OpenRaiser/Intern-Atlas huggingface dataset:
A rank-order distance based clustering algorithm for face tagging
Authors: Chunhui Zhu, Fang Wen 0001, Jian Sun 0001
Venue: Conference CVPR 2011
Year: 2011

We present a novel clustering algorithm for tagging a face dataset (e. g., a personal photo album). The core of the algorithm is a new dissimilarity, called Rank-Order distance, which measures the dissimilarity between two faces using their neighboring information in the dataset. The Rank-Order distance is motivated by an observation that faces of the same person usually share their top neighbors. Specifically, for each face, we generate a ranking order list by sorting all other faces in the dataset by absolute distance (e. g., L1 or L2 distance between extracted face recognition features). Then, the Rank-Order distance of two faces is calculated using their ranking orders. Using the new distance, a Rank-Order distance based clustering algorithm is designed to iteratively group all faces into a small number of clusters for effective tagging. The proposed algorithm outperforms competitive clustering algorithms in term of both precision/recall and efficiency.
Decision boundary analysis of adversarial examples
Authors: Warren He, Bo Li 0026, Dawn Song
Venue: Conference ICLR 2018
Year: 2018

Deep neural networks (DNNs) are vulnerable to adversarial examples, which are carefully crafted instances aiming to cause prediction errors for DNNs. Recent research on adversarial examples has examined local neighborhoods in the input space of DNN models. However, previous work has limited what regions to consider, focusing either on low-dimensional subspaces or small balls. In this paper, we argue that information from larger neighborhoods, such as from more directions and from greater distances, will better characterize the relationship between adversarial examples and the DNN models. First, we introduce an attack, OPTMARGIN, that generates adversarial examples robust to small perturbations. These examples successfully evade a defense that only considers a small ball around an input instance. Second, we analyze a larger neighborhood around input instances by looking at properties of surrounding decision boundaries, namely the distances to the boundaries and the adjacent classes. We find that the boundaries around these adversarial examples do not resemble the boundaries around benign examples. Finally, we show that, under scrutiny of the surrounding decision boundaries, our OPTMARGIN examples do not convincingly mimic benign examples. Although our experiments are limited to a few specific attacks, we hope these findings will motivate new, more evasive attacks and ultimately, effective defenses.
The devil is in the wrongly-classified samples: Towards unified open-set recognition
Authors: Jun Cen, Di Luan, Shiwei Zhang 0001, Yixuan Pei, Yingya Zhang, Deli Zhao, Shaojie Shen, Qifeng Chen 0001
Venue: Conference ICLR 2023
Year: 2023

Open-set Recognition (OSR) aims to identify test samples whose classes are not seen during the training process. Recently, Unified Open-set Recognition (UOSR) has been proposed to reject not only unknown samples but also known but wrongly classified samples, which tends to be more practical in real-world applications. The UOSR draws little attention since it is proposed, but we find sometimes it is even more practical than OSR in the real world applications, as evaluation results of known but wrongly classified samples are also wrong like unknown samples. In this paper, we deeply analyze the UOSR task under different training and evaluation settings to shed light on this promising research direction. For this purpose, we first evaluate the UOSR performance of several OSR methods and show a significant finding that the UOSR performance consistently surpasses the OSR performance by a large margin for the same method. We show that the reason lies in the known but wrongly classified samples, as their uncertainty distribution is extremely close to unknown samples rather than known and correctly classified samples. Second, we analyze how the two training settings of OSR (i.e., pre-training and outlier exposure) influence the UOSR. We find although they are both beneficial for distinguishing known and correctly classified samples from unknown samples, pre-training is also helpful for identifying known but wrongly classified samples while outlier exposure is not. In addition to different training settings, we also formulate a new evaluation setting for UOSR which is called few-shot UOSR, where only one or five samples per unknown class are available during evaluation to help identify unknown samples. We propose FS-KNNS for the few-shot UOSR to achieve state-of-the-art performance under all settings.
Ordinal hyperplanes ranker with cost sensitivities for age estimation
Authors: Kuang-Yu Chang, Chu-Song Chen, Yi-Ping Hung
Venue: Conference CVPR 2011
Year: 2011

In this paper, we propose an ordinal hyperplane ranking algorithm called OHRank, which estimates human ages via facial images. The design of the algorithm is based on the relative order information among the age labels in a database. Each ordinal hyperplane separates all the facial images into two groups according to the relative order, and a cost-sensitive property is exploited to find better hyperplanes based on the classification costs. Human ages are inferred by aggregating a set of preferences from the ordinal hyperplanes with their cost sensitivities. Our experimental results demonstrate that the proposed approach outperforms conventional multiclass-based and regression-based approaches as well as recently developed ranking-based age estimation approaches.
Scalable recognition with a vocabulary tree
Authors: David Nistér, Henrik Stewénius
Venue: Conference CVPR 2006
Year: 2006

