Association for the Advancement of Artificial Intelligence: AAAI Publications
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DreamUHD: Frequency Enhanced Variational Autoencoder for Ultra-High-Definition Image Restoration
No full textExisting ultra-high-definition (UHD) image restoration methods often struggle with consistency due to downsampling. We aim to address these challenges by leveraging the powerful latent space representation and reconstruction capabilities of Variational Autoencoders (VAE). However, applying VAE to UHD image restoration presents challenges: 1) High-performing VAEs have large parameter sizes, leading to significant carbon footprints; 2) The self-reconstruction property of VAE hinders bridging the domain gap between clean and degraded images; 3) Latent encoding in VAE can lose high-frequency information, compromising image detail.
To overcome these challenges, we propose a frequency enhanced VAE UHD image restoration framework by integrating frequency priors. First, we design the Fourier-based lightweight frequency learning within the VAE to improve parameter efficiency. Then, we introduce a wavelet-based adapter that extracts multi-scale image information and employs frequency-aware adaptive modulation to bridge the domain gap by integrating degraded image data into the pre-trained VAE. Additionally, the adapter injects high-frequency information into the VAE decoder, enhancing detail in the restored images. In this way, our method effectively combines the powerful latent space representation with frequency priors to enhance UHD image restoration. Extensive experiments on various UHD image restoration tasks show that our method surpasses state-of-the-art methods both qualitatively and quantitatively
Towards Robust Visual Question Answering via Prompt-Driven Geometric Harmonization
No full textVisual Question Answering (VQA) has garnered significant attention as a crucial link between vision and language, aimed at generating accurate responses to visual queries. However, current VQA models still struggle with the challenges of minority class collapse and spurious semantic correlations posed by language bias and imbalanced distributions. To address these challenges, this paper proposes a novel Prompt-Driven Geometric Harmonization (PDGH) paradigm, which integrates both geometric structure and information entropy principles to enhance the ability of VQA models to generalize effectively across diverse scenarios. Specifically, our PDGH approach is meticulously designed to generate image-generated prompts that are guided by specific question cues, facilitating a more accurate and context-aware understanding of the visual content. Moreover, we project the prompt-visual-question and visual-question joint representations into a unified hypersphere space, applying feature weight self-orthogonality and prompt-information entropy correction constraints to optimize the margin, further alleviating minority class collapse and correcting language bias. To maintain the geometric integrity of the representation space, we introduce multi-space geometric contrast constraints to minimize the impact of spurious priors introduced during training. Finally, a semantic matrix is constructed for the coordinated joint representation to ensure that the learned instances are semantically consistent and improve reasoning ability. Extensive experiments on various general and medical VQA datasets demonstrate the consistent superiority of our PDGH approach over existing state-of-the-art baselines
Infer the Whole from a Glimpse of a Part: Keypoint-Based Knowledge Graph for Vehicle Re-Identification
No full textVehicle re-identification aims to match vehicles across non-overlapping camera views. Many existing methods extract features from one specific image, and these methods lack view-invariance when comparing vehicles of different orientations. As a result, discriminative parts obscured by viewpoint changes cannot contribute effectively to matching. This work presents a novel keypoint-based framework for vehicle Re-ID. We propose to explicitly model the intrinsic structural relationships between vehicle components via knowledge graph. By establishing connection between keypoints, our approach aims to leverage such prior to match vehicles even when some parts are not directly comparable due to orientation inconsistencies. Specifically, given query and gallery images, we first detect visible keypoints. Then, a transformer-based model infers features for non-overlapped keypoints by conditioning on visible correspondences defined in the knowledge graph. The final representation integrates visible and inferred features. Extensive experiments demonstrate our method outperforms state-of-the-arts on standard benchmarks under cross-view matching scenarios. To our knowledge, this is the first work introducing structural priors via keypoint knowledge graphs for view-invariant vehicle re-identification
Does VLM Classification Benefit from LLM Description Semantics?
No full textAccurately describing images with text is a foundation of explainable AI. Vision-Language Models (VLMs) like CLIP have recently addressed this by aligning images and texts in a shared embedding space, expressing semantic similarities between vision and language embeddings. VLM classification can be improved with descriptions generated by Large Language Models (LLMs). However, it is difficult to determine the contribution of actual description semantics, as the performance gain may also stem from a semantic-agnostic ensembling effect, where multiple modified text prompts act as a noisy test-time augmentation for the original one.
