GenAI4ELab/papercli-papers huggingface dataset:
Zero-Shot Noise2Mean: Gap Minimization for Efficient Denoising from a Single Noisy Image
Authors: Duo Liu; Yiqi Shi; Guoyin Zhang; Sizhao Li; Liguo Zhang
Venue: AAAI 2025 (main)

Abstract: Acquiring pairwise noisy-clean training data is challenging. Consequently, some self-supervised denoising methods utilize noisy image pairs as both input and target for network training. However, a major issue with these methods is the gap between the clean images of the input and target. In this paper, we achieve high-quality image denoising by reducing or even eliminating this gap. Our method requires no training data or prior knowledge of the noise distribution. It consists of two lightweight networks that can be trained using only a single noisy test image. Specifically, we propose a random mask-based downsampler that generates multiple pairs of downsampled noisy images, which are similar but distinct. These image pairs serve as the input for the first network, with the mean image of each pair used as the target. This initially reduces the gap between the clean images of the input and target. Particularly, in our method, the clean counterpart of the first network's target (i.e., the mean image) can be obtained. We then train a second network using the mean image as input and its clean counterpart as the target. This effectively eliminates the gap and achieves better denoising results. Extensive experiments demonstrate that our method outperforms in both denoising performance and efficiency.
Making Large Vision Language Models to Be Good Few-Shot Learners
Authors: Fan Liu; Wenwen Cai; Jian Huo; Chuanyi Zhang; Delong Chen; Jun Zhou
Venue: AAAI 2025 (main)

Abstract: Few-shot classification (FSC) is a fundamental yet challenging task in computer vision that involves recognizing novel classes from limited data. While previous methods have focused on enhancing visual features or incorporating additional modalities, Large Vision Language Models (LVLMs) offer a promising alternative due to their rich knowledge and strong visual perception. However, LVLMs risk learning specific response formats rather than effectively extracting useful information from support data in FSC. In this paper, we investigate LVLMs' performance in FSC and identify key issues such as insufficient learning and the presence of severe position biases. To tackle above challenges, we adopt the meta-learning strategy to teach models ``learn to learn". By constructing a rich set of meta-tasks for instruction fine-tuning, LVLMs enhance the ability to extract information from few-shot support data for classification. Additionally, we further boost LVLM's few-shot learning capabilities through label augmentation (LA) and candidate selection (CS) in the fine-tuning and inference stages, respectively. LA is implemented via a character perturbation strategy to ensure the model focuses on support information. CS leverages attribute descriptions to filter out unreliable candidates and simplify the task. Extensive experiments demonstrate that our approach achieves superior performance on both general and fine-grained datasets. Furthermore, our candidate selection strategy has been proven beneficial for training-free LVLMs.
DreamAlign: Dynamic Text-to-3D Optimization with Human Preference Alignment
Authors: Gaofeng Liu; Zhiyuan Ma; Tao Fang
Venue: AAAI 2025 (main)

Abstract: Recent years have witnessed the remarkable success of Text-to-3D generation, particularly with the rise of mainstream conditional diffusion models (DMs). Though achieving substantial progress, existing methods still face a knotty "human preference" dilemma, that is the 3D contents generated by the models often deviate greatly from the desired effects (e.g., perspective, aesthetics, shading, appearance, etc.) due to the lack of attention to human preferences. To mitigate the limitation of data deficiency and enable human preference learning, we first elaborately curate the HP3D, a text-to-3D dataset with expert preference annotations which is initally captioned by the multimodal large model LLava and then refined by human expert. Based on such a brand-new HP3D, we further propose DreamAlign, a reward-free method that does not require designing any complex reward models whereas only by introducing a light-weight lora adapter and then designing a novel direct 3D preference optimization (D-3DPO) algorithm for training. Moreover, in the stage of text-to-3D we design an additional Preference Contrastive Feedback training for score distillation sampling, which enables the generated 3D objects to align the human preferences (e.g., aesthetics, material, etc.).  Extensive experiments demonstrate that DreamAlign consistently achieves state-of-the-art performance on generative effects and human preference alignment across various benchmark evaluations.
Multi-Label Few-Shot Image Classification via Pairwise Feature Augmentation and Flexible Prompt Learning
Authors: Han Liu; Yuanyuan Wang; Xiaotong Zhang; Feng Zhang; Wei Wang; Fenglong Ma; Hong Yu
Venue: AAAI 2025 (main)

