Large-scale 3D bounding box annotation is crucial for LiDAR object detection but comes at a high cost. Semi-supervised object detection (SSOD) offers promising solutions to leverage unannotated data, but the predominant pseudo-labeling approach requires careful hyperparameter tuning for training on noisy teacher labels. In this work, we propose a Cross-Scan Object Transfer (CSOT) paradigm for LiDAR SSOD. Central to our approach is Hotspot Network, a transformer-based network that predicts possible placement locations for annotated objects in unannotated scans and assigns scores to each location. By leveraging these contextual consistent location predictions, CSOT successfully enables object copy-paste in LiDAR SSOD for the first time. To train object detectors on partially annotated scans generated by CSOT, we adopt a Spatial-Aware classification loss throughout our partial supervision to handle false negative issues caused by treating all unlabeled objects as background. We conduct extensive experiments to verify the efficacy and generality of our method. Compared to other state-of-the-art label-efficient methods used in LiDAR detection, our approach requires the least amount of annotation while achieves the best detectior. Using only 1% of the labeled data on the Waymo dataset, our semi-supervised detector achieves performance on par with the fully supervised baseline. Similarly, on the nuScenes dataset, our semi-supervised CenterPoint reaches 99% of the fully supervised model's detection performance in terms of NDS score, while using just 5% of the labeled data.
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With the ability to generate high-quality images, text-to-image (T2I) models can be exploited for creating inappropriate content. To prevent misuse, existing safety measures are either based on text blacklists, easily circumvented, or harmful content classification, using large datasets for training and offering low flexibility. Here, we propose Latent Guard, a framework designed to improve safety measures in text-to-image generation. Inspired by blacklist-based approaches, Latent Guard learns a latent space on top of the T2I model's text encoder, where we check the presence of harmful concepts in the input text embeddings. Our framework is composed of a data generation pipeline specific to the task using large language models, ad-hoc architectural components, and a contrastive learning strategy to benefit from the generated data. Our method is evaluated on three datasets and against four baselines.
Handwritten text generation (HTG) conditioned on content and style is a challenging task due to the variability of inter-user characteristics and the unlimited combinations of characters that form new words unseen during training. Diffusion Models have recently shown promising results in HTG, however they are still under-explored. We present DiffusionPen (DiffPen), a 5-shot style handwritten text generation approach based on Latent Diffusion Models. By utilizing a hybrid style extractor that combines the power of metric learning and classification, our approach manages to capture both textual and stylistic characteristics of seen and unseen words and styles and generate realistic handwritten samples. We perform experiments on IAM offline handwriting database to evaluate the generated style and content and compare them with other SotA methods. Our method outperforms other methods qualitatively and quantitatively and additional data from our method can improve the performance of a Handwriting Text Recognition (HTR) system. The code is available at: (code repository will be released in case of acceptance to maintain anonymity).
Face Forgery videos have elicited critical social public concerns and various detectors have been proposed. However, fully-supervised detectors may lead to easily overfitting to specific forgery methods or videos, and existing self-supervised detectors are strict on auxiliary tasks, such as requiring audio or multi-modalities, leading to limited generalization and robustness. In this paper, we examine whether we can solve this issue by leveraging visual-only real face videos. To this end, we propose to learn the Natural Consistency representation (NACO) of real face videos in a self-supervised manner, which is inspired by the observation that fake videos struggle to maintain the natural spatiotemporal consistency even under unknown forgery methods and different perturbations. Our NACO first extracts spatial features of each frame by CNNs then integrates them into Transformer to learn the long-range spatiotemporal representation, leveraging the advantages of CNNs and Transformer on local spatial receptive field and long-term memory respectively. Furthermore, a Spatial Predictive Module (SPM) and a Temporal Contrastive Module (TCM) are introduced to enhance the natural consistency representation learning. The SPM aims to predict random masked spatial features from spatiotemporal representation, and the TCM regularizes the latent distance of spatiotemporal representation by shuffling the natural order to disturb the consistency, which could both force our NACO more sensitive to the natural spatiotemporal consistency. After the representation learning stage, a MLP head is fine-tuned to perform the usual forgery video classification task. Extensive experiments show that our method outperforms other state-of-the-art competitors with impressive generalization and robustness.