A recognition scheme that scales efficiently to a large number of objects is presented. The efficiency and quality is exhibited in a live demonstration that recognizes CD-covers from a database of 40000 images of popular music CD’s. The scheme builds upon popular techniques of indexing descriptors extracted from local regions, and is robust to background clutter and occlusion. The local region descriptors are hierarchically quantized in a vocabulary tree. The vocabulary tree allows a larger and more discriminatory vocabulary to be used efficiently, which we show experimentally leads to a dramatic improvement in retrieval quality. The most significant property of the scheme is that the tree directly defines the quantization. The quantization and the indexing are therefore fully integrated, essentially being one and the same. The recognition quality is evaluated through retrieval on a database with ground truth, showing the power of the vocabulary tree approach, going as high as 1 million images.
Locally rigid globally non-rigid surface registration
Authors: Kent Fujiwara, Ko Nishino, Jun Takamatsu, Bo Zheng 0001, Katsushi Ikeuchi
Venue: Conference ICCV 2011
Year: 2011

We present a novel non-rigid surface registration method that achieves high accuracy and matches characteristic features without manual intervention. The key insight is to consider the entire shape as a collection of local structures that individually undergo rigid transformations to collectively deform the global structure. We realize this locally rigid but globally non-rigid surface registration with a newly derived dual-grid Free-form Deformation (FFD) framework. We first represent the source and target shapes with their signed distance fields (SDF). We then superimpose a sampling grid onto a conventional FFD grid that is dual to the control points. Each control point is then iteratively translated by a rigid transformation that minimizes the difference between two SDFs within the corresponding sampling region. The translated control points then interpolate the embedding space within the FFD grid and determine the overall deformation. The experimental results clearly demonstrate that our method is capable of overcoming the difficulty of preserving and matching local features.
What makes an image memorable?
Authors: Phillip Isola, Jianxiong Xiao, Antonio Torralba 0001, Aude Oliva
Venue: Conference CVPR 2011
Year: 2011

When glancing at a magazine, or browsing the Internet, we are continuously being exposed to photographs. Despite of this overflow of visual information, humans are extremely good at remembering thousands of pictures along with some of their visual details. But not all images are equal in memory. Some stitch to our minds, and other are forgotten. In this paper we focus on the problem of predicting how memorable an image will be. We show that memorability is a stable property of an image that is shared across different viewers. We introduce a database for which we have measured the probability that each picture will be remembered after a single view. We analyze image features and labels that contribute to making an image memorable, and we train a predictor based on global image descriptors. We find that predicting image memorability is a task that can be addressed with current computer vision techniques. Whereas making memorable images is a challenging task in visualization and photography, this work is a first attempt to quantify this useful quality of images.
Centerclip: Token clustering for efficient text-video retrieval
Authors: Shuai Zhao, Linchao Zhu, Xiaohan Wang, Yi Yang
Venue: Conference SIGIR 2022
Year: 2022

Recently, large-scale pre-training methods like CLIP have made great progress in multi-modal research such as text-video retrieval. In CLIP, transformers are vital for modeling complex multi-modal relations. However, in the vision transformer of CLIP, the essential visual tokenization process, which produces discrete visual token sequences, generates many homogeneous tokens due to the redundancy nature of consecutive and similar frames in videos. This significantly increases computation costs and hinders the deployment of video retrieval models in web applications. In this paper, to reduce the number of redundant video tokens, we design a multi-segment token clustering algorithm to find the most representative tokens and drop the non-essential ones. As the frame redundancy occurs mostly in consecutive frames, we divide videos into multiple segments and conduct segment-level clustering. Center tokens from each segment are later concatenated into a new sequence, while their original spatial-temporal relations are well maintained. We instantiate two clustering algorithms to efficiently find deterministic medoids and iteratively partition groups in high dimensional space. Through this token clustering and center selection procedure, we successfully reduce computation costs by removing redundant visual tokens. This method further enhances segment-level semantic alignment between video and text representations, enforcing the spatio-temporal interactions of tokens from within-segment frames. Our method, coined as CenterCLIP, surpasses existing state-of-the-art by a large margin on typical text-video benchmarks, while reducing the training memory cost by 35\% and accelerating the inference speed by 14\% at the best case. The code is available at \href{https://github.com/mzhaoshuai/CenterCLIP}{https://github.com/mzhaoshuai/CenterCLIP}.
The role of permutation invariance in linear mode connectivity of neural networks
Authors: Rahim Entezari, Hanie Sedghi, Olga Saukh, Behnam Neyshabur
Venue: Conference ICLR 2022
Year: 2022

