GenAI4ELab/papercli-papers huggingface dataset:
Suppress Content Shift: Better Diffusion Features via Off-the-Shelf Generation Techniques
Authors: Benyuan Meng; Qianqian Xu; Zitai Wang; Zhiyong Yang; Xiaochun Cao; Qingming Huang
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Diffusion models are powerful generative models, and this capability can also be applied to discrimination. The inner activations of a pre-trained diffusion model can serve as features for discriminative tasks, namely, diffusion feature. We discover that diffusion feature has been hindered by a hidden yet universal phenomenon that we call content shift. To be specific, there are content differences between features and the input image, such as the exact shape of a certain object. We locate the cause of content shift as one inherent characteristic of diffusion models, which suggests the broad existence of this phenomenon in diffusion feature. Further empirical study also indicates that its negative impact is not negligible even when content shift is not visually perceivable. Hence, we propose to suppress content shift to enhance the overall quality of diffusion features. Specifically, content shift is related to the information drift during the process of recovering an image from the noisy input, pointing out the possibility of turning off-the-shelf generation techniques into tools for content shift suppression. We further propose a practical guideline named GATE to efficiently evaluate the potential benefit of a technique and provide an implementation of our methodology. Despite the simplicity, the proposed approach has achieved superior results on various tasks and datasets, validating its potential as a generic booster for diffusion features. Our code is available at https://github.com/Darkbblue/diffusion-content-shift.
What Matters in Graph Class Incremental Learning? An Information Preservation Perspective
Authors: Jialu Li; Yu Wang; Pengfei Zhu; Wanyu Lin; Qinghua Hu
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Graph class incremental learning (GCIL) requires the model to classify emerging nodes of new classes while remembering old classes. Existing methods are designed to preserve effective information of old models or graph data to alleviate forgetting, but there is no clear theoretical understanding of what matters in information preservation. In this paper, we consider that present practice suffers from high semantic and structural shifts assessed by two devised shift metrics. We provide insights into information preservation in GCIL and find that maintaining graph information can preserve information of old models in theory to calibrate node semantic and graph structure shifts. We correspond graph information into low-frequency local-global information and high-frequency information in spatial domain. Based on the analysis, we propose a framework, Graph Spatial Information Preservation (GSIP). Specifically, for low-frequency information preservation, the old node representations obtained by inputting replayed nodes into the old model are aligned with the outputs of the node and its neighbors in the new model, and then old and new outputs are globally matched after pooling. For high-frequency information preservation, the new node representations are encouraged to imitate the near-neighbor pair similarity of old node representations. GSIP achieves a 10\% increase in terms of the forgetting metric compared to prior methods on large-scale datasets. Our framework can also seamlessly integrate existing replay designs. The code is available through https://github.com/Jillian555/GSIP.
PrefPaint: Aligning Image Inpainting Diffusion Model with Human Preference
Authors: Kendong Liu; Zhiyu Zhu; Chuanhao Li; Hui LIU; Huanqiang Zeng; Junhui Hou
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: In this paper, we make the first attempt to align diffusion models for image inpainting with human aesthetic standards via a reinforcement learning framework, significantly improving the quality and visual appeal of inpainted images. Specifically, instead of directly measuring the divergence with paired images, we train a reward model with the dataset we construct, consisting of nearly 51,000 images annotated with human preferences. Then, we adopt a reinforcement learning process to fine-tune the distribution of a pre-trained diffusion model for image inpainting in the direction of higher reward. Moreover, we theoretically deduce the upper bound on the error of the reward model, which illustrates the potential confidence of reward estimation throughout the reinforcement alignment process, thereby facilitating accurate regularization.
Extensive experiments on inpainting comparison and downstream tasks, such as image extension and 3D reconstruction, demonstrate the effectiveness of our approach, showing significant improvements in the alignment of inpainted images with human preference compared with state-of-the-art methods. This research not only advances the field of image inpainting but also provides a framework for incorporating human preference into the iterative refinement of generative models based on modeling reward accuracy, with broad implications for the design of visually driven AI applications. Our code and dataset are publicly available at \url{https://prefpaint.github.io}.