Vision Transformer (ViT) has become one of the most prevailing fundamental backbone networks for various computer vision tasks, which however requires a high computational cost and inference latency. Recently, post-training quantization (PTQ) has emerged as a promising way to promote the efficiency of Vision Transformers. Nevertheless, existing PTQ approaches for ViTs suffer from the inflexible quantization on the post-Softmax and post-GeLU activations with power-law-like distributions. To address the issue, we propose a novel non-uniform quantizer dubbed AdaLog, which adapts the logarithmic base to accommodate the power-law-like distribution of activations, and simultaneously allows for hardware-friendly quantization and de-quantization. By further employing the bias reparameterization, AdaLog Quantizer is applicable to both the post-Softmax and post-GeLU activations. Moreover, we develop a Beam-Search strategy to select the best logarithm base for AdaLog quantizers, as well as those scaling factors and zero points for uniform quantizers. Extensive experimental results on public benchmarks demonstrate the effectiveness of our approach for various ViT-based architectures and vision tasks such as classification, object detection, and instance segmentation.
In recent years, there have been attempts to increase the kernel size of Convolutional Neural Nets (CNNs) to mimic the global receptive field of Vision Transformers' (ViTs) self-attention blocks. That approach, however, quickly hit an upper bound and saturated way before achieving a global receptive field. In this work, we demonstrate that by leveraging the Wavelet Transform (WT), it is, in fact, possible to obtain very large receptive fields without suffering from over-parameterization, e.g., for a k times k receptive field, the number of trainable parameters in the proposed method grows only logarithmically with k. The proposed layer, named WTConv, can be used as a drop-in replacement in existing architectures, results in an effective multi-frequency response, and scales gracefully with the size of the receptive field. We demonstrate the effectiveness of the WTConv layer within ConvNeXt and MobileNetV2 architectures for image classification, as well as backbones for downstream tasks, and show it yields additional properties such as robustness to image corruption and increased response to shapes over textures. Our code will be released upon acceptance.
Learning visual representation with image-text datasets attracts a lot of attention in recent years. Existing approaches primarily rely on cross-modality supervision, and incorporate intra-modality supervision if necessary. They overlook the potential benefits of modality-fused supervision. Since modality-fused representation augments the image representation with textual information, we conjecture it is more discriminative and potential to be a strong teacher for visual representation learning. In this paper, we validate this hypothesis by experiments and propose a novel method FuseTeacher that learns visual representation by modality-fused supervision. Specifically, we introduce a fusion encoder that encodes image and text into a fusion representation. This representation can be utilized to supervise the visual representation learning in two distillation ways: (i) Classification Distillation: we cluster image-text pairs into K clusters using the fusion representation and assign each pair a soft cluster assignment, which is served as a pseudo classification label for supervising the image encoder. (ii) Retrieval Distillation: we calculate the similarities between the fusion representation and all text representations in the same batch. By using the similarity distribution as pseudo retrieval similarity between the corresponding image and all texts, we can prevent one-to-one contrastive learning from separating relevant but unpaired pairs. The FuseTeacher is compatible with existing language supervised visual representation learning methods. Experimental results demonstrate that it is able to bring significant improvements and achieves state-of-the-art methods on various datasets. Our code, datasets and pre-trained models will be released.