In this paper, we conjecture that if the permutation invariance of neural networks is taken into account, SGD solutions will likely have no barrier in the linear interpolation between them. Although it is a bold conjecture, we show how extensive empirical attempts fall short of refuting it. We further provide a preliminary theoretical result to support our conjecture. Our conjecture has implications for lottery ticket hypothesis, distributed training, and ensemble methods.
Density-aware person detection and tracking in crowds
Authors: Mikel Rodriguez, Ivan Laptev, Josef Sivic, Jean-Yves Audibert
Venue: Conference ICCV 2011
Year: 2011

We address the problem of person detection and tracking in crowded video scenes. While the detection of individual objects has been improved significantly over the recent years, crowd scenes remain particularly challenging for the detection and tracking tasks due to heavy occlusions, high person densities and significant variation in people's appearance. To address these challenges, we propose to leverage information on the global structure of the scene and to resolve all detections jointly. In particular, we explore constraints imposed by the crowd density and formulate person detection as the optimization of a joint energy function combining crowd density estimation and the localization of individual people. We demonstrate how the optimization of such an energy function significantly improves person detection and tracking in crowds. We validate our approach on a challenging video dataset of crowded scenes.
Evaluating a probabilistic model for cross-lingual information retrieval
Authors: Jinxi Xu, Ralph M. Weischedel, Chanh Nguyen
Venue: Conference SIGIR 2001
Year: 2001

This work proposes and evaluates a probabilistic cross-lingual retrieval system. The system uses a generative model to estimate the probability that a document in one language is relevant, given a query in another language. An important component of the model is translation probabilities from terms in documents to terms in a query. Our approach is evaluated when 1) the only resource is a manually generated bilingual word list, 2) the only resource is a parallel corpus, and 3) both resources are combined in a mixture model. The combined resources produce about 90% of monolingual performance in retrieving Chinese documents. For Spanish the system achieves 85% of monolingual performance using only a pseudo-parallel Spanish-English corpus. Retrieval results are comparable with those of the structural query translation technique (Pirkola, 1998) when bilingual lexicons are used for query translation. When parallel texts in addition to conventional lexicons are used, it achieves better retrieval results but requires more computation than the structural query translation technique. It also produces slightly better results than using a machine translation system for CLIR, but the improvement over the MT system is not significant.
CPR: Classifier-Projection Regularization for Continual Learning
Authors: Sungmin Cha, Hsiang Hsu, Taebaek Hwang, Flávio P. Calmon, Taesup Moon
Venue: Conference ICLR 2021
Year: 2021

We propose a general, yet simple patch that can be applied to existing regularization-based continual learning methods called classifier-projection regularization (CPR). Inspired by both recent results on neural networks with wide local minima and information theory, CPR adds an additional regularization term that maximizes the entropy of a classifier's output probability. We demonstrate that this additional term can be interpreted as a projection of the conditional probability given by a classifier's output to the uniform distribution. By applying the Pythagorean theorem for KL divergence, we then prove that this projection may (in theory) improve the performance of continual learning methods. In our extensive experimental results, we apply CPR to several state-of-the-art regularization-based continual learning methods and benchmark performance on popular image recognition datasets. Our results demonstrate that CPR indeed promotes a wide local minima and significantly improves both accuracy and plasticity while simultaneously mitigating the catastrophic forgetting of baseline continual learning methods. The codes and scripts for this work are available at https://github.com/csm9493/CPR_CL.
What makes a chair a chair?
Authors: Helmut Grabner, Juergen Gall, Luc Van Gool
Venue: Conference CVPR 2011
Year: 2011

Many object classes are primarily defined by their functions. However, this fact has been left largely unexploited by visual object categorization or detection systems. We propose a method to learn an affordance detector. It identifies locations in the 3d space which “support” the particular function. Our novel approach “imagines” an actor performing an action typical for the target object class, instead of relying purely on the visual object appearance. So, function is handled as a cue complementary to appearance, rather than being a consideration after appearance-based detection. Experimental results are given for the functional category “sitting”. Such affordance is tested on a 3d representation of the scene, as can be realistically obtained through SfM or depth cameras. In contrast to appearance-based object detectors, affordance detection requires only very few training examples and generalizes very well to other sittable objects like benches or sofas when trained on a few chairs.
Robustness verification for transformers
Authors: Zhouxing Shi, Huan Zhang 0001, Kai-Wei Chang 0001, Minlie Huang, Cho-Jui Hsieh
Venue: Conference ICLR 2020
Year: 2020

