GenAI4ELab/papercli-papers huggingface dataset:
DDAE: Towards Deep Dynamic Vision BERT Pretraining
Authors: Honghao Chen; Xiangwen Kong; Xiangyu Zhang; Xin Zhao; Kaiqi Huang
Venue: AAAI 2024 (main)

Abstract: Recently, masked image modeling (MIM) has demonstrated promising prospects in self-supervised representation learning. However, existing MIM frameworks recover all masked patches equivalently, ignoring that the reconstruction difficulty of different patches can vary sharply due to their diverse distance from visible patches. In this paper, we propose a novel deep dynamic supervision to enable MIM methods to dynamically reconstruct patches with different degrees of difficulty at different pretraining phases and depths of the model. Our deep dynamic supervision helps to provide more locality inductive bias for ViTs especially in deep layers, which inherently makes up for the absence of local prior for self-attention mechanism. Built upon the deep dynamic supervision, we propose Deep Dynamic AutoEncoder (DDAE), a simple yet effective MIM framework that utilizes dynamic mechanisms for pixel regression and feature self-distillation simultaneously. Extensive experiments across a variety of vision tasks including ImageNet classification, semantic segmentation on ADE20K and object detection on COCO demonstrate the effectiveness of our approach.
Rethinking Multi-Scale Representations in Deep Deraining Transformer
Authors: Hongming Chen; Xiang Chen; Jiyang Lu; Yufeng Li
Venue: AAAI 2024 (main)

Abstract: Existing Transformer-based image deraining methods depend mostly on fixed single-input single-output U-Net architecture. In fact, this not only neglects the potentially explicit information from multiple image scales, but also lacks the capability of exploring the complementary implicit information across different scales. In this work, we rethink the multi-scale representations and design an effective multi-input multi-output framework that constructs intra- and inter-scale hierarchical modulation to better facilitate rain removal and help image restoration. We observe that rain levels reduce dramatically in coarser image scales, thus proposing to restore rain-free results from the coarsest scale to the finest scale in image pyramid inputs, which also alleviates the difficulty of model learning. Specifically, we integrate a sparsity-compensated Transformer block and a frequency-enhanced convolutional block into a coupled representation module, in order to jointly learn the intra-scale content-aware features. To facilitate representations learned at different scales to communicate with each other, we leverage a gated fusion module to adaptively aggregate the inter-scale spatial-aware features, which are rich in correlated information of rain appearances, leading to high-quality results. Extensive experiments demonstrate that our model achieves consistent gains on five benchmarks.
Unsupervised Group Re-identification via Adaptive Clustering-Driven Progressive Learning
Authors: Hongxu Chen; Quan Zhang; Jian-Huang Lai; Xiaohua Xie
Venue: AAAI 2024 (main)

Abstract: Group re-identification (G-ReID) aims to correctly associate groups with the same members captured by different cameras. However, supervised approaches for this task often suffer from the high cost of cross-camera sample labeling. Unsupervised methods based on clustering can avoid sample labeling, but the problem of member variations often makes clustering unstable, leading to incorrect pseudo-labels. To address these challenges, we propose an adaptive clustering-driven progressive learning approach (ACPL), which consists of a group adaptive clustering (GAC) module and a global dynamic prototype update (GDPU) module. Specifically, GAC designs the quasi-distance between groups, thus fully capitalizing on both individual-level and holistic information within groups. In the case of great uncertainty in intra-group members, GAC effectively minimizes the impact of non-discriminative features and reduces the noise in the model's pseudo-labels. Additionally, our GDPU devises a dynamic weight to update the prototypes and effectively mine the hard samples with complex member variations, which improves the model's robustness. Extensive experiments conducted on four popular G-ReID datasets demonstrate that our method not only achieves state-of-the-art performance on unsupervised G-ReID but also performs comparably to several fully supervised approaches.
Guiding a Harsh-Environments Robust Detector via RAW Data Characteristic Mining
Authors: Hongyang Chen; Hung-Shuo Tai; Kaisheng Ma
Venue: AAAI 2024 (main)