Contrastive Language-Image Pre-training (CLIP) has been the cornerstone for zero-shot classification, text-image retrieval, and text-image generation by aligning image and text modalities. Despite its widespread adoption, a significant limitation of CLIP lies in the inadequate length of text input. The length of the text token is restricted to 77, and an empirical study shows the actual effective length is even less than 20. This prevents CLIP from handling detailed descriptions, limiting its applications for image retrieval and text-to-image generation with extensive prerequisites. To this end, we propose Long-CLIP as a plug-and-play alternative to CLIP that supports long-text input, retains or even surpasses its zero-shot generalizability, and aligns the CLIP latent space, making it readily replace CLIP without any further adaptation in downstream frameworks. Nevertheless, achieving this goal is far from straightforward, as simplistic fine-tuning can result in a significant degradation of CLIP's performance. Moreover, substituting the text encoder with a language model supporting longer contexts necessitates pretraining with vast amounts of data, incurring significant expenses. Accordingly, Long-CLIP introduces an efficient finetuning solution on CLIP with two novel strategies designed to maintain the original capabilities, including (1) a knowledge-preserved stretching of positional embedding and (2) a primary component matching of CLIP features. With leveraging just one million extra long text-image pairs, Long-CLIP has shown the superiority to CLIP for about 20% in long caption text-image retrieval and 6% in traditional text-image retrieval tasks, eg, COCO and Flickr30k. Furthermore, Long-CLIP offers enhanced capabilities for generating images from detailed text descriptions by replacing CLIP in a plug-and-play manner. All code and models will be publicly available.
A serious issue that harms the performance of zero-shot visual recognition is named objective misalignment, i.e., the learning objective prioritizes improving the recognition accuracy of seen classes rather than unseen classes, while the latter is the true target to pursue. This issue becomes more significant in zero-shot image segmentation because the stronger (i.e., pixel-level) supervision brings a larger gap between seen and unseen classes. To mitigate it, we propose a novel architecture named AlignZeg, which embodies a comprehensive improvement of the segmentation pipeline, including proposal extraction, classification, and correction, to better fit the goal of zero-shot segmentation. (1) Mutually-Refined Proposal Extraction. AlignZeg harnesses a mutual interaction between mask queries and visual features, facilitating detailed class-agnostic mask proposal extraction. (2) Generalization-Enhanced Proposal Classification. AlignZeg introduces synthetic data and incorporates multiple background prototypes to allocate a more generalizable feature space. (3) Predictive Bias Correction. During the inference stage, AlignZeg uses a class indicator to find potential unseen class proposals followed by a prediction postprocess to correct the prediction bias. Experiments demonstrate that AlignZeg markedly enhances zero-shot semantic segmentation, as shown by an average 3.5% increase in hIoU, largely attributed to a 7.1% improvement in identifying unseen classes, and we further validate that the improvement comes from alleviating the objective misalignment issue.
Video class-incremental learning (VCIL) aims to learn discriminative and generalized feature representations for video frames to mitigate catastrophic forgetting. Conventional VCIL approaches often retain a subset of frames or features from prior tasks as exemplars for subsequent incremental learning stages. However, these strategies overlook the connection between base and novel classes, sometimes even leading to privacy leakage. To overcome this challenge, we introduce a Spatial-Temporal Subspace Projection (STSP) scheme for VCIL. Specifically, we propose a discriminative Temporal-based Subspace Classifier (TSC) that represents each class with an orthogonal subspace basis and adopts subspace projection loss for classification. Differing from typical classification methods that rely on fully connected layers, our TSC is designed to discern the spatial-temporal dynamics in video content, thereby enhancing the representation of each video sample. Additionally, we implement inter- and intra-class orthogonal constraints into TSC, ensuring that each class occupies a unique orthogonal subspace, defined by its basis. To prevent catastrophic forgetting, we further employ a Spatial-based Gradient Projection (SGP) strategy. SGP adjusts the gradients of the network parameters to align with the approximate null space of the spatial feature set from previous tasks. Extensive experiments conducted on three benchmarks, namely HMDB51, UCF101, and Something-Something V2, demonstrate that our STSP method outperforms state-of-the-art comparison methods,
The First to Know: How Token Distributions Reveal Hidden Knowledge in Large Vision-Language Models?
