OpenRaiser/Intern-Atlas huggingface dataset:
Unified category and style generalization for instance-level sketch retrieval
Authors: Zechao Hu, Zhengwei Yang, Hao Li, Yixiong Zou, Fengbin Zhu, Zheng Wang
Venue: Conference SIGIR 2025
Year: 2025

Zero-shot instance-level sketch retrieval addresses a practical retrieval scenario in which sketches from unseen categories during training serve as queries to retrieve matching RGB images. The core challenges of this task lie in two aspects: unknown category generalization and subjective style adaptation. Existing methods either focus solely on category generalization or apply simplistic style elimination techniques within a specific category, leading to suboptimal performance when both challenges are present. To this end, we propose the Dual-Attentive Prompt (DAP) method, which unifies category generalization and style adaptation into a single, interpretable framework. Central to DAP is a dual-attentive prompt composer, consisting of two self-attention-based modules. This composer dynamically integrates pre-learned category-specific knowledge with instance-specific prompts that adapt to sketch-specific styles. By cooperating with additional style alignment loss, the proposed method ensures robust generalization of unseen categories while mitigating the impact of subjective style variations. Extensive experimental results demonstrate the state-of-the-art performance of the proposed method. Additionally, some insights are provided into the challenges of traditional training processes when handling multi-style sketches, along with quantitative and qualitative evidence showing how the proposed approach effectively mitigates the negative impact of subjective style variations.
LDMapNet-U: An end-to-end system for city-scale lane-level map updating
Authors: Deguo Xia, Weiming Zhang, Xiyan Liu, Wei Zhang, Chenting Gong, Xiao Tan, Jizhou Huang, Mengmeng Yang, Diange Yang
Venue: Conference KDD 2025
Year: 2025

An up-to-date city-scale lane-level map is an indispensable infrastructure and a key enabling technology for ensuring the safety and user experience of autonomous driving systems. In industrial scenarios, reliance on manual annotation for map updates creates a critical bottleneck. Lane-level updates require precise change information and must ensure consistency with adjacent data while adhering to strict standards. Traditional methods utilize a three-stage approach -- construction, change detection, and updating -- which often necessitates manual verification due to accuracy limitations. This results in labor-intensive processes and hampers timely updates. To address these challenges, we propose LDMapNet-U, which implements a new end-to-end paradigm for city-scale lane-level map updating. By reconceptualizing the update task as an end-to-end map generation process grounded in historical map data, we introduce a paradigm shift in map updating that simultaneously generates vectorized maps and change information. To achieve this, a Prior-Map Encoding (PME) module is introduced to effectively encode historical maps, serving as a critical reference for detecting changes. Additionally, we incorporate a novel Instance Change Prediction (ICP) module that learns to predict associations with historical maps. Consequently, LDMapNet-U simultaneously achieves vectorized map element generation and change detection. To demonstrate the superiority and effectiveness of LDMapNet-U, extensive experiments are conducted using large-scale real-world datasets. In addition, LDMapNet-U has been successfully deployed in production at Baidu Maps since April 2024, supporting lane-level map updating for over 360 cities and significantly shortening the update cycle from quarterly to weekly, thereby enhancing the timeliness and accuracy of lane-level map. The nationwide, high-frequency city-scale lane-level map has been instrumental in the development of the lane-level navigation product serving hundreds of millions of users, while also integrating into the autonomous driving systems of several leading vehicle companies.
An active illumination and appearance (aia) model for face alignment
Authors: Fatih Kahraman, Muhittin Gökmen, Sune Darkner, Rasmus Larsen 0001
Venue: Conference CVPR 2007
Year: 2007

Face recognition systems are typically required to work under highly varying illumination conditions. This leads to complex effects imposed on the acquired face image that pertains little to the actual identity. Consequently, illumination normalization is required to reach acceptable recognition rates in face recognition systems. In this paper, we propose an approach that integrates the face identity and illumination models under the widely used active appearance model framework as an extension to the texture model in order to obtain illumination-invariant face localization.
Shape, albedo, and illumination from a single image of an unknown object
Authors: Jonathan T. Barron, Jitendra Malik
Venue: Conference CVPR 2012
Year: 2012

