GenAI4ELab/papercli-papers huggingface dataset:
AK4Prompts: Aesthetics-driven Automatically Keywords-Ranking for Prompts in Text-To-Image Models
Authors: Haiyang Zhang; Mengchao Wang; Shuai He; Anlong Ming
Venue: IJCAI 2024 (main)

Abstract: Current text-to-image synthesis (TIS) models have demonstrated the ability to generate high-fidelity images based on textual prompts. However, the efficacy of these models heavily relies on the keywords present in the prompts, and there is a dearth of objective analysis regarding how different keywords impact the ultimate quality of generated results. Therefore, manual evaluation becomes necessary but limited and inefficient to ascertain the role played by keywords. In this paper, we propose automated keywords-ranking for prompts (AK4Prompts), a keyword evaluation model based on mainstream TIS models that explicitly quantifies the multidimensional impact of various keywords on image generation based on prompts. To enable personalized keyword evaluation based on prompt content, we propose decoupling the latent representations of keywords and prompts in TIS models, followed by integrating the semantic features of prompts into keywords. For quantitative and multidimensional evaluation, we align the fused features of keywords using HPSv2, aesthetic score, and CLIP score, each representing distinct factors contributing to keyword impact. Our AK4Prompts can flexibly and automatically select the keywords that best match the original prompt based on individual user preferences. Extensive experimental results show the superiority of AK4Prompts to improve the quality of generated images significantly over strong baselines. Our approach not only enhances usability and user experience but also addresses the current gap in automated analysis and evaluation of keyword effects. Our code is availableat https://github.com/mRobotit/AK4Prompts.
Detector Collapse: Backdooring Object Detection to Catastrophic Overload or Blindness in the Physical World
Authors: Hangtao Zhang; Shengshan Hu; Yichen Wang; Leo Yu Zhang; Ziqi Zhou; Xianlong Wang; Yanjun Zhang; Chao Chen
Venue: IJCAI 2024 (main)

Abstract: Object detection tasks, crucial in safety-critical systems like autonomous driving, focus on pinpointing object locations. These detectors are known to be susceptible to backdoor attacks. However, existing backdoor techniques have primarily been adapted from classification tasks, overlooking deeper vulnerabilities specific to object detection. This paper is dedicated to bridging this gap by introducing Detector Collapse (DC), a brand-new backdoor attack paradigm tailored for object detection. DC is designed to instantly incapacitate detectors (i.e., severely impairing detector's performance and culminating in a denial-of-service). To this end, we develop two innovative attack schemes: Sponge for triggering widespread misidentifications and Blinding for rendering objects invisible. Remarkably, we introduce a novel poisoning strategy exploiting natural objects, enabling DC to act as a practical backdoor in real-world environments. Our experiments on different detectors across several benchmarks show a significant improvement (~10%-60% absolute and ~2-7x relative) in attack efficacy over state-of-the-art attacks.
DANCE: Dual-View Distribution Alignment for Dataset Condensation
Authors: Hansong Zhang; Shikun Li; Fanzhao Lin; Weiping Wang; Zhenxing Qian; Shiming Ge
Venue: IJCAI 2024 (main)

Abstract: Dataset condensation addresses the problem of data burden by learning a small synthetic training set that preserves essential knowledge from the larger real training set. To date, the state-of-the-art (SOTA) results are often yielded by optimization-oriented methods, but their inefficiency hinders their application to realistic datasets. On the other hand, the Distribution-Matching (DM) methods show remarkable efficiency but sub-optimal results compared to optimization-oriented methods. In this paper, we reveal the limitations of current DM-based methods from the inner-class and inter-class views, i.e., Persistent Training and Distribution Shift. To address these problems, we propose a new DM-based method named Dual-view distribution AligNment for dataset CondEnsation (DANCE), which exploits a few pre-trained models to improve DM from both inner-class and inter-class views. Specifically, from the inner-class view, we construct multiple ``mid encoders'' to perform pseudo long-term distribution alignment, making the condensed set a good proxy of the real one during the whole training process; while from the inter-class view, we use the expert models to perform distribution calibration, ensuring the synthetic data remains in the real class region during condensing. Experiments demonstrate the proposed method achieves a SOTA performance while maintaining comparable efficiency with the original DM across various scenarios. Source codes are available at https://github.com/Hansong-Zhang/DANCE.
Shap-Mix: Shapley Value Guided Mixing for Long-Tailed Skeleton Based Action Recognition
Authors: Jiahang Zhang; Lilang Lin; Jiaying Liu
Venue: IJCAI 2024 (main)