PDF ↗Large vision-language models (LVLMs), designed to interpret and respond to human instructions, occasionally generate hallucinated or harmful content due to inappropriate instructions. This study uses linear probing to shed light on the hidden knowledge at the output layer of LVLMs. We demonstrate that the logit distributions of the first tokens contain sufficient information to determine whether to respond to the instructions, including recognizing unanswerable visual questions, defending against multi-modal jailbreaking attack, and identifying deceptive questions. Such hidden knowledge is gradually lost in logits of subsequent tokens during response generation. Then, we illustrate a simple decoding strategy at the generation of the first token, effectively improving the generated content. In experiments, we find a few interesting insights: First, the CLIP model already contains a strong signal for solving these tasks, indicating potential bias in the existing datasets. Second, we observe performance improvement by utilizing the first logit distributions on three additional tasks, including indicting uncertainty in math solving, mitigating hallucination, and image classification. Last, with the same training data, simply finetuning LVLMs improve models' performance but is still inferior to linear probing on these tasks.
Traditional single-task image restoration methods excel in handling specific degradation types but struggle with multiple degradations. To address this limitation, we propose Grouped Restoration with Image Degradation Similarity (GRIDS), a novel approach that harmonizes the competing objectives inherent in multiple-degradation restoration. We first introduce a quantitative method for assessing relationships between image degradations using statistical modeling of deep degradation representations. This analysis facilitates the strategic grouping of similar tasks, enhancing both the efficiency and effectiveness of the restoration process. Based on the degradation similarity, GRIDS divides restoration tasks into optimal groups, where tasks within the same group are highly correlated. For instance, GRIDS effectively groups 11 degradation types into 4 cohesive groups. Trained models within each group show significant improvements, with an average improvement of 0.09dB over single-task upper bound models and 2.24dB over the mix-training baseline model. GRIDS incorporates an adaptive model selection mechanism for inference, automatically selecting the appropriate grouped-training model based on the input degradation. This mechanism is particularly useful for real-world scenarios with unknown degradations as it does not rely on explicit degradation classification modules. Furthermore, our method can predict model generalization ability without the need for network inference, providing valuable insights for practitioners.
Vision Transformers (ViT) have emerged as the de-facto choice for numerous industry grade vision solutions. But their inference cost can be prohibitive for many settings, as they compute self-attention in each layer which suffers from quadratic computational complexity in the number of tokens. On the other hand, spatial information in images and spatio-temporal information in videos is usually sparse. In this work, we introduce LookupViT, that aims to exploit this information sparsity to reduce the cost of ViT inference. LookupViT provides a novel general purpose vision transformer block that operates by compressing information from higher resolution tokens to a fixed number of tokens. These few compressed tokens undergo meticulous processing, while the higher-resolution tokens are passed through computationally cheaper layers. Information sharing between these two token sets is enabled through a bidirectional cross-attention mechanism. The approach offers multiple advantages - (a) easy to implement on standard ML accelerators (GPUs/TPUs) via standard high-level operators, (b) applicable to standard ViT and its variants, thus generalizes to various tasks, (c) can handle different tokenization and attention approaches. LookupViT also offers flexibility for the compressed tokens, enabling performance-computation trade-offs in a single trained model. LookupViT's effectiveness on multiple domains - (a) for image-classification (ImageNet-1K and ImageNet-21K), (b) video classification (Kinetics400 and Something-Something V2), (c) image captioning (COCO-Captions) with a frozen encoder. LookupViT provides 2x reduction in FLOPs while upholding or improving accuracy across these domains. In addition, LookupViT also demonstrates out-of-the-box robustness on corrupted image classification (ImageNet-C), improving by more than 4% over ViT.