Abstract: Consumer-grade cameras capture the RAW physical description of a scene and then process the image signals to obtain high-quality RGB images that are faithful to human visual perception. Conventionally, dense prediction scenes require high-precision recognition of objects in RGB images. However, predicting RGB data to exhibit the expected adaptability and robustness in harsh environments can be challenging. By capitalizing on the broader color gamut and higher bit depth offered by RAW data, in this paper, we demonstrate that RAW data can significantly improve the accuracy and robustness of object detectors in harsh environments. Firstly, we propose a general Pipeline for RAW Detection (PRD), along with a preprocessing strategy tailored to RAW data. Secondly, we design the RAW Corruption Benchmark (RCB) to address the dearth of benchmarks that reflect realistic scenarios in harsh environments. Thirdly, we demonstrate the significant improvement of RAW images in object detection for low-light and corrupt scenes. Specifically, our experiments indicate that PRD (using FCOS) outperforms RGB detection by 13.9mAP on LOD-Snow without generating restored images. Finally, we introduce a new nonlinear method called Functional Regularization (FR), which can effectively mine the unique characteristics of RAW data. The code is available at https://github.com/DreamerCCC/RawMining.
CutFreq: Cut-and-Swap Frequency Components for Low-Level Vision Augmentation
Authors: Hongyang Chen; Kaisheng Ma
Venue: AAAI 2024 (main)

Abstract: Low-level vision plays a crucial role in a wide range of imaging quality and image recognition applications. However, the limited size, quality, and diversity of datasets often pose significant challenges for low-level tasks. Data augmentation is the most effective and practical way of sample expansion, but the commonly used augmentation methods in high-level tasks have limited improvement in the low-level due to the boundary effects or the non-realistic context information. In this paper, we propose the Cut-and-Swap Frequency Components (CutFreq) method for low-level vision, which aims to preserve high-level representations with directionality and improve image synthesis quality. Observing the significant frequency domain differences between reconstructed images and real ones, in CutFreq, we propose to transform the input and real images separately in the frequency domain, then define two stages for the model training process, and finally swap the specified frequency bands respectively and inversely transform to generate augmented samples. The experimental results show the superior performance of CutFreq on five low-level vision tasks. Moreover, we demonstrate the effectiveness of CutFreq in the low-data regime. Code is available at https://github.com/DreamerCCC/CutFreq.
Null Space Matters: Range-Null Decomposition for Consistent Multi-Contrast MRI Reconstruction
Authors: Jiacheng Chen; Jiawei Jiang; Fei Wu; Jianwei Zheng
Venue: AAAI 2024 (main)

Abstract: Consistency and interpretability have long been the critical issues in MRI reconstruction. While interpretability has been dramatically improved with the employment of deep unfolding networks (DUNs), current methods still suffer from inconsistencies and generate inferior anatomical structure. Especially in multi-contrast scenes, different imaging protocols often exacerbate the concerned issue. In this paper, we propose a range-null decomposition-assisted DUN architecture to ensure consistency while still providing desirable interpretability. Given the input decomposed, we argue that the inconsistency could be analytically relieved by feeding solely the null-space component into proximal mapping, while leaving the range-space counterpart fixed. More importantly, a correlation decoupling scheme is further proposed to narrow the information gap for multi-contrast fusion, which dynamically borrows isotropic features from the opponent while maintaining the modality-specific ones. Specifically, the two features are attached to different frequencies and learned individually by the newly designed isotropy encoder and anisotropy encoder. The former strives for the contrast-shared information, while the latter serves to capture the contrast-specific features. The quantitative and qualitative results show that our proposal outperforms most cutting-edge methods by a large margin. Codes will be released on https://github.com/chenjiachengzzz/RNU.
PNeSM: Arbitrary 3D Scene Stylization via Prompt-Based Neural Style Mapping
Authors: Jiafu Chen; Wei Xing; Jiakai Sun; Tianyi Chu; Yiling Huang; Boyan Ji; Lei Zhao; Huaizhong Lin; Haibo Chen; Zhizhong Wang
Venue: AAAI 2024 (main)