We address the problem of recovering shape, albedo, and illumination from a single grayscale image of an object, using shading as our primary cue. Because this problem is fundamentally underconstrained, we construct statistical models of albedo and shape, and define an optimization problem that searches for the most likely explanation of a single image. We present two priors on albedo which encourage local smoothness and global sparsity, and three priors on shape which encourage flatness, outward-facing orientation at the occluding contour, and local smoothness. We present an optimization technique for using these priors to recover shape, albedo, and a spherical harmonic model of illumination. Our model, which we call SAIFS (shape, albedo, and illumination from shading) produces reasonable results on arbitrary grayscale images taken in the real world, and outperforms all previous grayscale “intrinsic image” - style algorithms on the MIT Intrinsic Images dataset.
Latent structured models for human pose estimation
Authors: Catalin Ionescu, Fuxin Li, Cristian Sminchisescu
Venue: Conference ICCV 2011
Year: 2011

We present an approach for automatic 3D human pose reconstruction from monocular images, based on a discriminative formulation with latent segmentation inputs. We advanced the field of structured prediction and human pose reconstruction on several fronts. First, by working with a pool of figure-ground segment hypotheses, the prediction problem is formulated in terms of combined learning and inference over segment hypotheses and 3D human articular configurations. Beside constructing tractable formulations for the combined segment selection and pose estimation problem, we propose new augmented kernels that can better encode complex dependencies between output variables. Furthermore, we provide primal linear re-formulations based on Fourier kernel approximations, in order to scale-up the non-linear latent structured prediction methodology. The proposed models are shown to be competitive in the HumanEva benchmark and are also illustrated in a clip collected from a Hollywood movie, where the model can infer human poses from monocular images captured in complex environments.
certified!!) adversarial robustness for free!
Authors: Nicholas Carlini, Florian Tramèr, Krishnamurthy (Dj) Dvijotham, Leslie Rice, Mingjie Sun, J. Zico Kolter
Venue: Conference ICLR 2023
Year: 2023

In this paper we show how to achieve state-of-the-art certified adversarial robustness to 2-norm bounded perturbations by relying exclusively on off-the-shelf pretrained models. To do so, we instantiate the denoised smoothing approach of Salman et al. 2020 by combining a pretrained denoising diffusion probabilistic model and a standard high-accuracy classifier. This allows us to certify 71% accuracy on ImageNet under adversarial perturbations constrained to be within an 2-norm of 0.5, an improvement of 14 percentage points over the prior certified SoTA using any approach, or an improvement of 30 percentage points over denoised smoothing. We obtain these results using only pretrained diffusion models and image classifiers, without requiring any fine tuning or retraining of model parameters.
A decision tree framework for spatiotemporal sequence prediction
Authors: Taehwan Kim, Yisong Yue, Sarah L. Taylor, Iain A. Matthews
Venue: Conference KDD 2015
Year: 2015

We study the problem of learning to predict a spatiotemporal output sequence given an input sequence. In contrast to conventional sequence prediction problems such as part-of-speech tagging (where output sequences are selected using a relatively small set of discrete labels), our goal is to predict sequences that lie within a high-dimensional continuous output space. We present a decision tree framework for learning an accurate non-parametric spatiotemporal sequence predictor. Our approach enjoys several attractive properties, including ease of training, fast performance at test time, and the ability to robustly tolerate corrupted training data using a novel latent variable approach. We evaluate on several datasets, and demonstrate substantial improvements over existing decision tree based sequence learning frameworks such as SEARN and DAgger.
Resurrecting label propagation for graphs with heterophily and label noise
Authors: Yao Cheng 0009, Caihua Shan, Yifei Shen 0004, Xiang Li 0067, Siqiang Luo, Dongsheng Li 0002
Venue: Conference KDD 2024
Year: 2024