Abstract: Multi-label few-shot image classification is a crucial and challenging task due to limited annotated data and elusive category specificity. However, research on this topic is still in the rudimentary stage and few methods are available. Existing methods either leverage data augmentation to alleviate data scarcity or utilize label features as auxiliary knowledge to eliminate the negative effect caused by irrelevant categories, but they ignore the utilization of image region features for data augmentation, and overlook to learn appropriate text feature to better match the image features of specific categories. Moreover, these methods only focus on one side and do not effectively tackle the above two issues simultaneously. In this paper, we introduce a novel prototype-based multi-label few-shot learning framework that seamlessly integrates pairwise feature augmentation and flexible prompt learning. Specifically, by pairwise feature augmentation, we leverage the region features of images in the support set to generate more image features and construct image prototypes, thus alleviating the issue of data scarcity. By flexible prompt learning, we adaptively acquire class-specific prompts to build text prototypes that highly match the image features of specific classes, thereby mitigating the impact of irrelevant classes. Finally, with adaptive learnable parameters, we merge image and text prototypes to obtain the final prototypes, achieving a more powerful classifier for multi-label few-shot image classification. Extensive experimental results demonstrate that our proposed method can push the performance to a higher level.
UN-DETR: Promoting Objectness Learning via Joint Supervision for Unknown Object Detection
Authors: HaoMiao Liu; Hao Xu; Chuhuai Yue; Bo Ma
Venue: AAAI 2025 (main)

Abstract: Unknown Object Detection (UOD) aims to identify objects of unseen categories, differing from the traditional detection paradigm limited by the closed-world assumption. A key component of UOD is learning a generalized representation, i.e. objectness for both known and unknown categories to distinguish and localize objects from the background in a class-agnostic manner. However, previous methods obtain supervision signals for learning objectness in isolation from either localization or classification information, leading to poor performance for UOD. To address this issue, we propose a transformer-based UOD framework, UN-DETR. Based on this, we craft Instance Presence Score (IPS) to represent the probability of an object's presence. For the purpose of information complementarity, IPS employs a strategy of joint supervised learning, integrating attributes representing general objectness from the positional and the categorical latent space as supervision signals. To enhance IPS learning, we introduce a one-to-many assignment strategy to incorporate more supervision. Then, we propose Unbiased Query Selection to provide premium initial query vectors for the decoder. Additionally, we propose an IPS-guided post process strategy to filter redundant boxes and correct classification predictions for known and unknown objects. Finally, we pretrain the entire UN-DETR in an unsupervised manner, in order to obtain objectness prior. Our UN-DETR is comprehensively evaluated on multiple UOD and known detection benchmarks, demonstrating its effectiveness and achieving state-of-the-art performance.
SCott: Accelerating Diffusion Models with Stochastic Consistency Distillation
Authors: Hongjian Liu; Qingsong Xie; Tianxiang Ye; Zhijie Deng; Chen Chen; Shixiang Tang; Xueyang Fu; Haonan Lu; Zheng-Jun Zha
Venue: AAAI 2025 (main)

