GenAI4ELab/papercli-papers huggingface dataset:
Bright-NeRF: Brightening Neural Radiance Field with Color Restoration from Low-Light RAW Images
Authors: Min Wang; Xin Huang; Guoqing Zhou; Qifeng Guo; Qing Wang
Venue: AAAI 2025 (main)

Abstract: Neural Radiance Fields (NeRF) have demonstrated prominent performance in novel view synthesis tasks. However, their input heavily relies on image acquisition under normal light conditions, making it challenging to learn accurate scene contents in low-light environments where images typically exhibit significant noise and severe color distortion. To address these challenges, we propose a novel approach, Bright-NeRF, which learns enhanced and high-quality radiance fields from multi-view low-light RAW images in an unsupervised manner. Our method simultaneously achieves color restoration, denoising, and enhanced novel view synthesis. Specifically, we leverage a physically-inspired model of the sensor's response to illumination and introduce a chromatic adaptation loss to constrain the learning of response, enabling consistent color perception of objects regardless of lighting conditions. We further utilize the RAW data's properties to expose the scene's intensity automatically. Additionally, we have collected a multi-view low-light RAW image dataset of real-world scenes to advance research in this field. Experimental results demonstrate that our proposed method significantly outperforms existing 2D and 3D approaches. Our code and dataset will be made publicly available.
Thinking in Granularity: Dynamic Quantization for Image Super-Resolution by Intriguing Multi-Granularity Clues
Authors: Mingshen Wang; Zhao Zhang; Feng Li; Ke Xu; Kang Miao; Meng Wang
Venue: AAAI 2025 (main)

Abstract: Dynamic quantization has attracted rising attention in image super-resolution (SR) as it expands the potential of heavy SR models onto mobile devices while preserving competitive performance. Most current methods explore layer-to-bit configuration upon varying local regions, adaptively allocating the bit to each layer and patch. Despite the benefits, they still fall short in the tradeoff of SR accuracy and quantization efficiency. Apart from this, adapting the quantization level for each layer individually can disturb the original inter-layer relationships, thus diminishing the representation capability of quantized models. In this work, we propose Granular-DQ, which takes advantage of multi-granularity clues and local patch statistics, achieving a distinctive patch-wise and layer-invariant dynamic quantization paradigm. Specifically, Granular-DQ initiates by developing a granularity-bit controller to apprehend the coarse-to-fine granular representations of local patches, matching their proportional contribution to the entire image to determine the proper bit-width allocation. On this premise, we investigate the interrelationships between bit-width and information density within high-bit patches, establishing a soft gate that enables further fine-grained dynamic bit adaption. Extensive experiments validate the superiority of Granular-DQ in the trade-off between efficiency and accuracy over recent state-of-the-art methods on various SR models.
Deep Multi-modal Graph Clustering via Graph Transformer Network
Authors: Qianqian Wang; Haiming Xu; Zihao Zhang; Wei Feng; Quanxue Gao
Venue: AAAI 2025 (main)

Abstract: Current deep multi-modal graph clustering methods primarily rely on Graph Neural Network (GNN) to fully exploit attribute features and graph structures, including message propagation and low-dimensional feature embedding. However, these methods lack further exploration of graph structural information, such as the relationship between nodes and shortest paths. Additionally, they may not sufficiently mine complementary information among multi-modal graph data. To address these issues, we propose a novel Deep Multi-modal Graph Clustering via Graph Transformer Network method, called DMGC-GTN. This method thoroughly dissects and utilizes graph structural information, applying graph smoothing to node features and incorporating various forms of embeddings into the transformer architecture. This achieves a unified embedding of graph structure and multi-modal feature attributes, fully exploiting the complementary information within multi-modal graph data. Extensive experiments demonstrate the effectiveness of our algorithm.
Meme Trojan: Backdoor Attacks Against Hateful Meme Detection via Cross-Modal Triggers
Authors: Ruofei Wang; Hongzhan Lin; Ziyuan Luo; Ka Chun Cheung; Simon See; Jing Ma; Renjie Wan
Venue: AAAI 2025 (main)