Most Influential Subset Selection: Challenges, Promises, and Beyond
Authors: Yuzheng Hu; Pingbang Hu; Han Zhao; Jiaqi Ma
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: How can we attribute the behaviors of machine learning models to their training data? While the classic influence function sheds light on the impact of individual samples, it often fails to capture the more complex and pronounced collective influence of a set of samples. To tackle this challenge, we study the Most Influential Subset Selection (MISS) problem, which aims to identify a subset of training samples with the greatest collective influence. We conduct a comprehensive analysis of the prevailing approaches in MISS, elucidating their strengths and weaknesses. Our findings reveal that influence-based greedy heuristics, a dominant class of algorithms in MISS, can provably fail even in linear regression. We delineate the failure modes, including the errors of influence function and the non-additive structure of the collective influence. Conversely, we demonstrate that an adaptive version of these heuristics which applies them iteratively, can effectively capture the interactions among samples and thus partially address the issues. Experiments on real-world datasets corroborate these theoretical findings, and further demonstrate that the merit of adaptivity can extend to more complex scenarios such as classification tasks and non-linear neural networks. We conclude our analysis by emphasizing the inherent trade-off between performance and computational efficiency, questioning the use of additive metrics such as the linear datamodeling score, and offering a range of discussions.
Globally Q-linear Gauss-Newton Method for Overparameterized Non-convex Matrix Sensing
Authors: Xixi Jia; Fangchen FENG; Deyu Meng; Defeng Sun
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: This paper focuses on the optimization of overparameterized, non-convex low-rank matrix sensing (LRMS)—an essential component in contemporary statistics and machine learning. Recent years have witnessed significant breakthroughs in first-order methods, such as gradient descent, for tackling this non-convex optimization problem. However, the presence of numerous saddle points often prolongs the time required for gradient descent to overcome these obstacles. Moreover, overparameterization can markedly decelerate gradient descent methods, transitioning its convergence rate from linear to sub-linear.  In this paper, we introduce an approximated Gauss-Newton (AGN) method for tackling the non-convex LRMS problem. Notably, AGN incurs a computational cost comparable to gradient descent per iteration but converges much faster without being slowed down by saddle points. We prove that, despite the non-convexity of the objective function, AGN achieves Q-linear convergence from random initialization to the global optimal solution. The global Q-linear convergence of AGN represents a substantial enhancement over the convergence of the existing methods for the overparameterized non-convex LRMS. The code for this paper is available at \url{https://github.com/hsijiaxidian/AGN}.
One for All: Multi-Domain Joint Training for Point Cloud Based 3D Object Detection
Authors: Zhenyu Wang; Ya-Li Li; Hengshuang Zhao; Shengjin Wang
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: The current trend in computer vision is to utilize one universal model to address all various tasks. Achieving such a universal model inevitably requires incorporating multi-domain data for joint training to learn across multiple problem scenarios. In point cloud based 3D object detection, however, such multi-domain joint training is highly challenging, because large domain gaps among point clouds from different datasets lead to the severe domain-interference problem. In this paper, we propose OneDet3D, a universal one-for-all model that addresses 3D detection across different domains, including diverse indoor and outdoor scenes, within the same framework and only one set of parameters. We propose the domain-aware partitioning in scatter and context, guided by a routing mechanism, to address the data interference issue, and further incorporate the text modality for a language-guided classification to unify the multi-dataset label spaces and mitigate the category interference issue. The fully sparse structure and anchor-free head further accommodate point clouds with significant scale disparities. Extensive experiments demonstrate the strong universal ability of OneDet3D to utilize only one trained model for addressing almost all 3D object detection tasks (Fig. 1). We will open-source the code for future research and applications.