Abstract: In real-world scenarios, human actions often fall into a long-tailed distribution. It makes the existing skeleton-based action recognition works, which are mostly designed based on balanced datasets, suffer from a sharp performance degradation. Recently, many efforts have been made to image/video long-tailed learning. However, directly applying them to skeleton data can be sub-optimal due to the lack of consideration of the crucial spatial-temporal motion patterns, especially for some modality-specific methodologies such as data augmentation. To this end, considering the crucial role of the body parts in the spatially concentrated human actions, we attend to the mixing augmentations and propose a novel method, Shap-Mix, which improves long-tailed learning by mining representative motion patterns for tail categories. Specifically, we first develop an effective spatial-temporal mixing strategy for the skeleton to boost representation quality. Then, the employed saliency guidance method is presented, consisting of the saliency estimation based on Shapley value and a tail-aware mixing policy. It preserves the salient motion parts of minority classes in mixed data, explicitly establishing the relationships between crucial body structure cues and high-level semantics. Extensive experiments on three large-scale skeleton datasets show our remarkable performance improvement under both long-tailed and balanced settings. Our project is publicly available at: https://jhang2020.github.io/Projects/Shap-Mix/Shap-Mix.html.
Sparse Multi-Relational Graph Convolutional Network for Multi-type Object Trajectory Prediction
Authors: Jianhui Zhang; Jun Yao; Liqi Yan; Yanhong Xu; Zheng Wang
Venue: IJCAI 2024 (main)

Abstract: Object trajectory prediction is a hot research issue with wide applications in video surveillance and autonomous driving. The previous studies consider the interaction sparsity mainly among the pedestrians instead of multi-type of objects, which brings new types of interactions and consequently superfluous ones. This paper proposes a Multi-type Object Trajectory Prediction (MOTP) method with a Sparse Multi-relational Graph Convolutional Network (SMGCN) and a novel multi-round Global Temporal Aggregation (GTA). MOTP introduces a novel adaptive sparsification and multi-scale division method to model interactions among multitype of objects. It further incorporates a Sparse Multi-relational Temporal Graph to capture the temporal division of multi-type trajectories, along with a multi-round Global Temporal Aggregation (GTA) mechanism to mitigate error accumulation, and enhances the trajectory prediction accuracy. The extensive evaluation on the ETH, UCY and SDD datasets shows that our method outperforms the typical state-of-the-art works by significant margins. Codes will be available in https://github.com/ sounio/SMGCN.
3DBench: A Scalable 3D Benchmark and Instruction-Tuning Dataset
Authors: Junjie Zhang; Tianci Hu; Xiaoshui Huang; Yongshun Gong; Dan Zeng
Venue: IJCAI 2024 (main)

Abstract: Evaluating the performance of Multi-modal Large Language Models (MLLMs), integrating both point cloud and language, presents significant challenges. The lack of a comprehensive assessment hampers determining whether these models truly represent advancements, thereby impeding further progress in the field. Current evaluations heavily rely on classification and caption tasks, falling short in providing a thorough assessment of MLLMs. A pressing need exists for a more sophisticated evaluation method capable of thoroughly analyzing the spatial understanding and expressive capabilities of these models. To address these issues, we introduce a scalable 3D benchmark, accompanied by a large-scale instruction-tuning dataset known as 3DBench, providing an extensible platform for a comprehensive evaluation of MLLMs. Specifically, we establish the benchmark that spans a wide range of spatial and semantic scales, from object-level to scene-level, addressing both perception and planning tasks. Furthermore, we present a rigorous pipeline for automatically constructing scalable 3D instruction-tuning datasets, covering 10 diverse multi-modal tasks with more than 0.23 million QA pairs generated in total. Thorough experiments evaluating trending MLLMs, comparisons against existing datasets, and variations of training protocols demonstrate the superiority of 3DBench, offering valuable insights into current limitations and potential research directions. Codes are available at https://github.com/Inshsang/3DBench.
A Fourier Perspective of Feature Extraction and Adversarial Robustness
Authors: Liangqi Zhang; Yihao Luo; Haibo Shen; Tianjiang Wang
Venue: IJCAI 2024 (main)