Label noise is a common challenge in large datasets, as it can significantly degrade the generalization ability of deep neural networks. Most existing studies focus on noisy labels in computer vision; however, graph models encompass both node features and graph topology as input, and become more susceptible to label noise through message-passing mechanisms. Recently, only a few works have been proposed to tackle the label noise on graphs. One significant limitation is that they operate under the assumption that the graph exhibits homophily and that the labels are distributed smoothly. However, real-world graphs can exhibit varying degrees of heterophily, or even be dominated by heterophily, which results in the inadequacy of the current methods. In this paper, we study graph label noise in the context of arbitrary heterophily, with the aim of rectifying noisy labels and assigning labels to previously unlabeled nodes. We begin by conducting two empirical analyses to explore the impact of graph homophily on graph label noise. Following observations, we propose a efficient algorithm, denoted as $R^{2}LP$. Specifically, $R^{2}LP$ is an iterative algorithm with three steps: (1) reconstruct the graph to recover the homophily property, (2) utilize label propagation to rectify the noisy labels, (3) select high-confidence labels to retain for the next iteration. By iterating these steps, we obtain a set of correct labels, ultimately achieving high accuracy in the node classification task. The theoretical analysis is also provided to demonstrate its remarkable denoising effect. Finally, we perform experiments on ten benchmark datasets with different levels of graph heterophily and various types of noise. In these experiments, we compare the performance of $R^{2}LP$ against ten typical baseline methods. Our results illustrate the superior performance of the proposed $R^{2}LP$.
A co-training approach for noisy time series learning
Authors: Weiqi Zhang, Jianfeng Zhang, Jia Li, Fugee Tsung
Venue: Conference CIKM 2023
Year: 2023

In this work, we focus on robust time series representation learning. Our assumption is that real-world time series is noisy and complementary information from different views of the same time series plays an important role while analyzing noisy input. Based on this, we create two views for the input time series through two different encoders. We conduct co-training based contrastive learning iteratively to learn the encoders. Our experiments demonstrate that this co-training approach leads to a significant improvement in performance. Especially, by leveraging the complementary information from different views, our proposed TS-CoT method can mitigate the impact of data noise and corruption. Empirical evaluations on four time series benchmarks in unsupervised and semi-supervised settings reveal that TS-CoT outperforms existing methods. Furthermore, the representations learned by TS-CoT can transfer well to downstream tasks through fine-tuning1.
Evaluation of super-voxel methods for early video processing
Authors: Chenliang Xu, Jason J. Corso
Venue: Conference CVPR 2012
Year: 2012

Supervoxel segmentation has strong potential to be incorporated into early video analysis as superpixel segmentation has in image analysis. However, there are many plausible supervoxel methods and little understanding as to when and where each is most appropriate. Indeed, we are not aware of a single comparative study on supervoxel segmentation. To that end, we study five supervoxel algorithms in the context of what we consider to be a good supervoxel: namely, spatiotemporal uniformity, object/region boundary detection, region compression and parsimony. For the evaluation we propose a comprehensive suite of 3D volumetric quality metrics to measure these desirable supervoxel characteristics. We use three benchmark video data sets with a variety of content-types and varying amounts of human annotations. Our findings have led us to conclusive evidence that the hierarchical graph-based and segmentation by weighted aggregation methods perform best and almost equally-well on nearly all the metrics and are the methods of choice given our proposed assumptions.
Meta-guided adaptive weight learner for noisy correspondence
Authors: Chenyu Mu, Erkun Yang, Cheng Deng
Venue: Conference SIGIR 2025
Year: 2025

