GenAI4ELab/papercli-papers huggingface dataset:
Active Visual Exploration Based on Attention-Map Entropy
Authors: Adam Pardyl; Grzegorz Rypeść; Grzegorz Kurzejamski; Bartosz Zieliński; Tomasz Trzciński
Venue: IJCAI 2023 (main)

Abstract: Active visual exploration addresses the issue of limited sensor capabilities in real-world scenarios, where successive observations are actively chosen based on the environment. To tackle this problem, we introduce a new technique called Attention-Map Entropy (AME). It leverages the internal uncertainty of the transformer-based model to determine the most informative observations. In contrast to existing solutions, it does not require additional loss components, which simplifies the training. Through experiments, which also mimic retina-like sensors, we show that such simplified training significantly improves the performance of reconstruction, segmentation and classification on publicly available datasets.
Answer Mining from a Pool of Images: Towards Retrieval-Based Visual Question Answering
Authors: Abhirama Subramanyam Penamakuri; Manish Gupta; Mithun Das Gupta; Anand Mishra
Venue: IJCAI 2023 (main)

Abstract: We study visual question answering in a setting where the answer has to be mined from a pool of relevant and irrelevant images given as a context. For such a setting, a model must first retrieve relevant images from the pool and answer the question from these retrieved images. We refer to this problem as retrieval-based visual question answering (or RETVQA in short). The RETVQA is distinctively different and more challenging than the traditionally-studied Visual Question Answering (VQA), where a given question has to be answered with a single relevant image in context. Towards solving the RETVQA task, we propose a unified Multi Image BART (MI-BART) that takes a question and retrieved images using our relevance encoder for free-form fluent answer generation. Further, we introduce the largest dataset in this space, namely RETVQA, which has the following salient features: multi-image and retrieval requirement for VQA, metadata-independent questions over a pool of heterogeneous images, expecting a mix of classification-oriented and open-ended generative answers. Our proposed framework achieves an accuracy of 76.5% and a fluency of 79.3% on the proposed dataset, namely RETVQA and also outperforms state-of-the-art methods by 4.9% and 11.8% on the image segment of the publicly available WebQA dataset on the accuracy and fluency metrics, respectively.
Contour-based Interactive Segmentation
Authors: Polina Popenova; Danil Galeev; Anna Vorontsova; Anton Konushin
Venue: IJCAI 2023 (main)

Abstract: Recent advances in interactive segmentation (IS) allow speeding up and simplifying image editing and labeling greatly. The majority of modern IS approaches accept user input in the form of clicks. However, using clicks may require too many user interactions, especially when selecting small ob- jects, minor parts of an object, or a group of ob- jects of the same type. In this paper, we consider such a natural form of user interaction as a loose contour, and introduce a contour-based IS method. We evaluate the proposed method on the standard segmentation benchmarks, our novel UserContours dataset, and its subset UserContours-G containing difficult segmentation cases. Through experiments, we demonstrate that a single contour provides the same accuracy as multiple clicks, thus reducing the required amount of user interactions.
XFormer: Fast and Accurate Monocular 3D Body Capture
Authors: Lihui Qian; Xintong Han; Faqiang Wang; Hongyu Liu; Haoye Dong; Zhiwen Li; Huawei Wei; Zhe Lin; Cheng-Bin Jin
Venue: IJCAI 2023 (main)

Abstract: We present XFormer, a novel human mesh and motion capture method that achieves real-time performance on consumer CPUs given only monocular images as input. The proposed network architecture contains two branches: a keypoint branch that estimates 3D human mesh vertices given 2D keypoints, and an image branch that makes prediction directly from the RGB image features. At the core of our method is a cross-modal transformer block that allows information flow across these two branches by modeling the attention between 2D keypoint coordinates and image spatial features. Our architecture is smartly designed, which enables us to train on various types of datasets including images with 2D/3D annotations, images with 3D pseudo labels, and motion capture datasets that do not have associated images. This effectively improves the accuracy and generalization ability of our system. Built on a lightweight backbone (MobileNetV3), our method runs blazing fast (over 30fps on a single CPU core) and still yields competitive accuracy. Furthermore, with a HRNet backbone, XFormer delivers state-of-the-art performance on Huamn3.6 and 3DPW datasets.
ViT-P3DE∗: Vision Transformer Based Multi-Camera Instance Association with Pseudo 3D Position Embeddings
Authors: Minseok Seo; Hyuk-Jae Lee; Xuan Truong Nguyen
Venue: IJCAI 2023 (main)

