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Ofir Shifman, Yair Weiss

Deep neural networks that achieve remarkable performance in image classification have previously been shown to be easily fooled by tiny transformations such as a one pixel translation of the input image. In order to address this problem, two approaches have been proposed in recent years. The first approach suggests using huge datasets together with data augmentation in the hope that a highly varied training set will teach the network to learn to be invariant. The second approach suggests using architectural modifications based on sampling theory to deal explicitly with image translations. In this paper, we show that these approaches still fall short in robustly handling 'natural' image translations that simulate a subtle change in camera orientation. Our findings reveal that a mere one-pixel translation can result in a significant change in the predicted image representation for approximately 40\% of the test images in state-of-the-art models (e.g. open-CLIP trained on LAION-2B or DINO-v2) , while models that are explicitly constructed to be robust to cyclic translations can still be fooled with 1 pixel realistic (non-cyclic) translations 11\% of the time. We present \textbf{R}obust \textbf{I}nference by \textbf{C}rop \textbf{S}election: a simple method that can be proven to achieve any desired level of consistency, although with a modest tradeoff with the model's accuracy. Importantly, we demonstrate how employing this method reduces the ability to fool state-of-the-art models with a 1 pixel translation to less than 5\% while suffering from only a 1\% drop in classification accuracy. Additionally, we show that our method can be easy adjusted to deal with circular shifts as well. In such case we achieve 100\% robustness to integer shifts with \textit{state-of-the-art} accuracy, and with no need for any further training. \keywords{Robustness \and Translation Invariance \and Neural Networks}

Declan McIntosh, Alexandra Branzan Albu

We propose Online-InReaCh, the first fully unsupervised online method for detecting and localizing anomalies on-the-fly in image sequences while following non-stationary distributions. Previous anomaly detection methods are limited to supervised one-class classification or are unsupervised but still pre-compute their nominal model. Online-InReaCh can operate online by dynamically maintaining a nominal model of commonly occurring patches that associate well across image realizations of the underlying nominal distribution while removing stale previously nominal patches. Online-InReaCh, while competitive in previous offline benchmarks, also achieves 0.936 and 0.961 image- and pixel-wise AUROC when tested online on MVTecAD, where 23.8% of all randomly sampled images contain anomalies. Online-InReaCh's performance did not correlate with anomaly proportion even to 33.5%. We also show that Online-InReaCh can integrate new nominal structures and distinguish anomalies after a single frame, even in the worst-case distribution shift from one training class to a new previously unseen testing class.

Kihwan Yoon, Yong Han Kim, Sungjei Kim, Jinwoo Jeong

Within the video, different regions have varying motion complexity, with simple regions containing static or global motion and complex regions containing fast motion or lots of local motion. In recent years, the performance of flow-based Video Frame Interpolation (VFI) algorithms has improved significantly. However, existing training methods train on randomly cropped regions of train data without considering the complexity of the motion. As a result, they cannot handle all regions of the frame that contain varying motion complexity. To solve this problem, we propose a novel VFI approach (IAM-VFI) that can interpolate any motion by considering the motion complexity of all regions in the frame. First, we propose a training data classification method for motion optimization based on each motion complexity. Then, using the proposed data, a flow estimation network generates optimized results for each complexity. Finally, we propose a Motion Complexity Estimation Network (MCENet) to generate a Motion Complexity Map (MCM) that can estimate the motion complexity of each region. Our proposed methods can be easily applied to most flow-based VFI algorithms. Experimental results show that the proposed method can interpolate any motion and significantly improve the performance of existing VFI algorithms.

Jing Wu, Mehrtash Harandi

Machine unlearning has become a pivotal task to erase the influence of data from a trained model. It adheres to recent data regulation standards and enhances the privacy and security of machine learning applications. In this work, we present a new machine unlearning approach Scissorhands. Initially, Scissorhands identifies the most pertinent parameters in the given model relative to the forgetting data via connection sensitivity. By reinitializing the most influential top-k percent of these parameters, a trimmed model for erasing the influence of the forgetting data is obtained. Subsequently, Scissorhands fine-tunes the trimmed model with a gradient projection-based approach, seeking parameters that preserve information on the remaining data while discarding information related to the forgetting data. Our experimental results, conducted across image classification and image generation tasks, demonstrate that Scissorhands, showcases competitive performance when compared to existing methods. Source code is available at https://github.com/AnonymousUser-hi/Scissorhands.