Abstract: Hateful meme detection aims to prevent the proliferation of hateful memes on various social media platforms. Considering its impact on social environments, this paper introduces a previously ignored but significant threat to hateful meme detection: backdoor attacks. By injecting specific triggers into meme samples, backdoor attackers can manipulate the detector to output their desired outcomes. To explore this, we propose the Meme Trojan framework to initiate backdoor attacks on hateful meme detection. Meme Trojan involves creating a novel Cross-Modal Trigger (CMT) and a learnable trigger augmentor to enhance the trigger pattern according to each input sample. Due to the cross-modal property, the proposed CMT can effectively initiate backdoor attacks on hateful meme detectors under an automatic application scenario. Additionally, the injection position and size of our triggers are adaptive to the texts contained in the meme, which ensures that the trigger is seamlessly integrated with the meme content. Our approach outperforms the state-of-the-art backdoor attack methods, showing significant improvements in effectiveness and stealthiness. We believe that this paper will draw more attention to the potential threat posed by backdoor attacks on hateful meme detection.
The Parables of the Mustard Seed and the Yeast: Extremely Low-Budget, High-Performance Nighttime Semantic Segmentation
Authors: Shiqin Wang; Xin Xu; Haoyang Chen; Kui Jiang; Zheng Wang
Venue: AAAI 2025 (main)

Abstract: Nighttime Semantic Segmentation (NSS) is essential to many cutting-edge vision applications. However, existing technologies overly rely on massive labeled data, whose annotation is time-consuming and laborious. In this paper, we pioneer a new task focusing on exploring the potential of training strategy and framework design with limited annotation to achieve high-performance NSS. Insufficient information at very low labeling budgets can easily lead to under-optimization or overfitting of the model. Our solution comprises two main components: i) a novel region-based active sampling strategy called Contextual-Aware Region Query (CARQ), which identifies highly informative target nighttime regions for labeling; and ii) an innovative Fragmentation Synergy Active Domain Adaptation framework (FS-ADA), which progressively broadcasts the limited annotation to the unlabeled regions, achieving high performance with a minimal annotation budget. Extensive experiments demonstrate that our method outperforms state-of-the-art UDA-NSS & ADA-SS methods across four day-to-nighttime benchmarks, and generalizes well to foggy, rainy, & snowy scenes. In particular only with 1% target nighttime data annotation, our method is on par with the mainstream fully-supervised methods on the BDD100K-Night val dataset.
GFlow: Recovering 4D World from Monocular Video
Authors: Shizun Wang; Xingyi Yang; Qiuhong Shen; Zhenxiang Jiang; Xinchao Wang
Venue: AAAI 2025 (main)

Abstract: Recovering 4D world from monocular video is a crucial yet challenging task. Conventional methods usually rely on the assumptions of multi-view videos, known camera parameters, or static scenes. In this paper, we relax all these constraints and tackle a highly ambitious but practical task: With only one monocular video without camera parameters, we aim to recover the dynamic 3D world alongside the camera poses. To solve this, we introduce GFlow, a new framework that utilizes only 2D priors (depth and optical flow) to lift a video to a 4D scene, as a flow of 3D Gaussians through space and time. GFlow starts by segmenting the video into still and moving parts, then alternates between optimizing camera poses and the dynamics of the 3D Gaussian points. This method ensures consistency among adjacent points and smooth transitions between frames. Since dynamic scenes always continually introduce new visual content, we present prior-driven initialization and pixel-wise densification strategy for Gaussian points to integrate new content. By combining all those techniques, GFlow transcends the boundaries of 4D recovery from causal videos; it naturally enables tracking of points and segmentation of moving objects across frames. Additionally, GFlow estimates the camera poses for each frame, enabling novel view synthesis by changing camera pose. This capability facilitates extensive scene-level or object-level editing, highlighting GFlow's versatility and effectiveness.
Autoregressive Sequence Modeling for 3D Medical Image Representation
Authors: Siwen Wang; Churan Wang; Fei Gao; Lixian Su; Fandong Zhang; Yizhou Wang; Yizhou Yu
Venue: AAAI 2025 (main)