Abstract: Adversarial robustness and interpretability are longstanding challenges of computer vision. Deep neural networks are vulnerable to adversarial perturbations that are incomprehensible and imperceptible to humans. However, the opaqueness of networks prevents one from theoretically addressing adversarial robustness. As a human-comprehensible approach, the frequency perspective has been adopted in recent works to investigate the properties of neural networks and adversarial examples. In this paper, we investigate the frequency properties of feature extraction and analyze the stability of different frequency features when attacking different frequencies. Therefore, we propose an attack method, F-PGD, based on the projected gradient descent to attack the specified frequency bands. Utilizing this method, we find many intriguing properties of neural networks and adversarial perturbations. We experimentally show that contrary to the low-frequency bias of neural networks, the effective features of the same class are distributed across all frequency bands. Meanwhile, the high-frequency features often dominate when the neural networks make conflicting decisions on different frequency features. Furthermore, the attack experiments show that the low-frequency features are more robust to the attacks on different frequencies, but the interference to the high frequencies makes the network unable to make the right decision. These properties indicate that the decision-making process of neural networks tends to use as few low-frequency features as possible and cannot integrate features of different frequencies.
Continual Compositional Zero-Shot Learning
Authors: Yang Zhang; Songhe Feng; Jiazheng Yuan
Venue: IJCAI 2024 (main)

Abstract: Compositional Zero-Shot Learning (CZSL) aims to recognize unseen compositions with the knowledge learned from seen compositions, where each composition is composed of two primitives (attribute and object). However, existing CZSL methods are designed to learn compositions from fixed primitive set, which cannot handle the continually expanding primitive set in real-world applications. In this paper, we propose a new CZSL setting, named Continual Compositional Zero-Shot Learning (CCZSL), which requires the model to recognize unseen compositions composed of learned primitive set while continually increasing the size of learned primitive set. Contextuality and catastrophic forgetting are the main issues to be addressed in this setting. Specifically, we capture similar contextuality in compositions through several learnable Super-Primitives that can modify the invariant primitive embedding to better adapt the contextuality in the corresponding composition. Then we introduce a dual knowledge distillation loss which aims at maintaining old knowledge learned from previous sessions and avoiding overfitting of new session. We design the CCZSL evaluation protocol and conduct extensive experiments on widely used benchmarks, demonstrating the superiority of our method compared to the state-of-the-art CZSL methods.
GenSeg: On Generating Unified Adversary for Segmentation
Authors: Yuxuan Zhang; Zhenbo Shi; Wei Yang; Shuchang Wang; Shaowei Wang; Yinxing Xue
Venue: IJCAI 2024 (main)

Abstract: Great advancements in semantic, instance, and panoptic segmentation have been made in recent years, yet the top-performing models remain vulnerable to imperceptible adversarial perturbation. Current attacks on segmentation primarily focus on a single task, and these methods typically rely on iterative instance-specific strategies, resulting in limited attack transferability and low efficiency. In this paper, we propose GenSeg, a Generative paradigm that creates unified adversaries for Segmentation tasks. In particular, we propose an intermediate-level objective to enhance attack transferability, including a mutual agreement loss for feature deviation, and a prototype obfuscating loss to disrupt intra-class and inter-class relationships. Moreover, GenSeg crafts an adversary in a single forward pass, significantly boosting the attack efficiency. Besides, we unify multiple segmentation tasks to GenSeg in a novel category-and-mask view, which makes it possible to attack these segmentation tasks within this unified framework, and conduct cross-domain and cross-task attacks as well. Extensive experiments demonstrate the superiority of GenSeg in black-box attacks compared with state-of-the-art attacks. To our best knowledge, GenSeg is the first approach capable of conducting cross-domain and cross-task attacks on segmentation tasks, which are closer to real-world scenarios.
ChatSpot: Bootstrapping Multimodal LLMs via Precise Referring Instruction Tuning
Authors: Liang Zhao; En Yu; Zheng Ge; Jinrong Yang; Haoran Wei; Hongyu Zhou; Jianjian Sun; Yuang Peng; Runpei Dong; Chunrui Han; Xiangyu Zhang
Venue: IJCAI 2024 (main)