Abstract: 3D scene stylization refers to transform the appearance of a 3D scene to match a given style image, ensuring that images rendered from different viewpoints exhibit the same style as the given style image, while maintaining the 3D consistency of the stylized scene. Several existing methods have obtained impressive results in stylizing 3D scenes. However, the mod- els proposed by these methods need to be re-trained when applied to a new scene. In other words, their models are cou- pled with a specific scene and cannot adapt to arbitrary other scenes. To address this issue, we propose a novel 3D scene stylization framework to transfer an arbitrary style to an ar- bitrary scene, without any style-related or scene-related re- training. Concretely, we first map the appearance of the 3D scene into a 2D style pattern space, which realizes complete disentanglement of the geometry and appearance of the 3D scene and makes our model be generalized to arbitrary 3D scenes. Then we stylize the appearance of the 3D scene in the 2D style pattern space via a prompt-based 2D stylization al- gorithm. Experimental results demonstrate that our proposed framework is superior to SOTA methods in both visual qual- ity and generalization.
TagFog: Textual Anchor Guidance and Fake Outlier Generation for Visual Out-of-Distribution Detection
Authors: Jiankang Chen; Tong Zhang; Wei-Shi Zheng; Ruixuan Wang
Venue: AAAI 2024 (main)

Abstract: Out-of-distribution (OOD) detection is crucial in many real-world applications. However, intelligent models are often trained solely on in-distribution (ID) data, leading to overconfidence when misclassifying OOD data as ID classes.  In this study, we propose a new learning framework which leverage simple Jigsaw-based fake OOD data and rich semantic embeddings (`anchors') from the ChatGPT description of ID knowledge to help guide the training of the image encoder. The learning framework can be flexibly combined with existing post-hoc approaches to OOD detection, and extensive empirical evaluations on multiple OOD detection benchmarks demonstrate that rich textual representation of ID knowledge and fake OOD knowledge can well help train a visual encoder for OOD detection. With the learning framework, new state-of-the-art performance was achieved on all the benchmarks. The code is available at https://github.com/Cverchen/TagFog.
EVE: Efficient Vision-Language Pre-training with Masked Prediction and Modality-Aware MoE
Authors: Junyi Chen; Longteng Guo; Jia Sun; Shuai Shao; Zehuan Yuan; Liang Lin; Dongyu Zhang
Venue: AAAI 2024 (main)

Abstract: Building scalable vision-language models to learn from diverse, multimodal data remains an open challenge. In this paper, we introduce an Efficient Vision-languagE foundation model, namely EVE, which is one unified multimodal Transformer pre-trained solely by one unified pre-training task. Specifically, EVE encodes both vision and language within a shared Transformer network integrated with modality-aware sparse Mixture-of-Experts (MoE) modules, which capture modality-specific information by selectively switching to different experts. To unify pre-training tasks of vision and language, EVE performs masked signal modeling on image-text pairs to reconstruct masked signals, i.e., image pixels and text tokens, given visible signals. This simple yet effective pre-training objective accelerates training by 4x compared to the model pre-trained with Image-Text Contrastive and Image-Text Matching losses. Owing to the combination of the unified architecture and pre-training task, EVE is easy to scale up, enabling better downstream performance with fewer resources and faster training speed. Despite its simplicity, EVE achieves state-of-the-art performance on various vision-language downstream tasks, including visual question answering, visual reasoning, and image-text retrieval.
CaMIL: Causal Multiple Instance Learning for Whole Slide Image Classification
Authors: Kaitao Chen; Shiliang Sun; Jing Zhao
Venue: AAAI 2024 (main)