Abstract: Three-dimensional (3D) medical images, such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), are essential for clinical applications. However, the need for diverse and comprehensive representations is particularly pronounced when considering the variability across different organs, diagnostic tasks, and imaging modalities. How to effectively interpret the intricate contextual information and extract meaningful insights from these images remains an open challenge to the community. While current self-supervised learning methods have shown potential, they often consider an image as a whole thereby overlooking the extensive, complex relationships among local regions from one or multiple images. In this work, we introduce a pioneering method for learning 3D medical image representations through an autoregressive pre-training framework. Our approach sequences various 3D medical images based on spatial, contrast, and semantic correlations, treating them as interconnected visual tokens within a token sequence. By employing an autoregressive sequence modeling task, we predict the next visual token in the sequence, which allows our model to deeply understand and integrate the contextual information inherent in 3D medical images. Additionally, we implement a random startup strategy to avoid overestimating token relationships and to enhance the robustness of learning. The effectiveness of our approach is demonstrated by the superior performance over others on nine downstream tasks in public datasets.
CAD-GPT: Synthesising CAD Construction Sequence with Spatial Reasoning-Enhanced Multimodal LLMs
Authors: Siyu Wang; Cailian Chen; Xinyi Le; Qimin Xu; Lei Xu; Yanzhou Zhang; Jie Yang
Venue: AAAI 2025 (main)

Abstract: Computer-aided design (CAD) significantly enhances the efficiency, accuracy, and innovation of design processes by enabling precise 2D and 3D modeling, extensive analysis, and optimization. Existing methods for creating CAD models rely on latent vectors or point clouds, which are difficult to obtain, and storage costs are substantial. Recent advances in Multimodal Large Language Models (MLLMs) have inspired researchers to use natural language instructions and images for CAD model construction. However, these models still struggle with inferring accurate 3D spatial location and orientation, leading to inaccuracies in determining the spatial 3D starting points and extrusion directions for constructing geometries. This work introduces CAD-GPT, a CAD synthesis method with spatial reasoning-enhanced MLLM that takes either a single image or a textual description as input. To achieve precise spatial inference, our approach introduces a 3D Modeling Spatial Mechanism. This method maps 3D spatial positions and 3D sketch plane rotation angles into a 1D linguistic feature space using a specialized spatial unfolding mechanism, while discretizing 2D sketch coordinates into an appropriate planar space to enable precise determination of spatial starting position, sketch orientation, and 2D sketch coordinate translations. Extensive experiments demonstrate that CAD-GPT consistently outperforms existing state-of-the-art methods in CAD model synthesis, both quantitatively and qualitatively.
Imagine: Image-Guided 3D Part Assembly with Structure Knowledge Graph
Authors: Weihao Wang; Yu Lan; Mingyu You; Bin He
Venue: AAAI 2025 (main)

Abstract: 3D part assembly is a promising task in 3D computer vision and robotics, focusing on assembling 3D parts together by predicting their 6-DoF poses. Like most 3D shape understanding tasks, existing methods primarily address this task by memorizing the poses of parts during the training process, leading to inaccuracies in complex assemblies and poor generalization to novel categories. In order to essentially improve the performance, structure knowledge of the target assembly is indispensable before assembling, which abstracts the potential part composition and their structural relationships. An image of the target assembly can serve as a common source for constructing this structure knowledge. Nevertheless, the image is far from enough, as its knowledge can be incomplete and ambiguous due to part occlusion and varying views. To tackle these issues, we propose Imagine, a novel Image-guided 3D part assembly framework with structure knowledge graph. As a novel assembly prior, the structure knowledge graph originates from the image and is refined as understanding the 3D parts. It encodes robust part-aware structural and semantic information of the assembly, guides the 3D parts from a coarse super-structure to a fine assembly, and co-evolves progressively throughout the assembly process. Extensive experiments demonstrate the state-of-the-art performance of our framework, along with strong generalization to novel images and categories.
MVReward: Better Aligning and Evaluating Multi-View Diffusion Models with Human Preferences
Authors: Weitao Wang; Haoran Xu; Yuxiao Yang; Zhifang Liu; Jun Meng; Haoqian Wang
Venue: AAAI 2025 (main)