Feng Wang, Jieru Mei, Alan Yuille

Recent advances in contrastive language-image pretraining (CLIP) have demonstrated strong capabilities in zero-shot classification by aligning visual and textual features at an image level. However, in dense prediction tasks, CLIP often struggles to localize visual features within an image and fails to attain favorable pixel-level segmentation results. In this work, we investigate in CLIP's spatial reasoning mechanism and identify that its failure of dense prediction is caused by a location misalignment issue in the self-attention process. Based on this observation, we propose a training-free adaptation approach for CLIP's semantic segmentation, which only introduces a very simple modification to CLIP but can effectively address the issue of location misalignment. Specifically, we reform the self-attention mechanism with leveraging query-to-query and key-to-key similarity to determine attention scores. Remarkably, this minimal modification to CLIP significantly enhances its capability in dense prediction, improving the original CLIP's 14.1% average zero-shot mIoU over eight semantic segmentation benchmarks to 38.2%, and outperforming the existing SoTA's 33.9% by a large margin.

Jinho Jeong, Jinwoo Kim, Younghyun Jo, Seon Joo Kim

Single Image Super-Resolution (SISR) plays a vital role in various applications, driven by advancements in Deep Neural Networks (DNNs). However, increasing model complexity raises computational costs, necessitating efficient solutions. Existing patch-based approaches aiming at efficient SR encounter challenges in adapting to varying pixel difficulties and suffer from decreased efficiency with larger patches. To address these limitations, we propose Pixel-level Classifier for Single Image Super-Resolution (PCSR), a novel pixel-level distribution method for efficient SISR. Our approach optimizes computational resource allocation at the pixel level, achieving better efficiency compared to patch-based methods, and also provides user tunability without re-training and mitigates artifacts through post-processing techniques. Experimental results demonstrate the effectiveness of PCSR across diverse SISR models and benchmarks, surpassing existing approaches in terms of the PSNR-FLOPs trade-off.

Fengyuan Liu, Haochen Luo, Yiming Li, Philip Torr, Jindong Gu

Recent progress in visual generative models enables the generation of high-quality images. To prevent the misuse of generated images, it is important to identify the origin model that generates them. In this work, we study the origin attribution of generated images in a practical setting where only a few images generated by a source model are available and the source model cannot be accessed. The goal is to check if a given image is generated by the source model. We first formulate this problem as a few-shot one-class classification task. To solve the task, we propose OCC-CLIP, a CLIP-based framework for few-shot one-class classification, enabling the identification of an image's source model, even among multiple candidates. Extensive experiments corresponding to various generative models verify the effectiveness of our OCC-CLIP framework. Furthermore, an experiment based on the recently released DALL·E-3 API verifies the real-world applicability of our solution.

Fangqin Zhou, Mert Kilickaya, Joaquin Vanschoren, Ran Piao

Hyperspectral Imaging (HSI) plays an increasingly critical role in precise vision tasks within remote sensing, capturing a wide spectrum of visual data. Transformer architectures have significantly enhanced HSI task performance, while advancements in Transformer Architecture Search (TAS) have improved model discovery. To harness these advancements for HSI classification, we make the following contributions: i) We propose HyTAS, the first benchmark on transformer architecture search for Hyperspectral imaging, ii) We comprehensively evaluate 12 different methods to identify the optimal transformer over 5 different datasets, iii) We perform an extensive factor analysis on the Hyperspectral transformer search performance, greatly motivating future research in this direction. All benchmark materials included in our supplementary will be publicly available upon publication.

Xingyi Yang, Xinchao Wang

This paper introduces a novel paradigm termed \textbf{Neural Metamorphosis}~(\textbf{NeuMeta}), which aims to represent a continuous family of networks within a single versatile model. Unlike traditional methods that rely on separate models for different network tasks or sizes, NeuMeta enables an expansive continuum of neural networks that readily morph to fit various needs. The core mechanism is to train a neural implicit function that takes the desired network size and parameter coordinates as inputs, and generates exact corresponding weight values without requiring separate models for different configurations. Specifically, to achieve weight smoothness in a single model, we address the Shortest Hamiltonian Path problem within each neural clique graph. We maintain cross-model consistency by incorporating input noise during training. As such, NeuMeta may dynamically create arbitrary network parameters during the inference stage by sampling on the weight manifold. NeuMeta shows promising results in synthesizing parameters for unseen network configurations. Our extensive tests in image classification, semantic segmentation, and image generation reveal that NeuMeta sustains full-size performance even at a 75% compression rate.

