of visual gap between rendered and target data, synthetic-data-driven multi-view estimators also suffer from overfitting to the camera viewpoint distribution sampled during training which usually differs from real-world distributions. Tackling both challenges, we propose a novel simulation-based training pipeline for multi-view human mesh recovery, which (a) relies on intermediate 2D representations which are more robust to synthetic-to-real domain gap; (b) leverages learnable calibration and triangulation to adapt to more diversified camera setups; and (c) progressively aggregates multi-view information in a canonical 3D space to remove ambiguities in 2D representations. Through extensive benchmarking, we demonstrate the superiority of the proposed solution especially for unseen in-the-wild scenarios.
Incremental Image De-raining via Associative Memory
Authors: Yi Gu; Chao Wang; Jie Li
Venue: AAAI 2023 (main)

Abstract: While deep learning models have achieved the state-of-the-art performance on single-image rain removal, most methods only consider learning fixed mapping rules on the single synthetic dataset for lifetime. This limits the real-life application as iterative optimization may change mapping rules and training samples. However, when models learn a sequence of datasets in multiple incremental steps, they are susceptible to catastrophic forgetting that adapts to new incremental episodes while failing to preserve previously acquired mapping rules. In this paper, we argue the importance of sample diversity in the episodes on the iterative optimization, and propose a novel memory management method, Associative Memory, to achieve incremental image de-raining. It bridges connections between current and past episodes for feature reconstruction by sampling domain mappings of past learning steps, and guides the learning to trace the current pathway back to the historical environment without storing extra data. Experiments demonstrate that our method can achieve better performance than existing approaches on both inhomogeneous and incremental datasets within the spectrum of highly compact systems.
Flexible 3D Lane Detection by Hierarchical Shape Matching
Authors: Zhihao Guan; Ruixin Liu; Zejian Yuan; Ao Liu; Kun Tang; Tong Zhou; Erlong Li; Chao Zheng; Shuqi Mei
Venue: AAAI 2023 (main)

Abstract: As one of the basic while vital technologies for HD map construction, 3D lane detection is still an open problem due to varying visual conditions, complex typologies, and strict demands for precision. In this paper, an end-to-end flexible and hierarchical lane detector is proposed to precisely predict 3D lane lines from point clouds. Specifically, we design a hierarchical network predicting flexible representations of lane shapes at different levels, simultaneously collecting global instance semantics and avoiding local errors. In the global scope, we propose to regress parametric curves w.r.t adaptive axes that help to make more robust predictions towards complex scenes, while in the local vision the structure of lane segment is detected in each of the dynamic anchor cells sampled along the global predicted curves. Moreover, corresponding global and local shape matching losses and anchor cell generation strategies are designed. Experiments on two datasets show that we overwhelm current top methods under high precision standards, and full ablation studies also verify each part of our method. Our codes will be released at https://github.com/Doo-do/FHLD.
Underwater Ranker: Learn Which Is Better and How to Be Better
Authors: Chunle Guo; Ruiqi Wu; Xin Jin; Linghao Han; Weidong Zhang; Zhi Chai; Chongyi Li
Venue: AAAI 2023 (main)

Abstract: In this paper, we present a ranking-based underwater image quality assessment (UIQA) method, abbreviated as URanker. The URanker is built on the efficient conv-attentional image Transformer. In terms of underwater images, we specially devise (1) the histogram prior that embeds the color distribution of an underwater image as histogram token to attend global degradation and (2) the dynamic cross-scale correspondence to model local degradation. The final prediction depends on the class tokens from different scales, which comprehensively considers multi-scale dependencies. With the margin ranking loss, our URanker can accurately rank the order of underwater images of the same scene enhanced by different underwater image enhancement (UIE) algorithms according to their visual quality. To achieve that, we also contribute a dataset, URankerSet, containing sufficient results enhanced by different UIE algorithms and the corresponding perceptual rankings, to train our URanker. Apart from the good performance of URanker, we found that a simple U-shape UIE network can obtain promising performance when it is coupled with our pre-trained URanker as additional supervision. In addition, we also propose a normalization tail that can significantly improve the performance of UIE networks. Extensive experiments demonstrate the state-of-the-art performance of our method. The key designs of our method are discussed. Our code and dataset are available at https://li-chongyi.github.io/URanker_files/.
ShadowFormer: Global Context Helps Shadow Removal
Authors: Lanqing Guo; Siyu Huang; Ding Liu; Hao Cheng; Bihan Wen
Venue: AAAI 2023 (main)

Abstract: Recent deep learning methods have achieved promising results in image shadow removal. However, most of the existing approaches focus on working locally within shadow and non-shadow regions, resulting in severe artifacts around the shadow boundaries as well as inconsistent illumination between shadow and non-shadow regions. It is still challenging for the deep shadow removal model to exploit the global contextual correlation between shadow and non-shadow regions. In this work, we first propose a Retinex-based shadow model, from which we derive a novel transformer-based network, dubbed ShandowFormer, to exploit non-shadow regions to help shadow region restoration. A multi-scale channel attention framework is employed to hierarchically capture the global information. Based on that, we propose a Shadow-Interaction Module (SIM) with Shadow-Interaction Attention (SIA) in the bottleneck stage to effectively model the context correlation between shadow and non-shadow regions. We conduct extensive experiments on three popular public datasets, including ISTD, ISTD+, and SRD, 
to evaluate the proposed method. Our method achieves state-of-the-art performance by using up to 150X fewer model parameters.
RAFaRe: Learning Robust and Accurate Non-parametric 3D Face Reconstruction from Pseudo 2D&3D Pairs
Authors: Longwei Guo; Hao Zhu; Yuanxun Lu; Menghua Wu; Xun Cao
Venue: AAAI 2023 (main)

Abstract: We propose a robust and accurate non-parametric method for single-view 3D face reconstruction (SVFR). While tremendous efforts have been devoted to parametric SVFR, a visible gap still lies between the result 3D shape and the ground truth. We believe there are two major obstacles: 1) the representation of the parametric model is limited to a certain face database; 2) 2D images and 3D shapes in the fitted datasets are distinctly misaligned. To resolve these issues, a large-scale pseudo 2D&3D dataset is created by first rendering the detailed 3D faces, then swapping the face in the wild images with the rendered face. These pseudo 2D&3D pairs are created from publicly available datasets which eliminate the gaps between 2D and 3D data while covering diverse appearances, poses, scenes, and illumination. We further propose a non-parametric scheme to learn a well-generalized SVFR model from the created dataset, and the proposed hierarchical signed distance function turns out to be effective in predicting middle-scale and small-scale 3D facial geometry. Our model outperforms previous methods on FaceScape-wild/lab and MICC benchmarks and is well generalized to various appearances, poses, expressions, and in-the-wild environments. The code is released at https://github.com/zhuhao-nju/rafare.