Abstract: Recent years have witnessed remarkable progress in 3D content generation. However, corresponding evaluation methods struggle to keep pace. Automatic approaches have proven challenging to align with human preferences, and the mixed comparison of text- and image-driven methods often leads to unfair evaluations. In this paper, we present a comprehensive framework to better align and evaluate multi-view diffusion models with human preferences. To begin with, we first collect and filter a standardized image prompt set from DALL·E and Objaverse, which we then use to generate multi-view assets with several multi-view diffusion models. Through a systematic ranking pipeline on these assets, we obtain a human annotation dataset with 16k expert pairwise comparisons and train a reward model, coined MVReward, to effectively encode human preferences. With MVReward, image-driven 3D methods can be evaluated against each other in a more fair and transparent manner. Building on this, we further propose Multi-View Preference Learning (MVP), a plug-and-play multi-view diffusion tuning strategy. Extensive experiments demonstrate that MVReward can serve as a reliable metric and MVP consistently enhances the alignment of multi-view diffusion models with human preferences.
Divide, Conquer and Combine: A Training-Free Framework for High-Resolution Image Perception in Multimodal Large Language Models
Authors: Wenbin Wang; Liang Ding; Minyan Zeng; Xiabin Zhou; Li Shen; Yong Luo; Wei Yu; Dacheng Tao
Venue: AAAI 2025 (main)

Abstract: Multimodal large language models (MLLMs) have experienced significant advancements recently, but still struggle to recognize and interpret intricate details in high-resolution (HR) images effectively. While state-of-the-art (SOTA) MLLMs claim to process images at 4K resolution, existing MLLM benchmarks only support up to 2K, leaving the capabilities of SOTA models on true HR images largely untested. Furthermore, existing methods for enhancing HR image perception in MLLMs rely on computationally expensive visual instruction tuning. To address these limitations, we introduce HR-Bench, the first deliberately designed benchmark to rigorously evaluate MLLM performance on 4K & 8K images. Through extensive experiments, we demonstrate that while downsampling HR images leads to vision information loss, leveraging complementary modalities, e.g., text, can effectively compensate for this loss. Building upon this insight, we propose Divide, Conquer and Combine, a novel training-free framework for enhancing MLLM perception of HR images. Our method follows a three-staged approach: 1) Divide: recursively partitioning the HR image into patches and merging similar patches to minimize computational overhead, 2) Conquer: leveraging the MLLM to generate accurate textual descriptions for each image patch, and 3) Combine: utilizing the generated text descriptions to enhance the MLLM's understanding of the overall HR image. Extensive experiments show that: 1) the SOTA MLLM achieves 63% accuracy, which is markedly lower than the 87% accuracy achieved by humans on HR-Bench; 2) our method brings consistent and significant improvements (a relative increase of +6% on HR-Bench and +8% on general multimodal benchmarks).
FreeGen: Bridging Visual-Linguistic Discrepancies Towards Diffusion-based Pixel-level Data Synthesis
Authors: Wenzhuang Wang; Mingcan Ma; Yong Chen; Changqun Xia; Zhenbao Liang; Jia Li
Venue: AAAI 2025 (main)

Abstract: Text-to-image diffusion model has inspired research into text-to-data synthesis without human intervention, where spatial attentions correlated with semantic entities in text prompts are primarily interpreted as pseudo-masks. However, these vannila attentions often deliver visual-linguistic discrepancies, in which the associations between image features and entity-level tokens are unstable and divergent, yielding inferior masks for realistic applications, especially in more practical open-vocabulary settings. To tackle this issue, we propose a novel text-guided self-driven generative paradigm, termed FreeGen, which addresses the discrepancies by recalibrating intrinsic visual-linguistic correlations and serves as a real-data-free method to automatically synthesize open-vocabulary pixel-level data for arbitrary entities. Specifically, we first learn an Attention Self-Rectification mechanism to reproject the inherent attention matrices to achieve robust semantic alignment, thereby obtaining class-discriminative masks. A Temporal Fluctuation Factor is present to assess mask quality based on its variation over uniform sampling timesteps, enabling the selection of reliable masks. These masks are then employed as self-supervised signals to support the learning of an Entity-level Grounding Decoder in a self-training manner, thus producing open-vocabulary segmentation results. Extensive experiments show that the existing segmenters trained on FreeGen narrow the performance gap with real data counterparts and remarkably outperform the state-of-the-art methods.
DCTMamba: Advancing JPEG Image Restoration Through Long-Sequence Modeling and Adaptive Frequency Strategy
Authors: Xi Wang; Xueyang Fu; Liang Li; Zheng-Jun Zha
Venue: AAAI 2025 (main)