Zhengfeng Lai, Haotian Zhang, Bowen Zhang, Wentao Wu, Haoping Bai, Aleksei Timofeev, Xianzhi Du, Zhe Gan, Jiulong Shan, Chen-Nee Chuah 等

Large-scale web-crawled datasets are fundamental for the success of pre-training vision-language models, such as CLIP. However, the inherent noise and potential irrelevance of web-crawled AltTexts pose challenges in achieving precise image-text alignment. Existing methods utilizing large language models (LLMs) for caption rewriting have shown promise on small, curated datasets like CC3M and CC12M. This study introduces a scalable pipeline for noisy caption rewriting. Unlike recent LLM rewriting techniques, we emphasize the incorporation of visual concepts into captions, termed as Visual-enriched Captions (VeCap). To ensure data diversity, we propose a novel mixed training scheme that optimizes the utilization of AltTexts alongside newly generated VeCap. We showcase the adaptation of this method for training CLIP on large-scale web-crawled datasets, termed VeCLIP. Employing this cost-effective pipeline, we effortlessly scale our dataset up to 300 million samples named VeCap dataset. Our results show significant advantages in image-text alignment and overall model performance. For example, VeCLIP achieves up to +25.2% gain in COCO and Flickr30k retrieval tasks under the 12M setting. For data efficiency, VeCLIP achieves +3% gain while only using 14% of the data employed in the vanilla CLIP and 11% in ALIGN. We also note the VeCap data is complementary with other well curated datasets good for zero-shot classification tasks. When combining VeCap and DFN, our model can achieve strong performance on both of image-text retrieval and zero-shot classification tasks, e.g. 83.1% accuracy@1 on ImageNet zero-shot for a H/14 model.

Lixiong Qin, Mei Wang, Xuannan Liu, Yuhang Zhang, Wei Deng, Xiaoshuai Song, Weiran Xu, Weihong Deng

With the comprehensive research conducted on various face analysis tasks, there is a growing interest among researchers to develop a unified approach to face perception. Existing methods mainly discuss unified representation and training, which lack task extensibility and application efficiency. To tackle this issue, we focus on the unified model structure, exploring a face generalist model. As an intuitive design, Naive Faceptor enables tasks with the same output shape and granularity to share the structural design of the standardized output head, achieving improved task extensibility. Furthermore, Faceptor is proposed to adopt a well-designed single-encoder dual-decoder architecture, allowing task-specific queries to represent new-coming semantics. This design enhances the unification of model structure while improving application efficiency in terms of storage overhead. Additionally, we introduce Layer-Attention into Faceptor, enabling the model to adaptively select features from optimal layers to perform the desired tasks. Through joint training on 13 face perception datasets, Faceptor achieves exceptional performance in facial landmark localization, face parsing, age estimation, expression recognition, binary attribute classification, and face recognition, achieving or surpassing specialized methods in most tasks. Our training framework can also be applied to auxiliary supervised learning, significantly improving performance in data-sparse tasks such as age estimation and expression recognition. The code and model will be made publicly available.

Rui Zhao, Huibin Yan, Shuoyao Wang

Due to data collection challenges, the mean-teacher learning paradigm has emerged as a critical approach for cross-domain object detection, especially in adverse weather conditions. Despite making significant progress, existing methods are still plagued by low-quality pseudo-labels in the degraded images. In this paper, we propose a generation-composition paradigm training framework that includes the tiny-object-friendly loss, i.e., IAoU loss with a joint-filtering and student-aware strategy to improve pseudo-labels generation quality and refine the filtering scheme. Specifically, in the generation phase of pseudo-labels, we observe that bounding box regression is essential for feature alignment and develop the IAoU loss to improve the accuracy of bounding box regression, further facilitating subsequent feature alignment. We also find that selecting bounding boxes based solely on classification confidence does not perform well in cross-domain noisy image scenes. Moreover, relying exclusively on predictions from the teacher model could cause the student model to collapse. Accordingly, in the composition phase, we introduce the mean-teacher model with a joint-filtering and student-aware strategy combining classification and regression thresholds from both the student and the teacher models. Our extensive experiments, conducted on synthetic and real-world adverse weather datasets, clearly demonstrate that the proposed method surpasses state-of-the-art benchmarks across all scenarios, particularly achieving a 12.4% improvement of mAP i.e., cityscapes to RTTS.