Cross-modal retrieval with noisy correspondences is a critical challenge, especially when data annotations for large-scale multimodal datasets are prone to systematic corruption. To mitigate the impact of noise, many existing methods rely on small-loss sample selection to filter out clean samples. However, these methods can ineluctably result in the inclusion of false positives, which significantly degrade the performance. To tackle this issue, we propose a novel method, named the Meta Similarity Importance Assignment Network (MSIAN), to achieve robust cross-modal retrieval. MSIAN employs a meta-learning strategy to dynamically learn the importance of each sample through a two-level optimization process. With adaptively guiding the learning process, MSIAN adjusts the importance weight of each sample based on its inherent trustworthiness. Thereby, thus iterative mechanism progressively shifts the network's focus on the most reliable data points, amplifying the impact of credible samples while diminishing the adaptive weight of noisy ones. Furthermore, MSIAN dynamically adapts the soft margin of each sample through continuously updated adaptive weights, thereby improving the robustness of the model. Extensive experiments on three widely used datasets, including Flickr30K, MS-COCO, and Conceptual Captions, demonstrate the effectiveness of our approach in improving cross-modal retrieval performance.
On evaluation metrics for diversity-enhanced recommendations
Authors: Xueqi Li, Gao Cong, Guoqing Xiao, Yang Xu, Wenjun Jiang, Kenli Li
Venue: Conference CIKM 2024
Year: 2024

Diversity is increasingly recognized as a crucial factor in recommendation systems for enhancing user satisfaction. However, existing studies on diversity-enhanced recommendation systems primarily focus on designing recommendation strategies, often overlooking the development of evaluation metrics. Widely used diversity metrics such as CC, ILAD, and ILMD are typically assessed independently of accuracy. This separation leads to a critical limitation: existing diversity measures are unable to distinguish between diversity improvements from effective recommendations and those from in effective recommendations. Our evaluations reveal that the diversity improvements are primarily contributed by ineffective recommendations, which often do not positively contribute to user satisfaction. Furthermore, existing diversity metrics disregard the feature distribution of ground-truth items, potentially skewing the assessment of diversity performance. To address these limitations, we design three new accuracy-aware metrics: DCC, FDCC, and DILAD, and conduct a re-evaluation using these metrics. Surprisingly, our results illustrate that the diversity improvements of existing diversity-enhanced approaches are limited and even negative compared to those of accurate recommendations. This finding underscores the need to explore more sophisticated diversity-enhanced techniques for improving the diversity within effective recommendations.
Real time simultaneous localisation and mapping with a single camera
Authors: Andrew J. Davison
Venue: Conference ICCV 2003
Year: 2003

Ego-motion estimation for an agile single camera moving through general, unknown scenes becomes a much more challenging problem when real-time performance is required rather than under the off-line processing conditions under which most successful structure from motion work has been achieved. This task of estimating camera motion from measurements of a continuously expanding set of self-mapped visual features is one of a class of problems known as Simultaneous Localisation and Mapping (SLAM) in the robotics community, and we argue that such real-time mapping research, despite rarely being camera-based, is more relevant here than off-line structure from motion methods due to the more fundamental emphasis placed on propagation of uncertainty. We present a top-down Bayesian framework for single-camera localisation via mapping of a sparse set of natural features using motion modelling and an information-guided active measurement strategy, in particular addressing the difficult issue of real-time feature initialisation via a factored sampling approach. Real-time handling of uncertainty permits robust localisation via the creating and active measurement of a sparse map of landmarks such that regions can be re-visited after periods of neglect and localisation can continue through periods when few features are visible. Results are presented of real-time localisation for a hand-waved camera with very sparse prior scene knowledge and all processing carried out on a desktop PC.
Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids
Authors: Yunzhu Li, Jiajun Wu 0001, Russ Tedrake, Joshua B. Tenenbaum, Antonio Torralba 0001
Venue: Conference ICLR 2019
Year: 2019