We propose an alternative evaluation scenario to decide if a performance boost of LLM-generated descriptions is caused by such a noise augmentation effect or rather by genuine description semantics. The proposed scenario avoids noisy test-time augmentation and ensures that genuine, distinctive descriptions cause the performance boost. Furthermore, we propose a training-free method for selecting discriminative descriptions that work independently of classname-ensembling effects. Our approach identifies descriptions that effectively differentiate classes within a local CLIP label neighborhood, improving classification accuracy across seven datasets. Additionally, we provide insights into the explainability of description-based image classification using VLMs
CAKE: Category Aware Knowledge Extraction for Open-Vocabulary Object Detection
No full textOpen vocabulary object detection (OVOD) task aims to detect objects of novel categories beyond the base categories in the training set. To this end, the detector needs to access image-text pairs containing rich semantic information or the visual language pre-trained model (VLM) learned on them. Recent OVOD methods rely on knowledge distillation from VLMs. However, there are two main problems in current methods: (1) Current knowledge distillation frameworks fail to take advantage of the global category information of VLMs and thus fail to learn category-specific knowledge. (2) Due to the overfitting phenomenon of base categories during training, current OVOD networks generally have the problem of suppressing novel categories as background. To address these two problems, we propose a Category Aware Knowledge Extraction framework (CAKE), which consists of a Category-Specific Knowledge Distillation branch (CSKD) and a Category Generalization Region Proposal Network (CG-RPN). CSKD can more fully extract category-strong related information through category-specific distillation, and it is also conducive to filtering the exclusion problem between individuals of the same category; in this process, the model constructs a category-specific feature set to maintain high-quality category features. CG-RPN leverages the guidance of feature set to adjust the confidence scores of region proposals, thereby mining proposals that potentially contain novel categories of objects.
Extensive experiments show that our method can plug and play well with many existing methods and significantly improve their detection performance. Moreover, our CAKE framework can reach the-state-of-the-art performance on OV-COCO and OV-LVIS datasets
Instruct Where the Model Fails: Generative Data Augmentation via Guided Self-contrastive Fine-tuning
No full textData augmentation is expected to bring about unseen features of training set, enhancing the model’s ability to generalize in situations where data is limited. Generative image models trained on large web-crawled datasets such as LAION are known to produce images with stereotypes and imperceptible bias when used to augment training data, owing to dataset misalignment and the generator’s ignorance of the downstream model. We improve downstream task awareness in generated images by proposing a task-aware fine-tuning strategy that actively detects failures of downstream task in the target model to fine-tune the generation process between epochs. The dynamic fine-tuning strategy is achieved by (1) inspecting misalignment between generated data and original data via VLM captioners and (2) adjusts both prompts and diffusion model so that the strategy dynamically guides the generator by focusing on the detected bias of VLM. This is done via re-captioning the overfitted data as well as finetuning the diffusion trajectory in a contrastive manner. To co-operate with the VLM captioner, the contrastive fine-tuning process dynamically adjusts different parts of the diffusion trajectory based on detected misalignment, thus shifting the the generated distribution away from making the downstream model overfit. Our experiments on few-shot class incremental learning show that our instruction-guided finetuning strategy consistently assists the downstream model with higher classification accuracy compared to generative data augmentation baselines such as Stable Diffusion and GPT-4o, and state-of-the-art non-generative strategies
Storynizor: Consistent Story Generation via Inter-Frame Synchronized and Shuffled ID Injection
No full textRecent advances in text-to-image diffusion models have spurred significant interest in continuous story image generation. In this paper, we introduce Storynizor, a model capable of generating coherent stories with strong inter-frame character consistency, effective foreground-background separation, and diverse pose variation. The core innovation of Storynizor lies in its key modules: ID-Synchronizer and ID-Injector. The ID-Synchronizer employs an auto-mask self-attention module and a mask perceptual loss across inter-frame images to improve the consistency of character generation, vividly representing their postures and backgrounds. The ID-Injector utilize a Shuffling Reference Strategy (SRS) to integrate ID features into specific locations, enhancing ID-based consistent character generation. Additionally, to facilitate the training of Storynizor, we have curated a novel dataset called StoryDB comprising 100, 000 images. This dataset contains single and multiple-character sets in diverse environments, layouts, and gestures with detailed descriptions. Experimental results indicate that Storynizor demonstrates superior coherent story generation with high-fidelity character consistency, flexible postures, and vivid backgrounds compared to other character-specific methods