Abstract: Whole slide image (WSI) classification is a crucial component in automated pathology analysis. Due to the inherent challenges of high-resolution WSIs and the absence of patch-level labels, most of the proposed methods follow the multiple instance learning (MIL) formulation. While MIL has been equipped with excellent instance feature extractors and aggregators, it is prone to learn spurious associations that undermine the performance of the model. For example, relying solely on color features may lead to erroneous diagnoses due to spurious associations between the disease and the color of patches. To address this issue, we develop a causal MIL framework for WSI classification, effectively distinguishing between causal and spurious associations. Specifically, we use the expectation of the intervention P(Y | do(X)) for bag prediction rather than the traditional likelihood P(Y | X). By applying the front-door adjustment, the spurious association is effectively blocked, where the intervened mediator is aggregated from patch-level features. We evaluate our proposed method on two publicly available WSI datasets, Camelyon16 and TCGA-NSCLC. Our causal MIL framework shows outstanding performance and is plug-and-play, seamlessly integrating with various feature extractors and aggregators.
Multi-Prototype Space Learning for Commonsense-Based Scene Graph Generation
Authors: Lianggangxu Chen; Youqi Song; Yiqing  Cai; Jiale Lu; Yang Li; Yuan Xie; Changbo Wang; Gaoqi He
Venue: AAAI 2024 (main)

Abstract: In the domain of scene graph generation, modeling commonsense as a single-prototype representation has been typically employed to facilitate the recognition of infrequent predicates. However, a fundamental challenge lies in the large intra-class variations of the visual appearance of predicates, resulting in subclasses within a predicate class. Such a challenge typically leads to the problem of misclassifying diverse predicates due to the rough predicate space clustering. In this paper, inspired by cognitive science, we maintain multi-prototype representations for each predicate class, which can accurately find the multiple class centers of the predicate space. Technically, we propose a novel multi-prototype learning framework consisting of three main steps: prototype-predicate matching, prototype updating, and prototype space optimization. We first design a triple-level optimal transport to match each predicate feature within the same class to a specific prototype. In addition, the prototypes are updated using momentum updating to find the class centers according to the matching results. Finally, we enhance the inter-class separability of the prototype space through iterations of the inter-class separability loss and intra-class compactness loss. Extensive evaluations demonstrate that our approach significantly outperforms state-of-the-art methods on the Visual Genome dataset.
Kumaraswamy Wavelet for Heterophilic Scene Graph Generation
Authors: Lianggangxu Chen; Youqi Song; Shaohui Lin; Changbo Wang; Gaoqi He
Venue: AAAI 2024 (main)

Abstract: Graph neural networks (GNNs) has demonstrated its capabilities in the field of scene graph generation (SGG) by updating node representations from neighboring nodes. Actually it can be viewed as a form of low-pass filter in the spatial domain, which smooths node feature representation and retains commonalities among nodes. However, spatial GNNs does not work well in the case of heterophilic SGG in which fine-grained predicates are always connected to a large number of coarse-grained predicates. Blind smoothing undermines the discriminative information of the fine-grained predicates, resulting in failure to predict them accurately. To address the heterophily, our key idea is to design tailored filters by wavelet transform from the spectral domain. First, we prove rigorously that when the heterophily on the scene graph increases, the spectral energy gradually shifts towards the high-frequency part. Inspired by this observation, we subsequently propose the Kumaraswamy Wavelet Graph Neural Network (KWGNN). KWGNN leverages complementary multi-group Kumaraswamy wavelets to cover all frequency bands. Finally, KWGNN adaptively generates band-pass filters and then integrates the filtering results to better accommodate varying levels of smoothness on the graph. Comprehensive experiments on the Visual Genome and Open Images datasets show that our method achieves state-of-the-art performance.
ViT-Calibrator: Decision Stream Calibration for Vision Transformer
Authors: Lin Chen; Zhijie Jia; Lechao Cheng; Yang Gao; Jie Lei; Yijun Bei; Zunlei Feng
Venue: AAAI 2024 (main)