Abstract: Human-AI interactivity is a critical aspect that reflects the usability of Multimodal Large Language Models (MLLMs). However, existing end-to-end MLLMs only allow users to interact with them through language instructions, leading to the limitation of the interactive accuracy and efficiency. In this study, we present precise referring instructions that utilize diverse reference representations such as points and boxes as referring prompts to refer to the special region. This enables MLLMs to focus on the region of interest and achieve finer-grained interaction. Based on precise referring instruction, we propose ChatSpot, a unified end-to-end MLLM that supports diverse forms of interactivity including mouse clicks, drag-and-drop, and drawing boxes, which provides a more flexible and seamless interactive experience. We also construct a multi-grained vision-language instruction-following dataset based on existing datasets and GPT-4 generating. Furthermore, we design a series of evaluation tasks to assess the effectiveness of region recognition and interaction. Experimental results showcase ChatSpot's promising performance. Project page: https://github.com/Ahnsun/ChatSpot.
Multi-Attention Based Visual-Semantic Interaction for Few-Shot Learning
Authors: Peng Zhao; Yin Wang; Wei Wang; Jie Mu; Huiting Liu; Cong Wang; Xiaochun Cao
Venue: IJCAI 2024 (main)

Abstract: Few-Shot Learning (FSL) aims to train a model that can generalize to recognize new classes, with each new class having only very limited training samples. Since extracting discriminative features for new classes with few samples is challenging, existing FSL methods leverage visual and semantic prior knowledge to guide discriminative feature learning. However, for meta-learning purposes, the semantic knowledge of the query set is unavailable, so their features lack discriminability. To address this problem, we propose a novel Multi-Attention based Visual-Semantic Interaction (MAVSI) approach for FSL. Specifically, we utilize spatial and channel attention mechanisms to effectively select discriminative visual features for the support set based on its ground-truth semantics while using all the support set semantics for each query set sample. Then, a relation module with class prototypes of the support set is employed to supervise and select discriminative visual features for the query set. To further enhance the discriminability of the support set, we introduce a visual-semantic contrastive learning module to promote the similarity between visual features and their corresponding semantic features. Extensive experiments on four benchmark datasets demonstrate that our proposed MAVSI could outperform existing state-of-the-art FSL methods.
Focus on the Whole Character: Discriminative Character Modeling for Scene Text Recognition
Authors: Bangbang Zhou; Yadong Qu; Zixiao Wang; Zicheng Li; Boqiang Zhang; Hongtao Xie
Venue: IJCAI 2024 (main)

Abstract: Recently, scene text recognition (STR) models have shown significant performance improvements. However, existing models still encounter difficulties in recognizing challenging texts that involve factors such as severely distorted and perspective characters. These challenging texts mainly cause two problems: (1) Large Intra-Class Variance. (2) Small Inter-Class Variance. An extremely distorted character may prominently differ visually from other characters within the same category, while the variance between characters from different classes is relatively small. To address the above issues, we propose a novel method that enriches the character features to enhance the discriminability of characters. Firstly, we propose the Character-Aware Constraint Encoder (CACE) with multiple blocks stacked. CACE introduces a decay matrix in each block to explicitly guide the attention region for each token. By continuously employing the decay matrix, CACE enables tokens to perceive morphological information at the character level. Secondly, an Intra-Inter Consistency Loss (I^2CL) is introduced to consider intra-class compactness and inter-class separability at feature space. I^2CL improves the discriminative capability of features by learning a long-term memory unit for each character category. Trained with synthetic data, our model achieves state-of-the-art performance on common benchmarks (94.1% accuracy) and Union14M-Benchmark (61.6% accuracy). Code is available at https://github.com/bang123-box/CFE.
CoFInAl: Enhancing Action Quality Assessment with Coarse-to-Fine Instruction Alignment
Authors: Kanglei Zhou; Junlin Li; Ruizhi Cai; Liyuan Wang; Xingxing Zhang; Xiaohui Liang
Venue: IJCAI 2024 (main)