Abstract: Multi-camera instance association, which identifies identical objects among multiple objects in multi-view images, is challenging due to several harsh constraints. To tackle this problem, most studies have employed CNNs as feature extractors but often fail under such harsh constraints. Inspired by Vision Transformer (ViT), we first develop a pure ViT-based framework for robust feature extraction through self-attention and residual connection. We then propose two novel methods to achieve robust feature learning. First, we introduce learnable pseudo 3D position embeddings (P3DEs) that represent the 3D location of an object in the world coordinate system, which is independent of the harsh constraints. To generate P3DEs, we encode the camera ID and the object's 2D position in the image using embedding tables. We then build a framework that trains P3DEs to represent an object's 3D position in a weakly supervised manner. Second, we also utilize joint patch generation (JPG). During patch generation, JPG considers an object and its surroundings as a single input patch to reinforce the relationship information between two features. Ultimately, experimental results demonstrate that both ViT-P3DE and ViT-P3DE with JPG achieve state-of-the-art performance and significantly outperform existing works, especially when dealing with extremely harsh constraints.
Teaching What You Should Teach: A Data-Based Distillation Method
Authors: Shitong Shao; Huanran Chen; Zhen Huang; Linrui Gong; Shuai Wang; Xinxiao Wu
Venue: IJCAI 2023 (main)

Abstract: In real teaching scenarios, an excellent teacher always teaches what he (or she) is good at but the student is not. This gives the student the best assistance in making up for his (or her) weaknesses and becoming a good one overall. Enlightened by this, we introduce the "Teaching what you Should Teach" strategy into a knowledge distillation framework, and propose a data-based distillation method named "TST" that searches for desirable augmented samples to assist in distilling more efficiently and rationally. To be specific, we design a neural network-based data augmentation module with priori bias to find out what meets the teacher's strengths but the student's weaknesses, by learning magnitudes and probabilities to generate suitable data samples. By training the data augmentation module and the generalized distillation paradigm alternately, a student model is learned with excellent generalization ability. To verify the effectiveness of our method, we conducted extensive comparative experiments on object recognition, detection, and segmentation tasks. The results on the CIFAR-100, ImageNet-1k, MS-COCO, and Cityscapes datasets demonstrate that our method achieves state-of-the-art performance on almost all teacher-student pairs. Furthermore, we conduct visualization studies to explore what magnitudes and probabilities are needed for the distillation process.
Learning Prototype Classifiers for Long-Tailed Recognition
Authors: Saurabh Sharma; Yongqin Xian; Ning Yu; Ambuj Singh
Venue: IJCAI 2023 (main)

Abstract: The problem of long-tailed recognition (LTR) has received attention in recent years due to the fundamental power-law distribution of objects in the real-world. Most recent works in LTR use softmax classifiers that are biased in that they correlate classifier norm with the amount of training data for a given class. In this work, we show that learning prototype classifiers addresses the biased softmax problem in LTR. Prototype classifiers can deliver promising results simply using Nearest-Class-Mean (NCM), a special case where prototypes are empirical centroids. We go one step further and propose to jointly learn prototypes by using distances to prototypes in representation space as the logit scores for classification. Further, we theoretically analyze the properties of Euclidean distance based prototype classifiers that lead to stable gradient-based optimization which is robust to outliers. To enable independent distance scales along each channel, we enhance Prototype classifiers by learning channel-dependent temperature parameters. Our analysis shows that prototypes learned by Prototype classifiers are better separated than empirical centroids. Results on four LTR benchmarks show that Prototype classifier outperforms or is comparable to state-of-the-art methods. Our code is made available at https://github.com/saurabhsharma1993/prototype-classifier-ltr.
Divide Rows and Conquer Cells: Towards Structure Recognition for Large Tables
Authors: Huawen Shen; Xiang Gao; Jin Wei; Liang Qiao; Yu Zhou; Qiang Li; Zhanzhan Cheng
Venue: IJCAI 2023 (main)