GenArtist: Multimodal LLM as an Agent for Unified Image Generation and Editing
Authors: Zhenyu Wang; Aoxue Li; Zhenguo Li; Xihui Liu
Venue: NeurIPS 2024 (NeurIPS 2024 spotlight)

Abstract: Despite the success achieved by existing image generation and editing methods, current models still struggle with complex problems including intricate text prompts, and the absence of  verification and self-correction mechanisms makes the generated images unreliable. Meanwhile, a single model tends to specialize in particular tasks and possess the corresponding capabilities, making it inadequate for fulfilling all user requirements. We propose GenArtist, a unified image generation and editing system, coordinated by a multimodal large language model (MLLM) agent. We integrate a comprehensive range of existing models into the tool library and utilize the agent for tool selection and execution. For a complex problem, the MLLM agent decomposes it into simpler sub-problems and constructs a tree structure to systematically plan the procedure of generation, editing, and self-correction with step-by-step verification. By automatically generating missing position-related inputs and incorporating position information, the appropriate tool can be effectively employed to address each sub-problem. Experiments demonstrate that GenArtist can perform various generation and editing tasks, achieving state-of-the-art performance and surpassing existing models such as SDXL and DALL-E 3, as can be seen in Fig. 1. We will open-source the code for future research and applications.
FuseMoE: Mixture-of-Experts Transformers for Fleximodal Fusion
Authors: Xing Han; Huy Nguyen; Carl William Harris; Nhat Ho; Suchi Saria
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: As machine learning models in critical fields increasingly grapple with multimodal data, they face the dual challenges of handling a wide array of modalities, often incomplete due to missing elements, and the temporal irregularity and sparsity of collected samples. Successfully leveraging this complex data, while overcoming the scarcity of high-quality training samples, is key to improving these models' predictive performance. We introduce ``FuseMoE'', a mixture-of-experts framework incorporated with an innovative gating function. Designed to integrate a diverse number of modalities, FuseMoE is effective in managing scenarios with missing modalities and irregularly sampled data trajectories. Theoretically, our unique gating function contributes to enhanced convergence rates, leading to better performance in multiple downstream tasks. The practical utility of FuseMoE in the real world is validated by a diverse set of challenging prediction tasks.
Voxel Proposal Network via Multi-Frame Knowledge Distillation for Semantic Scene Completion
Authors: Lubo Wang; Di Lin; Kairui Yang; Ruonan Liu; Qing Guo; Wuyuan Xie; Miaohui Wang; Lingyu Liang; Yi Wang; Ping Li
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: Semantic scene completion is a difficult task that involves completing the geometry and semantics of a scene from point clouds in a large-scale environment. Many current methods use 3D/2D convolutions or attention mechanisms, but these have limitations in directly constructing geometry and accurately propagating features from related voxels, the completion likely fails while propagating features in a single pass without considering multiple potential pathways. And they are generally only suitable for static scenes and struggle to handle dynamic aspects. This paper introduces Voxel Proposal Network (VPNet) that completes scenes from 3D and Bird's-Eye-View (BEV) perspectives. It includes Confident Voxel Proposal based on voxel-wise coordinates to propose confident voxels with high reliability for completion. This method reconstructs the scene geometry and implicitly models the uncertainty of voxel-wise semantic labels by presenting multiple possibilities for voxels. VPNet employs Multi-Frame Knowledge Distillation based on the point clouds of multiple adjacent frames to accurately predict the voxel-wise labels by condensing various possibilities of voxel relationships. VPNet has shown superior performance and achieved state-of-the-art results on the SemanticKITTI and SemanticPOSS datasets.