Abstract: Action Quality Assessment (AQA) is pivotal for quantifying actions across domains like sports and medical care. Existing methods often rely on pre-trained backbones from large-scale action recognition datasets to boost performance on smaller AQA datasets. However, this common strategy yields suboptimal results due to the inherent struggle of these backbones to capture the subtle cues essential for AQA. Moreover, fine-tuning on smaller datasets risks overfitting. To address these issues, we propose Coarse-to-Fine Instruction Alignment (CoFInAl). Inspired by recent advances in large language model tuning, CoFInAl aligns AQA with broader pre-trained tasks by reformulating it as a coarse-to-fine classification task. Initially, it learns grade prototypes for coarse assessment and then utilizes fixed sub-grade prototypes for fine-grained assessment. This hierarchical approach mirrors the judging process, enhancing interpretability within the AQA framework. Experimental results on two long-term AQA datasets demonstrate CoFInAl achieves state-of-the-art performance with significant correlation gains of 5.49% and 3.55% on Rhythmic Gymnastics and Fis-V, respectively. Our Code is available at https://github.com/ZhouKanglei/CoFInAl_AQA.
Zero-Shot Sketch Based Image Retrieval via Modality Capacity Guidance
Authors: Yanghong Zhou; Dawei Liu; P. Y. Mok
Venue: IJCAI 2024 (main)

Abstract: Zero-shot sketch-based image retrieval (ZS-SBIR), aiming to recognize and retrieve relevant photos based on freehand sketch queries that belong to unseen categories in the search set, has sparked considerable interest, benefiting from the rapid advancements in multimodal learning and feature representation research. Despite the recent improvements in performance, there are still rooms for refining feature representation and thus enhancing the generalization capabilities of the models. Most of the existing research efforts have primarily focused on learning the feature distribution of modalities within specific datasets, without considering the broader dataset-agnostic `population distribution' of relevant modalities. In this paper, we investigate the modality population distribution and apply such knowledge to guide feature learning. Specifically, we propose a modality capacity constraint loss to control the learning of population distribution for sketches and photos. This loss can be effectively combined with retrieval loss (e.g., triplet loss) or classification loss (e.g., InfoNCE loss) to enhance the performance of ZS-SBIR, through the fine-tuning process of pre-trained models like CLIP and DINO. Extensive experiment results have demonstrated our significant performance improvements, achieving an increase of 7.3%/3.2% and 19.9%/10.3% in terms of mAP@200/P@200 compared to the state-of-the-art models on CLIP and DINO, respectively, on the Sketchy-ext dataset (split 2). Data, code, and supplementary information are available at https://github.com/YHdian0716/ZS-SBIR-MCC.git.
Zero-shot High-fidelity and Pose-controllable Character Animation
Authors: Bingwen Zhu; Fanyi Wang; Tianyi Lu; Peng Liu; Jingwen Su; Jinxiu Liu; Yanhao Zhang; Zuxuan Wu; Guo-Jun Qi; Yu-Gang Jiang
Venue: IJCAI 2024 (main)

Abstract: Image-to-video (I2V) generation aims to create a video sequence from a single image, which requires high temporal coherence and visual fidelity. However, existing approaches suffer from inconsistency of character appearances and poor preservation of fine details. Moreover, they require a large amount of video data for training, which can be computationally demanding. To address these limitations, we propose PoseAnimate, a novel zero-shot I2V framework for character animation. PoseAnimate contains three key components: 1) a Pose-Aware Control Module (PACM) that incorporates diverse pose signals into text embeddings, to preserve character-independent content and maintain precise alignment of actions. 2) a Dual Consistency Attention Module (DCAM) that enhances temporal consistency and retains character identity and intricate background details. 3) a Mask-Guided Decoupling Module (MGDM) that refines distinct feature perception abilities, improving animation fidelity by decoupling the character and background. We also propose a Pose Alignment Transition Algorithm (PATA) to ensure smooth action transition. Extensive experiment results demonstrate that our approach outperforms the state-of-the-art training-based methods in terms of character consistency and detail fidelity. Moreover, it maintains a high level of temporal coherence throughout the generated animations.