Abstract: Despite the advanced long-sequence modeling of Mamba, which has expanded its applications in image restoration, there remains a lack of exploration combining its strengths with the specific characteristics of JPEG image restoration, where high-frequency components are lost after the Discrete Cosine Transform (DCT). To address this, we introduce DCTMamba, a new framework designed to apply Mamba more effectively to JPEG image restoration. Specifically, our method integrates the Discrete Cosine Transform (DCT) into the Mamba to establish the sequential scanning from lower to higher frequencies, enabling the network to initially reconstruct coarse structures and progressively refine the image with more intricate details. Furthermore, recognizing the variable frequency distributions that arise from DCT transformations across different image sizes, we have developed Scale-Adaptive Normalization to manage these variations adeptly. Comprehensive experiments confirm that DCTMamba significantly outperforms existing solutions, achieving high fidelity in both coarse structures and fine details.CTMamba significantly outperforms existing solutions, achieving high fidelity
in both coarse structures and fine details.
TokenMatcher: Diverse Tokens Matching for Unsupervised Visible-Infrared Person Re-Identification
Authors: Xiao Wang; Lekai Liu; Bin Yang; Mang Ye; Zheng Wang; Xin Xu
Venue: AAAI 2025 (main)

Abstract: Unsupervised visible-infrared person re-identification (US-VI-ReID) seeks to match infrared and visible images of the same individual without the use of annotations. Current methods typically derive cross-modal correspondences through a single global feature matching process for generating pseudo labels and learning modality-invariant features. However, this matching approach is hindered by both intra-modality and inter-modality discrepancies, which result in imprecise measurements. As a consequence, the clustering of individuals with single global feature is often incomplete and unreliable, leading to suboptimal performance in cross-modal clustering tasks. To address these challenges and to extract cross-modality discriminative identity information, we propose a TokenMatcher, which encompasses three key components: Diverse Tokens Matching (DTM), Diverse Tokens Neighbor Learning (DTNL), and the Homogeneous Fusion (HF) Module. DTM utilizes multiple class tokens within the visual transformer framework to capture diverse embedding representations, thereby facilitating the integration of fine-grained information essential for reliable cross-modality correspondences. DTNL enhances the intra-modality and inter-modality consistency among diverse tokens by refining neighborhood sets with insights from neighboring tokens and camera information, promoting robust neighborhood learning and fostering discriminative identity information. Additionally, the HF module consolidates clusters of the same identity while effectively separating those of different identities. Extensive experiments conducted on the publicly available SYSU-MM01 and RegDB datasets demonstrate the efficacy of the proposed method.
CNC: Cross-modal Normality Constraint for Unsupervised Multi-class Anomaly Detection
Authors: Xiaolei Wang; Xiaoyang Wang; Huihui Bai; Eng Gee Lim; Jimin Xiao
Venue: AAAI 2025 (main)

Abstract: Existing unsupervised distillation-based methods rely on the differences between encoded and decoded features to locate abnormal regions in test images. However, the decoder trained only on normal samples still reconstructs abnormal patch features well, degrading performance. This issue is particularly pronounced in unsupervised multi-class anomaly detection tasks. We attribute this behavior to ‘over-generalization’ (OG) of decoder: the significantly increasing diversity of patch patterns in multi-class training enhances the model generalization on normal patches, but also inadvertently broadens its generalization to abnormal patches. To mitigate ‘OG’, we propose a novel approach that leverages class-agnostic learnable prompts to capture common textual normality across various visual patterns, and then apply them to guide the decoded features towards a ‘normal’ textual representation, suppressing ‘over-generalization’ of the decoder on abnormal patterns. To further improve performance, we also introduce a gated mixture-of-experts module to specialize in handling diverse patch patterns and reduce mutual interference between them in multi-class training. Our method achieves competitive performance on the MVTec AD and VisA datasets, demonstrating its effectiveness.