Abstract: The iterative sampling procedure employed by diffusion models (DMs) often leads to significant latency. To address this, we propose Stochastic Consistency Distillation (SCott) to enable accelerated text-to-image generation, where high-quality generations can be achieved with just 2-4 sampling steps or even1 step, and further improvements can be obtained by additional cost, e.g., 4 steps. In contrast to vanilla consistency distillation (CD) which distills the ordinary differential equation solvers-based sampling process of a pre-trained teacher model into a student, SCott explores the possibility and validates the efficacy of integrating stochastic differential equation (SDE) solvers into CD to fully unleash the potential of the teacher. SCott is augmented with elaborate strategies to control the noise strength and sampling process of the SDE solver. An adversarial loss is further incorporated to strengthen the sample quality with rare sampling steps. Empirically, on the MSCOCO-2017 5K dataset with a Stable Diffusion-V1.5 teacher, SCott achieves an FID of 21.9, surpassing that of the 1-step InstaFlow (23.4) and the 4-step UFOGen (22.1). Moreover, SCott can yield more diverse samples than other consistency models for high-resolution image generation, with up to 16% improvement in a qualified metric.
ProtoCar: Learning 3D Vehicle Prototypes from Single-View and Unconstrained Driving Scene Images
Authors: Hongyuan Liu; Haochen Yu; Bochao Zou; Juntao Lyu; Qi Mei; Jiansheng Chen; Huimin Ma
Venue: AAAI 2025 (main)

Abstract: Reconstructing 3D models from sensor data is a valuable and promising direction for developing testing and validation environments in applications like autonomous driving. However, existing methods for 3D modeling often rely on extensive multi-view data or controlled conditions, making them difficult and expensive to scale. Furthermore, these methods, particularly those based on neural radiance fields, typically produce implicit models that can be challenging to manipulate and suffer from slow rendering speeds. In this paper, we introduce ProtoCar, a novel approach that overcomes these limitations by learning 3D vehicle prototypes from single-view images with diverse and unconstrained visual conditions. ProtoCar uses real-world driving data from LiDAR and image sensors, and employs 3D Gaussian splatting techniques to represent explicit geometric and texture. Extensive experiments demonstrate that ProtoCar generates high-quality 3D models and adapts well to various vehicle types and challenging visual scenarios, offering a scalable and effective solution for 3D modeling in environments with limited and variable visual information.
PEIE: Physics Embedded Illumination Estimation for Adaptive Dehazing
Authors: Huaizhuo Liu; Hai-Miao Hu; Yonglong Jiang; Yurui Liu
Venue: AAAI 2025 (main)

Abstract: Deep learning-based methods have made significant progress in image dehazing. However, these methods often falter when applied to real-world hazy images, primarily due to the scarcity of paired real-world data and the limitations of current dehazing feature extractors. Toward these issues, we introduce a novel Physics Embedded Illumination Estimation (PEIE) method for adaptive real-world dehazing. Specifically, (1) we identify the limitations of the widely used Atmospheric Scattering Model and propose a new physical model, the Illumination-Adaptive Scattering Model (IASM), for more accurate illumination representation in hazy imaging; (2) we develop a robust data synthesis pipeline that leverages the physics embedded illumination estimation to generate realistic hazy images; and (3) we design an Illumination-Adaptive Dehazing Unit (IDU) to extract dehazing features consistent with our proposed IASM in the latent space. By integrating the IDU into a U-Net architecture to create IADNet, we achieve significant improvements in dehazing performance through end-to-end training on synthetic data. Extensive experiments validate the superior performance of our PEIE method, significantly surpassing the state-of-the-arts in real-world dehazing.
TCPFormer: Learning Temporal Correlation with Implicit Pose Proxy for 3D Human Pose Estimation
Authors: Jiajie Liu; Mengyuan Liu; Hong Liu; Wenhao Li
Venue: AAAI 2025 (main)

Abstract: Recent multi-frame lifting methods have dominated the 3D human pose estimation. However, previous methods ignore the intricate dependence within the 2D pose sequence and learn single temporal correlation. To alleviate this limitation, we propose TCPFormer, which leverages an implicit pose proxy as an intermediate representation. Each proxy within the implicit pose proxy can build one temporal correlation therefore helping us learn a more comprehensive temporal correlation of human motion. Specifically, our method consists of three key components: Proxy Update Module (PUM),  Proxy Invocation Module (PIM), and Proxy Attention Module (PAM). PUM first uses pose features to update the implicit pose proxy, enabling it to store representative information from the pose sequence. PIM then invocates and integrates the pose proxy with the pose sequence to enhance the motion semantics of each pose. Finally, PAM leverages the above mapping between the pose sequence and pose proxy to enhance the temporal correlation of the whole pose sequence. Experiments on the Human3.6M and MPI-INF-3DHP datasets demonstrate that our proposed TCPFormer outperforms the previous state-of-the-art methods.
Union Is Strength! Unite the Power of LLMs and MLLMs for Chart Question Answering
Authors: Jiapeng Liu; Liang Li; Shihao Rao; Xiyan Gao; Weixin Guan; Bing Li; Can Ma
Venue: AAAI 2025 (main)