Abstract: Recent advanced Table Structure Recognition (TSR) models adopt image-to-text solutions to parse table structure. These methods can be formulated as image caption problem, i.e., input a single-table image and output table structure description in a specific text format, e.g., HTML. With the impressive success of Transformer in text generation tasks, these methods use Transformer architecture to predict HTML table text in an autoregressive manner. However, tables always emerge with a large variety of shapes and sizes. Autoregressive models usually suffer from the error accumulation problem as the length of predicted text increases, which results in unsatisfactory performance for large tables. In this paper, we propose a novel image-to-text based TSR method that relieves error accumulation problems and improves performance noticeably. At the core of our method is a cascaded two-step decoder architecture with the former decoder predicting HTML table row tags non-autoregressively and the latter predicting HTML table cell tags of each row in a semi-autoregressive manner. Compared with existing methods that predict HTML text autoregressively, the superiority of our row-to-cell progressive table parsing is twofold: (1) it generates an HTML tag sequence with a vertical-and-horizontal two-step `scanning', which better fits the inherent 2D structure of image data, (2) it performs substantially better for large tables (long sequence prediction) since it alleviates error accumulation problem specific to autoregressive models. Extensive experiments demonstrate that our method achieves competitive performance on three public benchmarks.
Data Level Lottery Ticket Hypothesis for Vision Transformers
Authors: Xuan Shen; Zhenglun Kong; Minghai Qin; Peiyan Dong; Geng Yuan; Xin Meng; Hao Tang; Xiaolong Ma; Yanzhi Wang
Venue: IJCAI 2023 (main)

Abstract: The conventional lottery ticket hypothesis (LTH) claims that there exists a sparse subnetwork within a dense neural network and a proper random initialization method, called the winning ticket, such that it can be trained from scratch to almost as good as the dense counterpart. Meanwhile, the research of LTH in vision transformers (ViTs) is scarcely evaluated. In this paper, we first show that the conventional winning ticket is hard to find at weight level of ViTs by existing methods. Then, we generalize the LTH for ViTs to input data consisting of image patches inspired by the input dependence of ViTs. That is, there exists a subset of input image patches such that a ViT can be trained from scratch by using only this subset of patches and achieve similar accuracy to the ViTs trained by using all image patches. We call this subset of input patches the winning tickets, which represent a significant amount of information in the input data. We use a ticket selector to generate the winning tickets based on the informativeness of patches for various types of ViT, including DeiT, LV-ViT, and Swin Transformers. The experiments show that there is a clear difference between the performance of models trained with winning tickets and randomly selected subsets, which verifies our proposed theory. We elaborate the analogical similarity between our proposed Data-LTH-ViTs and the conventional LTH for further verifying the integrity of our theory. The Source codes are available at https://github.com/shawnricecake/vit-lottery-ticket-input.
Discrepancy-Guided Reconstruction Learning for Image Forgery Detection
Authors: Zenan Shi; Haipeng Chen; Long Chen; Dong Zhang
Venue: IJCAI 2023 (main)