Autonomous LLM-Enhanced Adversarial Attack for Text-to-Motion
No full textHuman motion generative models have enabled promising applications, but the ability of text-to-motion (T2M) models to produce realistic motions raises security concerns if exploited maliciously. Despite growing interest in T2M, limited research focus on safeguarding these models against adversarial attacks, with existing work on text-to-image models proving insufficient for the unique motion domain. In the paper, we propose ALERT-Motion, an autonomous framework that leverages large language models (LLMs) to generate targeted adversarial attacks against black-box T2M models. Unlike prior methods that modify prompts through predefined rules, ALERT-Motion uses the knowledge of LLMs of human motion to autonomously generate subtle yet powerful adversarial text descriptions. It comprises two key modules: an adaptive dispatching module that constructs an LLM-based agent to iteratively refine and search for adversarial prompts; and a multimodal information contrastive module that extracts semantically relevant motion information to guide the agent's search. Through this LLM-driven approach, ALERT-Motion produces adversarial prompts querying victim models to produce outputs closely matching targeted motions, while avoiding obvious perturbations. Evaluations across popular T2M models demonstrate ALERT-Motion's superiority over previous methods, achieving higher attack success rates with stealthier adversarial prompts. This pioneering work on T2M adversarial attacks highlights the urgency of developing defensive measures as motion generation technology advances, urging further research into safe and responsible deployment
HiGDA: Hierarchical Graph of Nodes to Learn Local-to-Global Topology for Semi-Supervised Domain Adaptation
No full textThe enhanced representational power and broad applicability of deep learning models have attracted significant interest from the research community in recent years. However, these models often struggle to perform effectively under domain shift conditions, where the training data (the source domain) is related to but exhibits different distributions from the testing data (the target domain). To address this challenge, previous studies have attempted to reduce the domain gap between source and target data by incorporating a few labeled target samples during training—a technique known as semi-supervised domain adaptation (SSDA). While this strategy has demonstrated notable improvements in classification performance, the network architectures used in these approaches primarily focus on exploiting the features of individual images, leaving room for improvement in capturing rich representations. In this study, we introduce a Hierarchical Graph of Nodes designed to simultaneously present representations at both feature and category levels. At the feature level, we introduce a local graph to identify the most relevant patches within an image, facilitating adaptability to defined main object representations. At the category level, we employ a global graph to aggregate the features from samples within the same category, thereby enriching overall representations. Extensive experiments on widely used SSDA benchmark datasets, including Office-Home, DomainNet, and VisDA2017, demonstrate that both quantitative and qualitative results substantiate the effectiveness of HiGDA, establishing it as a new state-of-the-art method
Locate Anything on Earth: Advancing Open-Vocabulary Object Detection for Remote Sensing Community
No full textObject detection, particularly open-vocabulary object detection, plays a crucial role in Earth sciences, such as environmental monitoring, natural disaster assessment, and land-use planning. However, existing open-vocabulary detectors, primarily trained on natural-world images, struggle to generalize to remote sensing images due to a significant data domain gap. Thus, this paper aims to advance the development of open-vocabulary object detection in remote sensing community. To achieve this, we first reformulate the task as Locate Anything on Earth (LAE) with the goal of detecting any novel concepts on Earth. We then developed the LAE-Label Engine which collects, auto-annotates, and unifies up to 10 remote sensing datasets creating the LAE-1M — the first large-scale remote sensing object detection dataset with broad category coverage. Using the LAE-1M, we further propose and train the novel LAE-DINO Model, the first open-vocabulary foundation object detector for the LAE task, featuring Dynamic Vocabulary Construction (DVC) and Visual-Guided Text Prompt Learning (VisGT) modules. DVC dynamically constructs vocabulary for each training batch, while VisGT maps visual features to semantic space, enhancing text features. We comprehensively conduct experiments on established remote sensing benchmark DIOR, DOTAv2.0, as well as our newly introduced 80-class LAE-80C benchmark. Results demonstrate the advantages of the LAE-1M dataset and the effectiveness of the LAE-DINO method