Abstract: Chart Question Answering (CQA) requires models to perform chart perception and reasoning. Recent studies driven by Large Language Models (LLMs) have dominated CQA. These include employing more cognitively capable LLMs for indirectly reasoning over transformed charts, i.e., tables, and directly perceiving charts utilizing Multimodal Large Language Models (MLLMs) with a wider perceptual range. Yet, they often encounter bottlenecks due to the limitation of the receptive field of LLMs and the fragility of the complex reasoning of some MLLMs. To unite the strengths of LLMs and MLLMs to complement each other's limitations, we propose Synergy, a framework that unites the power of both models for CQA. Synergy first unites the chart with a table as the augmented perceptual signal. Next, it unites LLMs and MLLMs, scheduling the former to decompose a question into subquestions and the latter to answer these by perceiving the chart. Lastly, it operates LLMs to summarize the subquestion-answer pairs to refine the final answer. Extensive experimental results on popular CharQA and PlotQA benchmarks reveal that, with the power of union, Synergy outperforms strong competitors and achieves superior boosts over naive MLLMs by uniting them with a smaller LLM.
D&M: Enriching E-commerce Videos with Sound Effects by Key Moment Detection and SFX Matching
Authors: Jingyu Liu; Minquan Wang; Ye Ma; Bo Wang; Aozhu Chen; Quan Chen; Peng Jiang; Xirong Li
Venue: AAAI 2025 (main)

Abstract: Videos showcasing specific products are increasingly important for E-commerce. Key moments naturally exist as the first appearance of a specific product, presentation of its distinctive features, the presence of a buying link, etc. Adding proper sound effects (SFX) to such moments, or video decoration with SFX (VDSFX), is crucial for enhancing user engaging experience. Previous work adds SFX to videos by video-to-SFX matching at a holistic level, lacking the ability of adding SFX to a specific moment. Meanwhile, previous studies on video highlight detection or video moment retrieval consider only moment localization, leaving moment to SFX matching untouched. By contrast, we propose in this paper D&M, a unified method that  accomplishes key moment detection and moment-to-SFX matching simultaneously. Moreover, for the new VDSFX task we build a large-scale dataset SFX-Moment from an E-commerce video creation platform. For a fair comparison, we build competitive baselines by extending a number of current video moment detection methods to the new task. Extensive experiments on SFX-Moment show the superior performance of the proposed method over the baselines.
Attend and Enrich: Enhanced Visual Prompt for Zero-Shot Learning
Authors: Man Liu; Huihui Bai; Feng Li; Chunjie Zhang; Yunchao Wei; Tat-Seng Chua; Yao Zhao
Venue: AAAI 2025 (main)

Abstract: Zero-shot learning (ZSL) endeavors to transfer knowledge from the seen categories to recognize unseen categories, which mostly relies on the semantic-visual interactions between image and attribute tokens. Recently, the prompt learning has emerged in ZSL and demonstrated significant potential as it allows the zero-shot transfer of diverse visual concepts to downstream tasks. However, current methods explore the fixed adaptation of the learnable prompt on the seen domains, which make them over-emphasize the primary visual features observed during training, limiting their generalization capabilities to the unseen domains. In this work, we propose AENet, which endows semantic information into the visual prompt to distill semantic-enhanced prompt for visual representation enrichment, enabling effective knowledge transfer for ZSL. AENet comprises two key steps: 1) exploring the concept-harmonized tokens for the visual and attribute modalities, grounded on the modal-sharing token that represents consistent visual-semantic concepts; and 2) yielding the semantic-enhanced prompt via the visual residual refinement unit with attribute consistency supervision. It is further integrated with primary visual features to attend to semantic-related information for visual enhancement, thus strengthening transferable ability. Experimental results on three benchmarks show that our AENet outperforms existing state-of-the-art ZSL methods.
UP-Restorer: When Unrolling Meets Prompts for Unified Image Restoration
Authors: Minghao Liu; Wenhan Yang; Jinyi Luo; Jiaying Liu
Venue: AAAI 2025 (main)