Abstract: A surge of interest has emerged in utilizing Transformers in diverse vision tasks owing to its formidable performance. However, existing approaches primarily focus on optimizing internal model architecture designs that often entail significant trial and error with high burdens. In this work, we propose a new paradigm dubbed Decision Stream Calibration that boosts the performance of general Vision Transformers. To achieve this, we shed light on the information propagation mechanism in the learning procedure by exploring the correlation between different tokens and the relevance coefficient of multiple dimensions. Upon further analysis, it was discovered that 1) the final decision is associated with tokens of foreground targets, while token features of foreground target will be transmitted into the next layer as much as possible, and the useless token features of background area will be eliminated gradually in the forward propagation. 2) Each category is solely associated with specific sparse dimensions in the tokens. Based on the discoveries mentioned above, we designed a two-stage calibration scheme, namely ViT-Calibrator, including token propagation calibration stage and dimension propagation calibration stage. Extensive experiments on commonly used datasets show that the proposed approach can achieve promising results.
NeRF-VPT: Learning Novel View Representations with Neural Radiance Fields via View Prompt Tuning
Authors: Linsheng Chen; Guangrun Wang; Liuchun Yuan; Keze Wang; Ken Deng; Philip H.S. Torr
Venue: AAAI 2024 (main)

Abstract: Neural Radiance Fields (NeRF) have garnered remarkable success in novel view synthesis. Nonetheless, the task of generating high-quality images for novel views persists as a critical challenge. While the existing efforts have exhibited commendable progress, capturing intricate details, enhancing textures, and achieving superior Peak Signal-to-Noise Ratio (PSNR) metrics warrant further focused attention and advancement. In this work, we propose NeRF-VPT, an innovative method for novel view synthesis to address these challenges. Our proposed NeRF-VPT employs a cascading view prompt tuning paradigm, wherein RGB information gained from preceding rendering outcomes serves as instructive visual prompts for subsequent rendering stages, with the aspiration that the prior knowledge embedded in the prompts can facilitate the gradual enhancement of rendered image quality. NeRF-VPT only requires sampling RGB data from previous stage renderings as priors at each training stage, without relying on extra guidance or complex techniques. Thus, our NeRF-VPT is plug-and-play and can be readily integrated into existing methods. By conducting comparative analyses of our NeRF-VPT against several NeRF-based approaches on demanding real-scene benchmarks, such as Realistic Synthetic 360, Real Forward-Facing, Replica dataset, and a user-captured dataset, we substantiate that our NeRF-VPT significantly elevates baseline performance and proficiently generates more high-quality novel view images than all the compared state-of-the-art methods. Furthermore, the cascading learning of NeRF-VPT introduces adaptability to scenarios with sparse inputs, resulting in a significant enhancement of accuracy for sparse-view novel view synthesis. The source code and dataset are available at https://github.com/Freedomcls/NeRF-VPT.
WebVLN: Vision-and-Language Navigation on Websites
Authors: Qi Chen; Dileepa Pitawela; Chongyang Zhao; Gengze Zhou; Hsiang-Ting Chen; Qi Wu
Venue: AAAI 2024 (main)

Abstract: Vision-and-Language Navigation (VLN) task aims to enable AI agents to accurately understand and follow natural language instructions to navigate through real-world environments, ultimately reaching specific target locations. We recognise a promising opportunity to extend VLN to a comparable navigation task that holds substantial significance in our daily lives, albeit within the virtual realm: navigating websites on the Internet. This paper proposes a new task named Vision-and-Language Navigation on Websites (WebVLN), where we use question-based instructions to train an agent, emulating how users naturally browse websites. Unlike the existing VLN task that only pays attention to vision and instruction (language), the WebVLN agent further considers underlying web-specific content like HTML, which could not be seen on the rendered web pages yet contain rich visual and textual information. Toward this goal, we contribute a dataset, WebVLN-v1, and introduce a novel approach called Website-aware VLN Network (WebVLN-Net), which is built upon the foundation of state-of-the-art VLN techniques. Experimental results show that WebVLN-Net outperforms current VLN and web-related navigation methods. We believe that the introduction of the newWebVLN task and its dataset will establish a new dimension within the VLN domain and contribute to the broader vision-and-language research community. Code is available at: https://github.com/WebVLN/WebVLN.