Motion Graph Unleashed: A Novel Approach to Video Prediction
Authors: Yiqi Zhong; Luming Liang; Bohan Tang; Ilya Zharkov; Ulrich Neumann
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: We introduce motion graph, a novel approach to address the video prediction problem, i.e., predicting future video frames from limited past data. The motion graph transforms patches of video frames into interconnected graph nodes, to comprehensively describe the spatial-temporal relationships among them. This representation overcomes the limitations of existing motion representations such as image differences, optical flow, and motion matrix that either fall short in capturing complex motion patterns or suffer from excessive memory consumption. We further present a video prediction pipeline empowered by motion graph, exhibiting substantial performance improvements and cost reductions. Extensive experiments on various datasets, including UCF Sports, KITTI and Cityscapes, highlight the strong representative ability of motion graph. Especially on UCF Sports, our method matches and outperforms the SOTA methods with a significant reduction in model size by 78% and a substantial decrease in GPU memory utilization by 47%.
Generative Retrieval Meets Multi-Graded Relevance
Authors: Yubao Tang; Ruqing Zhang; Jiafeng Guo; Maarten de Rijke; Wei Chen; Xueqi Cheng
Venue: NeurIPS 2024 (NeurIPS 2024 spotlight)

Abstract: Generative retrieval represents a novel approach to information retrieval, utilizing an encoder-decoder architecture to directly produce relevant document identifiers (docids) for queries. While this method offers benefits, current implementations are limited to scenarios with binary relevance data, overlooking the potential for documents to have multi-graded relevance. Extending generative retrieval to accommodate multi-graded relevance poses challenges, including the need to reconcile likelihood probabilities for docid pairs and the possibility of multiple relevant documents sharing the same identifier. To address these challenges, we introduce a new framework called GRaded Generative Retrieval (GR$^2$). Our approach focuses on two key components: ensuring relevant and distinct identifiers, and implementing multi-graded constrained contrastive training. Firstly, we aim to create identifiers that are both semantically relevant and sufficiently distinct to represent individual documents effectively. This is achieved by jointly optimizing the relevance and distinctness of docids through a combination of docid generation and autoencoder models. Secondly, we incorporate information about the relationship between relevance grades to guide the training process. Specifically, we leverage a constrained contrastive training strategy to bring the representations of queries and the identifiers of their relevant documents closer together, based on their respective relevance grades.Extensive experiments on datasets with both multi-graded and binary relevance demonstrate the effectiveness of our method.
DC-Gaussian: Improving 3D Gaussian Splatting for Reflective Dash Cam Videos
Authors: Linhan Wang; Kai Cheng; Shuo Lei; Shengkun Wang; Wei Yin; Chenyang Lei; Xiaoxiao Long; Chang-Tien Lu
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: We present DC-Gaussian, a new method for generating novel views from in-vehicle dash cam videos. While neural rendering techniques have made significant strides in driving scenarios, existing methods are primarily designed for videos collected by autonomous vehicles. However, these videos are limited in both quantity and diversity compared to dash cam videos, which are more widely used across various types of vehicles and capture a broader range of scenarios. Dash cam videos often suffer from severe obstructions such as reflections and occlusions on the windshields, which significantly impede the application of neural rendering techniques. To address this challenge, we develop DC-Gaussian based on the recent real-time neural rendering technique 3D Gaussian Splatting (3DGS). Our approach includes an adaptive image decomposition module to model reflections and occlusions in a unified manner. Additionally, we introduce illumination-aware obstruction modeling to manage reflections and occlusions under varying lighting conditions. Lastly, we employ a geometry-guided Gaussian enhancement strategy to improve rendering details by incorporating additional geometry priors. Experiments on self-captured and public dash cam videos show that our method not only achieves state-of-the-art performance in novel view synthesis, but also accurately reconstructing captured scenes getting rid of obstructions.