Robustness verification that aims to formally certify the prediction behavior of neural networks has become an important tool for understanding model behavior and obtaining safety guarantees. However, previous methods can usually only handle neural networks with relatively simple architectures. In this paper, we consider the robustness verification problem for Transformers. Transformers have complex self-attention layers that pose many challenges for verification, including cross-nonlinearity and cross-position dependency, which have not been discussed in previous works. We resolve these challenges and develop the first robustness verification algorithm for Transformers. The certified robustness bounds computed by our method are significantly tighter than those by naive Interval Bound Propagation. These bounds also shed light on interpreting Transformers as they consistently reflect the importance of different words in sentiment analysis.
Robust local features for improving the generalization of adversarial training
Authors: Chuanbiao Song, Kun He 0001, Jiadong Lin, Liwei Wang 0001, John E. Hopcroft
Venue: Conference ICLR 2020
Year: 2020

Adversarial training has been demonstrated as one of the most effective methods for training robust models to defend against adversarial examples. However, adversarially trained models often lack adversarially robust generalization on unseen testing data. Recent works show that adversarially trained models are more biased towards global structure features. Instead, in this work, we would like to investigate the relationship between the generalization of adversarial training and the robust local features, as the robust local features generalize well for unseen shape variation. To learn the robust local features, we develop a Random Block Shuffle (RBS) transformation to break up the global structure features on normal adversarial examples. We continue to propose a new approach called Robust Local Features for Adversarial Training (RLFAT), which first learns the robust local features by adversarial training on the RBS-transformed adversarial examples, and then transfers the robust local features into the training of normal adversarial examples. To demonstrate the generality of our argument, we implement RLFAT in currently state-of-the-art adversarial training frameworks. Extensive experiments on STL-10, CIFAR-10 and CIFAR-100 show that RLFAT significantly improves both the adversarially robust generalization and the standard generalization of adversarial training. Additionally, we demonstrate that our models capture more local features of the object on the images, aligning better with human perception.
Efficient, robust and accurate fitting of a 3d morphable model
Authors: Sami Romdhani, Thomas Vetter
Venue: Conference ICCV 2003
Year: 2003

3D morphable models, as a means to generate images of a class of objects and to analyze them, have become increasingly popular. The problematic part of this framework is the registration of the model to an image, a.k.a. the fitting. The characteristic features of a fitting algorithm are its efficiency, robustness, accuracy and automation. Many accurate algorithms based on gradient descent techniques exist which are unfortunately short on the other features. Recently, an efficient algorithm called inverse compositional image alignment (ICIA) algorithm, able to fit 2D images, was introduced. We extent this algorithm to fit 3D morphable models using a novel mathematical notation which facilitates the formulation of the fitting problem. This formulation enables us to avoid a simplification so far used in the ICIA, being as efficient and leading to improved fitting precision. Additionally, the algorithm is robust without sacrificing its efficiency and accuracy, thereby conforming to three of the four characteristics of a good fitting algorithm.
Understanding egocentric activities
Authors: Alireza Fathi, Ali Farhadi, James M. Rehg
Venue: Conference ICCV 2011
Year: 2011

We present a method to analyze daily activities, such as meal preparation, using video from an egocentric camera. Our method performs inference about activities, actions, hands, and objects. Daily activities are a challenging domain for activity recognition which are well-suited to an egocentric approach. In contrast to previous activity recognition methods, our approach does not require pre-trained detectors for objects and hands. Instead we demonstrate the ability to learn a hierarchical model of an activity by exploiting the consistent appearance of objects, hands, and actions that results from the egocentric context. We show that joint modeling of activities, actions, and objects leads to superior performance in comparison to the case where they are considered independently. We introduce a novel representation of actions based on object-hand interactions and experimentally demonstrate the superior performance of our representation in comparison to standard activity representations such as bag of words.
Detecting activities of daily living in first-person camera views
Authors: Hamed Pirsiavash, Deva Ramanan
Venue: Conference CVPR 2012
Year: 2012

We present a novel dataset and novel algorithms for the problem of detecting activities of daily living (ADL) in firstperson camera views. We have collected a dataset of 1 million frames of dozens of people performing unscripted, everyday activities. The dataset is annotated with activities, object tracks, hand positions, and interaction events. ADLs differ from typical actions in that they can involve long-scale temporal structure (making tea can take a few minutes) and complex object interactions (a fridge looks different when its door is open). We develop novel representations including (1) temporal pyramids, which generalize the well-known spatial pyramid to approximate temporal correspondence when scoring a model and (2) composite object models that exploit the fact that objects look different when being interacted with. We perform an extensive empirical evaluation and demonstrate that our novel representations produce a two-fold improvement over traditional approaches. Our analysis suggests that real-world ADL recognition is “all about the objects,” and in particular, “all about the objects being interacted with.”