Abstract: All-in-one restoration needs to implicitly distinguish between different degradation conditions and apply specific prior constraints accordingly. To fulfill this goal, our work makes the first effort to create an all-in-one restoration via unrolling from the typical maximum a-posterior optimization function. This unrolling framework naturally leads to the construction of progressively solving models, which are equivalent to a diffusion enhancer taking as input dynamically generated prompts. Under a score-based diffusion model, the prompts are integrated for propogating and updating several context-related variables, i.e. transmission map, atmospheric light map and noise or rain map progressively. Such learned prompt generation process, which simulates the nonlinear operations in the unrolled solution, is combined with linear operations owning clear physics implications to make the diffusion models well reguarlized and more effective in learning degradation-related visual priors. Experimental results demonstrate that our method achieves significant performance improvements across various image restoration tasks, realizing true all-in-one image restoration.
LLM4GEN: Leveraging Semantic Representation of LLMs for Text-to-Image Generation
Authors: Mushui Liu; Yuhang Ma; Zhen Yang; Jun Dan; Yunlong Yu; Zeng Zhao; Zhipeng Hu; Bai Liu; Changjie Fan
Venue: AAAI 2025 (main)

Abstract: Diffusion models have exhibited substantial success in text-to-image generation. However, they often encounter challenges when dealing with complex and dense prompts involving multiple objects, attribute binding, and long descriptions. In this paper, we propose a novel framework called LLM4GEN, which enhances the semantic understanding of text-to-image diffusion models by leveraging the representation of Large Language Models (LLMs). It can be seamlessly incorporated into various diffusion models as a plug-and-play component. A specially designed Cross-Adapter Module (CAM) integrates the original text features of text-to-image models with LLM features, thereby enhancing text-to-image generation. Additionally, to facilitate and correct entity-attribute relationships in text prompts, we develop an entity-guided regularization loss to further improve generation performance. We also introduce DensePrompts, which contains 7,000 dense prompts to provide a comprehensive evaluation for the text-to-image generation task. Experiments indicate that LLM4GEN significantly improves the semantic alignment of SD1.5 and SDXL, demonstrating increases of 9.69% and 12.90% in color on T2I-CompBench, respectively. Moreover, it surpasses existing models in terms of sample quality, image-text alignment, and human evaluation.
Path-Adaptive Matting for Efficient Inference Under Various Computational Cost Constraints
Authors: Qinglin Liu; Zonglin Li; Xiaoqian Lv; Xin Sun; Ru Li; Shengping Zhang
Venue: AAAI 2025 (main)

Abstract: In this paper, we explore a novel image matting task aimed at achieving efficient inference under various computational cost constraints, specifically FLOP limitations, using a single matting network. Existing matting methods which have not explored scalable architectures or path-learning strategies, fail to tackle this challenge. To overcome these limitations, we introduce Path-Adaptive Matting (PAM), a framework that dynamically adjusts network paths based on image contexts and computational cost constraints. We formulate the training of the computational cost-constrained matting network as a bilevel optimization problem, jointly optimizing the matting network and the path estimator. Building on this formalization, we design a path-adaptive matting architecture by incorporating path selection layers and learnable connect layers to estimate optimal paths and perform efficient inference within a unified network. Furthermore, we propose a performance-aware path-learning strategy to generate path labels online by evaluating a few paths sampled from the prior distribution of optimal paths and network estimations, enabling robust and efficient online path learning. Experiments on five image matting datasets demonstrate that the proposed PAM framework achieves competitive performance across a range of computational cost constraints.