Abstract: In this paper, we propose a novel image forgery detection paradigm for boosting the model learning capacity on both forgery-sensitive and genuine compact visual patterns. Compared to the existing methods that only focus on the discrepant-specific patterns (\eg, noises, textures, and frequencies), our method has a greater generalization. Specifically, we first propose a Discrepancy-Guided Encoder (DisGE) to extract forgery-sensitive visual patterns. DisGE consists of two branches, where the mainstream backbone branch is used to extract general semantic features, and the accessorial discrepant external attention branch is used to extract explicit forgery cues. Besides, a Double-Head Reconstruction (DouHR) module is proposed to enhance genuine compact visual patterns in different granular spaces. Under DouHR, we further introduce a Discrepancy-Aggregation Detector (DisAD) to aggregate these genuine compact visual patterns, such that the forgery detection capability on unknown patterns can be improved. Extensive experimental results on four challenging datasets validate the effectiveness of our proposed method against state-of-the-art competitors.
Depth-Relative Self Attention for Monocular Depth Estimation
Authors: Kyuhong Shim; Jiyoung Kim; Gusang Lee; Byonghyo Shim
Venue: IJCAI 2023 (main)

Abstract: Monocular depth estimation is very challenging because clues to the exact depth are incomplete in a single RGB image. To overcome the limitation, deep neural networks rely on various visual hints such as size, shade, and texture extracted from RGB information. However, we observe that if such hints are overly exploited, the network can be biased on RGB information without considering the comprehensive view. We propose a novel depth estimation model named RElative Depth Transformer (RED-T) that uses relative depth as guidance in self-attention. Specifically, the model assigns high attention weights to pixels of close depth and low attention weights to pixels of distant depth. As a result, the features of similar depth can become more likely to each other and thus less prone to misused visual hints. We show that the proposed model achieves competitive results in monocular depth estimation benchmarks and is less biased to RGB information. In addition, we propose a novel monocular depth estimation benchmark that limits the observable depth range during training in order to evaluate the robustness of the model for unseen depths.
Acoustic NLOS Imaging with Cross Modal Knowledge Distillation
Authors: Ui-Hyeon Shin; Seungwoo Jang; Kwangsu Kim
Venue: IJCAI 2023 (main)

Abstract: Acoustic non-line-of-sight (NLOS) imaging aims to reconstruct hidden scenes by analyzing reflections of acoustic waves. Despite recent developments in the field, existing methods still have limitations such as sensitivity to noise in a physical model and difficulty in reconstructing unseen objects in a deep learning model. To address these limitations, we propose a novel cross-modal knowledge distillation (CMKD) approach for acoustic NLOS imaging. Our method transfers knowledge from a well-trained image network to an audio network, effectively combining the strengths of both modalities. As a result, it is robust to noise and superior in reconstructing unseen objects. Additionally, we evaluate real-world datasets and demonstrate that the proposed method outperforms state-of-the-art methods in acoustic NLOS imaging. The experimental results indicate that CMKD is an effective solution for addressing the limitations of current acoustic NLOS imaging methods. Our code, model, and data are available at https://github.com/shineh96/Acoustic-NLOS-CMKD.
VGOS: Voxel Grid Optimization for View Synthesis from Sparse Inputs
Authors: Jiakai Sun; Zhanjie Zhang; Jiafu Chen; Guangyuan Li; Boyan Ji; Lei Zhao; Wei Xing
Venue: IJCAI 2023 (main)

Abstract: Neural Radiance Fields (NeRF) has shown great success in novel view synthesis due to its state-of-the-art quality and flexibility. However, NeRF requires dense input views (tens to hundreds) and a long training time (hours to days) for a single scene to generate high-fidelity images. Although using the voxel grids to represent the radiance field can significantly accelerate the optimization process, we observe that for sparse inputs, the voxel grids are more prone to overfitting to the training views and will have holes and floaters, which leads to artifacts. In this paper, we propose VGOS, an approach for fast (3-5 minutes) radiance field reconstruction from sparse inputs (3-10 views) to address these issues. To improve the performance of voxel-based radiance field in sparse input scenarios, we propose two methods: (a) We introduce an incremental voxel training strategy, which prevents overfitting by suppressing the optimization of peripheral voxels in the early stage of reconstruction. (b) We use several regularization techniques to smooth the voxels, which avoids degenerate solutions. Experiments demonstrate that VGOS achieves state-of-the-art performance for sparse inputs with super-fast convergence. Code will be available at https://github.com/SJoJoK/VGOS.
Appearance Prompt Vision Transformer for Connectome Reconstruction
Authors: Rui Sun; Naisong Luo; Yuwen Pan; Huayu Mai; Tianzhu Zhang; Zhiwei Xiong; Feng Wu
Venue: IJCAI 2023 (main)