Haoqi Wang, Tong Zhang, Mathieu Salzmann

Vision Transformer models trained on large-scale datasets, although effective, often exhibit artifacts in the patch token they extract. While such defects can be alleviated by re-training the entire model with additional classification tokens, the underlying reasons for the presence of these tokens remain unclear. In this paper, we conduct a thorough investigation of this phenomenon, combining theoretical analysis with empirical observations. Our findings reveal that these artifacts originate from the pre-trained network itself, specifically stemming from the leading left singular vector of the network's weights. Furthermore, to mitigate these defects, we propose a novel fine-tuning smooth regularization that rectifies structural deficiencies using only a small dataset, thereby avoiding the need for complete re-training. We validate our method on various downstream tasks, including unsupervised segmentation, classification, and supervised segmentation, demonstrating its effectiveness in improving model performance. Our code and checkpoints will be released.

Yunan LI, Yihao Zhang, Shoude Li, Long Tian, DOU QUAN, Chaoneng Li, Qiguang Miao

Low illumination significantly impacts the performance of learning-based models trained in well-lit conditions. Although current methods alleviate this issue through either image-level enhancement or feature-level adaptation, they often focus solely on the image itself, ignoring how the task-relevant target varies along with different illumination. In this paper, we propose a target-aware representation learning framework designed to improve high-level task performance in low-illumination environments. We first achieve a bi-directional domain alignment from both image appearance and semantic features to bridge data under varying illumination conditions. To better focus on the target itself, we design a target highlighting strategy, incorporated with the saliency mechanism and Temporal Gaussian Mixture Model to emphasize the location and movement of task-relevant targets. We also design a mask token-based representation learning scheme to learn a more robust target-aware feature. Our framework ensures more robust and effective feature representation for high-level vision tasks in low illumination. Extensive experiments conducted on CODaN, ExDark, and ARID datasets validate the effectiveness of our method for both image and video-based tasks, such as classification, detection, and action recognition. The code will be released upon acceptance.

Dohyung Kim, Junghyup Lee, Jeimin Jeon, JAEHYEON MOON, BUMSUB HAM

Network quantization generally converts full-precision weights and/or activations into low-bit fixed-point values in order to accelerate an inference process. Recent approaches to network quantization further discretize the gradients into low-bit fixed-point values, enabling an efficient training. They typically set a quantization interval using a min-max range of the gradients or adjust the interval such that the quantization error for entire gradients is minimized. In this paper, we analyze the quantization error of gradients for the low-bit fixed-point training, and show that lowering the error for large-magnitude gradients boosts the quantization performance significantly. Based on this, we derive an upper bound of quantization error for the large gradients in terms of the quantization interval, and obtain an optimal condition for the interval minimizing the quantization error for large gradients. We also introduce an interval update algorithm that adjusts the quantization interval adaptively to maintain a small quantization error for large gradients. Experimental results demonstrate the effectiveness of our quantization method for various combinations of network architectures and bit-widths on various tasks, including image classification, object detection, and super-resolution. For comparison, our code will be made publicly available at the time of publication.

Zhen Zhu, Yiming Gong, Derek Hoiem

We propose an approach for anytime continual learning (AnytimeCL) for open vocabulary image classification. The AnytimeCL problem aims to break away from batch training and rigid models by requiring that a system can predict any set of labels at any time and efficiently update and improve when receiving one or more training samples at any time. Despite the challenging goal, we achieve substantial improvements over recent methods. We propose a dynamic weighting between predictions of a partially fine-tuned model and a fixed open vocabulary model that enables continual improvement when training samples are available for a subset of a task's labels. We also propose an attention-weighted PCA compression for compression of training features that reduces storage and computation with little impact to model accuracy. Our methods are validated with experiments that test flexibility of learning and inference.