SocraticLM: Exploring Socratic Personalized Teaching with Large Language Models
Authors: Jiayu Liu; Zhenya Huang; Tong Xiao; Jing Sha; Jinze Wu; Qi Liu; Shijin Wang; Enhong Chen
Venue: NeurIPS 2024 (NeurIPS 2024 spotlight)

Abstract: Large language models (LLMs) are considered a crucial technology for advancing intelligent education since they exhibit the potential for an in-depth understanding of teaching scenarios and providing students with personalized guidance. Nonetheless, current LLM-based application in personalized teaching predominantly follows a "Question-Answering" paradigm, where students are passively provided with answers and explanations. In this paper, we propose SocraticLM, which achieves a Socratic "Thought-Provoking" teaching paradigm that fulfills the role of a real classroom teacher in actively engaging students in the thought process required for genuine problem-solving mastery. To build SocraticLM, we first propose a novel "Dean-Teacher-Student" multi-agent pipeline to construct a new dataset, SocraTeach, which contains $35$K meticulously crafted Socratic-style multi-round (equivalent to $208$K single-round) teaching dialogues grounded in fundamental mathematical problems. Our dataset simulates authentic teaching scenarios, interacting with six representative types of simulated students with different cognitive states, and strengthening four crucial teaching abilities. SocraticLM is then fine-tuned on SocraTeach with three strategies balancing its teaching and reasoning abilities. Moreover, we contribute a comprehensive evaluation system encompassing five pedagogical dimensions for assessing the teaching quality of LLMs. Extensive experiments verify that SocraticLM achieves significant improvements in the teaching performance, outperforming GPT4 by more than 12\%. Our dataset and code is available at https://github.com/Ljyustc/SocraticLM.
Text-DiFuse: An Interactive Multi-Modal Image Fusion Framework based on Text-modulated Diffusion Model
Authors: Hao Zhang; Lei Cao; Jiayi Ma
Venue: NeurIPS 2024 (NeurIPS 2024 spotlight)

Abstract: Existing multi-modal image fusion methods fail to address the compound degradations presented in source images, resulting in fusion images plagued by noise, color bias, improper exposure, etc. Additionally, these methods often overlook the specificity of foreground objects, weakening the salience of the objects of interest within the fused images. To address these challenges, this study proposes a novel interactive multi-modal image fusion framework based on the text-modulated diffusion model, called Text-DiFuse. First, this framework integrates feature-level information integration into the diffusion process, allowing adaptive degradation removal and multi-modal information fusion. This is the first attempt to deeply and explicitly embed information fusion within the diffusion process, effectively addressing compound degradation in image fusion. Second, by embedding the combination of the text and zero-shot location model into the diffusion fusion process, a text-controlled fusion re-modulation strategy is developed. This enables user-customized text control to improve fusion performance and highlight foreground objects in the fused images. Extensive experiments on diverse public datasets show that our Text-DiFuse achieves state-of-the-art fusion performance across various scenarios with complex degradation. Moreover, the semantic segmentation experiment validates the significant enhancement in semantic performance achieved by our text-controlled fusion re-modulation strategy. The code is publicly available at https://github.com/Leiii-Cao/Text-DiFuse.
Personalized Adapter for Large Meteorology Model on Devices: Towards Weather Foundation Models
Authors: Shengchao Chen; Guodong Long; Jing Jiang; Chengqi Zhang
Venue: NeurIPS 2024 (NeurIPS 2024 poster)

Abstract: This paper demonstrates that pre-trained language models (PLMs) are strong foundation models for on-device meteorological variable modeling. We present LM-Weather, a generic approach to taming PLMs, that have learned massive sequential knowledge from the universe of natural language databases, to acquire an immediate capability to obtain highly customized models for heterogeneous meteorological data on devices while keeping high efficiency. Concretely, we introduce a lightweight personalized adapter into PLMs and endows it with weather pattern awareness. During communication between clients and the server, low-rank-based transmission is performed to effectively fuse the global knowledge among devices while maintaining high communication efficiency and ensuring privacy. Experiments on real-wold dataset show that LM-Weather outperforms the state-of-the-art results by a large margin across various tasks (e.g., forecasting and imputation at different scales). We provide extensive and in-depth analyses experiments, which verify that LM-Weather can (1) indeed leverage sequential knowledge from natural language to accurately handle meteorological sequence, (2) allows each devices obtain highly customized models under significant heterogeneity, and (3) generalize under data-limited and out-of-distribution (OOD) scenarios.