Abstract: Neural connectivity reconstruction aims to understand the function of biological reconstruction and promote basic scientific research. The intricate morphology and densely intertwined branches make it an extremely challenging task. Most previous best-performing methods adopt affinity learning or metric learning. Nevertheless, they either neglect to model explicit voxel semantics caused by implicit optimization or are hysteresis to spatial information. Furthermore, the inherent locality of 3D CNNs limits modeling long-range dependencies, leading to sub-optimal results. In this work, we propose a coherent and unified Appearance Prompt Vision Transformer (APViT) to integrate affinity and metric learning to exploit the complementarity by learning long-range spatial dependencies. The proposed APViT enjoys several merits. First, the extension continuity-aware attention module aims at constructing hierarchical attention customized for neuron extensibility and slice continuity to learn instance voxel semantic context from a global perspective and utilize continuity priors to enhance voxel spatial awareness. Second, the appearance prompt modulator is responsible for leveraging voxel-adaptive appearance knowledge conditioned on affinity rich in spatial information to instruct instance voxel semantics, exploiting the potential of affinity learning to complement metric learning. Extensive experimental results on multiple challenging benchmarks demonstrate that our APViT achieves consistent improvements with huge flexibility under the same post-processing strategy.
Domain-Adaptive Self-Supervised Face & Body Detection in Drawings
Authors: Barış Batuhan Topal; Deniz Yuret; Tevfik Metin Sezgin
Venue: IJCAI 2023 (main)

Abstract: Drawings are powerful means of pictorial abstraction and communication. Understanding diverse forms of drawings, including digital arts, cartoons, and comics, has been a major problem of interest for the computer vision and computer graphics communities. Although there are large amounts of digitized drawings from comic books and cartoons, they contain vast stylistic variations, which necessitate expensive manual labeling for training domain-specific recognizers. In this work, we show how self-supervised learning, based on a teacher-student network with a modified student network update design, can be used to build face and body detectors. Our setup allows exploiting large amounts of unlabeled data from the target domain when labels are provided for only a small subset of it. We further demonstrate that style transfer can be incorporated into our learning pipeline to bootstrap detectors using a vast amount of out-of-domain labeled images from natural images (i.e., images from the real world). Our combined architecture yields detectors with state-of-the-art (SOTA) and near-SOTA performance using minimal annotation effort. Our code can be accessed from https://github.com/barisbatuhan/DASS_Detector.
Shaken, and Stirred: Long-Range Dependencies Enable Robust Outlier Detection with PixelCNN++
Authors: Barath Mohan Umapathi; Kushal Chauhan; Pradeep Shenoy; Devarajan Sridharan
Venue: IJCAI 2023 (main)

Abstract: Reliable outlier detection is critical for real-world deployment of deep learning models. Although extensively studied, likelihoods produced by deep generative models have been largely dismissed as being impractical for outlier detection. First, deep generative model likelihoods are readily biased by low-level input statistics. Second, many recent solutions for correcting these biases are computationally expensive, or do not generalize well to complex, natural datasets. Here, we explore outlier detection with a state-of-the-art deep autoregressive model: PixelCNN++. We show that biases in PixelCNN++ likelihoods arise primarily from predictions based on local dependencies. We propose two families of bijective transformations -- ``stirring'' and ``shaking'' -- which ameliorate low-level biases and isolate the contribution of long-range dependencies to PixelCNN++ likelihoods. These transformations are inexpensive and readily computed at evaluation time. We test our approaches extensively with five grayscale and six natural image datasets and show that they achieve or exceed state-of-the-art outlier detection, particularly on datasets with complex, natural images. We also show that our solutions work well with other types of generative models (generative flows and variational autoencoders) and that their efficacy is governed by each model's reliance on local dependencies. In sum, lightweight remedies suffice to achieve robust outlier detection on image data with deep generative models.