Real-life control tasks involve matters of various substances---rigid or soft bodies, liquid, gas---each with distinct physical behaviors. This poses challenges to traditional rigid-body physics engines. Particle-based simulators have been developed to model the dynamics of these complex scenes; however, relying on approximation techniques, their simulation often deviates from real-world physics, especially in the long term. In this paper, we propose to learn a particle-based simulator for complex control tasks. Combining learning with particle-based systems brings in two major benefits: first, the learned simulator, just like other particle-based systems, acts widely on objects of different materials; second, the particle-based representation poses strong inductive bias for learning: particles of the same type have the same dynamics within. This enables the model to quickly adapt to new environments of unknown dynamics within a few observations. We demonstrate robots achieving complex manipulation tasks using the learned simulator, such as manipulating fluids and deformable foam, with experiments both in simulation and in the real world. Our study helps lay the foundation for robot learning of dynamic scenes with particle-based representations.
Causal inference with latent variables: Recent advances and future prospectives
Authors: Yaochen Zhu, Yinhan He, Jing Ma 0002, Mengxuan Hu, Sheng Li 0001, Jundong Li
Venue: Conference KDD 2024
Year: 2024

Causality lays the foundation for the trajectory of our world. Causal inference (CI), which aims to infer intrinsic causal relations among variables of interest, has emerged as a crucial research topic. Nevertheless, the lack of observation of important variables (e.g., confounders, mediators, exogenous variables, etc.) severely compromises the reliability of CI methods. The issue may arise from the inherent difficulty in measuring the variables. Additionally, in observational studies where variables are passively recorded, certain covariates might be inadvertently omitted by the experimenter. Depending on the type of unobserved variables and the specific CI task, various consequences can be incurred if these latent variables are carelessly handled, such as biased estimation of causal effects, incomplete understanding of causal mechanisms, lack of individual-level causal consideration, etc. In this survey, we provide a comprehensive review of recent developments in CI with latent variables. We start by discussing traditional CI techniques when variables of interest are assumed to be fully observed. Afterward, under the taxonomy of circumvention and inference-based methods, we provide an in-depth discussion of various CI strategies to handle latent variables, covering the tasks of causal effect estimation, mediation analysis, counterfactual reasoning, and causal discovery. Furthermore, we generalize the discussion to graph data where interference among units may exist. Finally, we offer fresh aspects for further advancement of CI with latent variables, especially new opportunities in the era of large language models (LLMs).
Gradient-based learning of higher-order image features
Authors: Roland Memisevic
Venue: Conference ICCV 2011
Year: 2011

Recent work on unsupervised feature learning has shown that learning on polynomial expansions of input patches, such as on pair-wise products of pixel intensities, can improve the performance of feature learners and extend their applicability to spatio-temporal problems, such as human action recognition or learning of image transformations. Learning of such higher order features, however, has been much more difficult than standard dictionary learning, because of the high dimensionality and because standard learning criteria are not applicable. Here, we show how one can cast the problem of learning higher-order features as the problem of learning a parametric family of manifolds. This allows us to apply a variant of a de-noising autoencoder network to learn higher-order features using simple gradient based optimization. Our experiments show that the approach can outperform existing higher-order models, while training and inference are exact, fast, and simple.
Nc2d: Novel class discovery for node classification
Authors: Yue Hou, Xueyuan Chen, He Zhu, Ruomei Liu, Bowen Shi, Jiaheng Liu, Junran Wu, Ke Xu
Venue: Conference CIKM 2024
Year: 2024

Novel Class Discovery (NCD) involves identifying new categories within unlabeled data by utilizing knowledge acquired from previously established categories. However, existing NCD methods often struggle to maintain a balance between the performance of old and new categories. Discovering unlabeled new categories in a class-incremental way is more practical but also more challenging, as it is frequently hindered by either catastrophic forgetting of old categories or an inability to learn new ones. Furthermore, the implementation of NCD on continuously scalable graph-structured data remains an under-explored area. In response to these challenges, we introduce for the first time a more practical NCD scenario for node classification (i.e., NC-NCD), and propose a novel self-training framework with prototype replay and distillation called SWORD, adopted to our NC-NCD setting. Our approach enables the model to cluster unlabeled new category nodes after learning labeled nodes while preserving performance on old categories without reliance on old category nodes. SWORD achieves this by employing a self-training strategy to learn new categories and preventing the forgetting of old categories through the joint use of feature prototypes and knowledge distillation. Extensive experiments on four common benchmarks demonstrate the superiority of SWORD over other state-of-the-art methods.