Paul Albert, Kevin McGuinness, Eric Arazo, Tarun Krishna, Noel O Connor, Jack Valmadre

Training a classifier on web-crawled data demands learning algorithms that are robust to annotation errors and irrelevant examples. This paper builds upon the recent empirical observation that applying unsupervised contrastive learning to noisy, web-crawled datasets yields a feature representation under which the in-distribution (ID) and out-of-distribution (OOD) samples are linearly separable. We show that direct estimation of the separating hyperplane can indeed offer an accurate detection of OOD samples, and yet, surprisingly, this detection does not translate into gains in classification accuracy. Digging deeper into this phenomenon, we discover that the near-perfect detection misses a type of clean examples that are valuable for supervised learning. These examples often represent visually simple images, which are relatively easy to identify as clean examples using standard loss- or distance-based methods despite being poorly separated from the OOD distribution using unsupervised learning. This urges us to propose a hybrid solution that alternates between noise detection using linear separation and a state-of-the-art (SOTA) small-loss approach. When combined with the SOTA algorithm PLS, we substantially improve SOTA results for real-world image classification in the presence of web noise. The code for reproducing the experiments in this paper will be publicly released upon acceptance.

Yu Bai, Bo Zhang, Zheng Zhang, Shuo Yan, Zibo Ma, Wu Liu, Xiuzhuang Zhou, Xiangyang Gong, Wendong Wang

In recent years, the Whole Slide Image (WSI) classification task has achieved great advancement due to the success of Multiple Instance Learning (MIL). However, MIL-based methods face two limitations: 1) often select the top-ranking instances of a WSI based on different metrics (e.g., attention score) to train the model due to the large resolution of WSIs, which may lead to missing global information; 2) usually consider all instances within a bag to be unordered, which will cause the local context information to be missing. To address the limitations of MIL-based methods, we formulate the WSI classification task as a long sequence classification problem in a weakly supervised setting. We propose a Noise Robust Memory-augmented (Norma) framework that serializes the WSI into an ordered sequence and caches each segment for future reuse in a sequential manner. By applying such paradiam, global and local context information of a WSI can be obtained during training. Furthermore, Normal adopts a Cyclic Training process to eliminate the noise introduced by the WSI-level labe. We obtains state-of-the-art results on CAMELYON-16, TCGA-BRAC and TCGA-LUNG datasets. We will release the code upon acceptance.

A. Tuan Nguyen, Kai Sheng Tai, Bor-Chun Chen, Satya Narayan Shukla, Hanchao Yu, Philip Torr, Taipeng Tian, Ser-Nam Lim

This paper addresses a significant limitation that prevents Contrastive Language-Image Pretrained Models (CLIP) from achieving optimal performance on downstream image classification tasks. The key problem with CLIP-style zero-shot classification is that it requires domain-specific context in the form of prompts to better align the class descriptions to the downstream data distribution. In particular, prompts for vision-language models are domain-level texts (e.g., ``a centered satellite image of ...'') which, together with the class names, are fed into the text encoder to provide more context for the downstream dataset. These prompts are typically manually tuned, which is time consuming and often sub-optimal. To overcome this bottleneck, this paper proposes uCAP, a method to automatically learn domain-specific prompts/contexts using only unlabeled in-domain images. We achieve this by modeling the generation of images given the class names and a domain-specific prompt with an unsupervised likelihood distribution, and then performing inference of the prompts. We validate the proposed method across various models and datasets, showing that uCAP consistently outperforms manually tuned prompts and related baselines on the evaluated datasets: ImageNet, CIFAR-10, CIFAR-100, OxfordPets (up to 2\%), SUN397 (up to 5\%), and Caltech101 (up to 3\%).

Zhewei Wu, Ruilong Yu, Qihe Liu, Shuying Cheng, Shilin Qiu, Shijie Zhou

Adversarial attacks in visual object tracking have significantly degraded the performance of advanced trackers by introducing imperceptible perturbations into images. However, there is still a lack of research on designing adversarial defense methods for object tracking. To address these issues, we propose an effective auxiliary pre-processing defense network, AADN, which performs defensive transformations on the input images before feeding them into the tracker. Moreover, it can be seamlessly integrated with other visual trackers as a plug-and-play module without parameter adjustments. We train AADN using adversarial training, specifically employing Dua-Loss to generate adversarial samples that simultaneously attack the classification and regression branches of the tracker. Extensive experiments conducted on the OTB100, LaSOT, and VOT2018 benchmarks demonstrate that AADN maintains excellent defense robustness against adversarial attack methods in both adaptive and non-adaptive attack scenarios. Moreover, when transferring the defense network to heterogeneous trackers, it exhibits reliable transferability. Finally, AADN achieves a processing time of up to 5ms/frame, allowing seamless integration with existing high-speed trackers without introducing significant computational overhead. We will make our code publicly available soon.