Whole Slide Image (WSI) classification has recently gained much attention in digital pathology, facing the researchers with unique challenges. We tackle two main challenges in existing (WSI) classification approaches. Initially, these methods invest a considerable amount of time and computational resources into pre-training a vision backbone on domain-specific training datasets for embedding generation. This limits the development of such models to certain groups and institutions with computational budgets, and hence slowing their development progress. Furthermore, they typically employ architectures with limited approximation capabilities for the Multiple Instance Learning (MIL), which are inadequate for the intricate characteristics of WSIs, resulting in a sub-optimal accuracy. Our research proposes novel solutions to these issues and balances efficiency and performance. Firstly, we present the novel approach of the continual self-supervised pretraining of ImageNet-1K Vision Transformers (ViTs) equipped with Adapters on pathology domain datasets, achieving a level of efficiency orders of magnitude better than prior techniques. Secondly, we introduce an innovative Sparse Transformer architecture and theoretically prove its universal approximability, featuring a new upper bound for the layer count. We additionally evaluate our method on both pathology and MIL datasets, showcasing its superiority on image- and patch-level accuracies compared to the previous methods. Our code is available at \url{https://github.com/jafarinia/snuffy}.
Multimodal models have gained increasing popularity recently. Many works have been proposed to learn the representations for different modalities. The representation can learn shared information from these domains, leading to increased and coherent joint and cross-generation. However, these works mainly considered standard Gaussian or Laplacian as their prior distribution. It can be challenging for the uni-modal and non-informative distribution to capture all the information from multiple data types. Meanwhile, energy-based models (EBM) have shown their effectiveness in multiple tasks due to their expressiveness and flexibility. But its capacity has yet to be discovered for the multimodal generative models. In this paper, we propose a novel framework to train multimodal latent generative models together with the energy-based models. The proposed method can lead to more expressive and informative prior which can better capture the information within multiple modalities. Our experiments showed that our model is effective and can increase generation coherence and latent classification for different multimodal datasets.
Traditional deep learning models rely on methods such as softmax cross-entropy and ArcFace loss for tasks like classification and face recognition. These methods mainly explore angular features in a hyperspherical space, often resulting in entangled inter-class features due to dense angular data across many classes. In this paper, a new field of feature exploration is proposed known as \textit{HyperSpaceX} which enhances class discrimination by exploring both angular and radial dimensions in multi-hyperspherical spaces, faciliated by a novel \textit{DistArc} loss. The proposed DistArc loss encompasses three feature arrangement components: two angular and one radial, enforcing intra-class binding and inter-class separation in multi-radial arrangement improving feature discriminability. Evaluation of \textit{HyperSpaceX} framework for the novel representation utilizes a proposed predictive measure that accounts for both angular and radial elements, providing a more comprehensive assessment of model accuracy beyond standard metrics. Experiments across six object classification and five face recognition datasets demonstrate state-of-the-art \textit{(SoTA)} results obtained from \textit{HyperSpaceX}, achieving up to a 20\% performance improvement on large-scale object datasets.
An increasingly massive number of remote-sensing images spurs the development of extensible object detectors that can detect objects beyond training categories. In this paper, we aim to develop open-vocabulary object detection (OVD) technique in aerial images that scales up object vocabulary size beyond training data. Two fundamental challenges hinder open vocabulary object detection performance: the qualities of both the class-agnostic region proposals and the pseudo-labels that can generalize well to novel object categories. To simultaneously generate high-quality proposals and pseudo-labels, we propose CastDet, a CLIP-activated student-teacher open-vocabulary object Detection framework. Our end-to-end framework following the student-teacher self-learning mechanism employs the RemoteCLIP model as an extra omniscient teacher with rich knowledge. By doing so, our approach boosts not only novel object proposals but also classification. Furthermore, we devise a dynamic label queue strategy to maintain high-quality pseudo labels during batch training. We conduct extensive experiments on multiple existing aerial object detection datasets, which are set up for the OVD task. Experimental results demonstrate our CastDet achieving superior open-vocabulary detection performance, e.g., reaching 40.5% mAP, which outperforms previous methods Detic/ViLD by 23.7%/14.9% on the VisDroneZSD dataset. To our best knowledge, this is the first work to apply and develop the open-vocabulary object detection technique for aeria images.
The concept bottleneck model (CBM) is an interpretable-by-design framework that makes decisions by first predicting a set of interpretable concepts, and then predicting the class label based on the given concepts. Existing CBMs are trained with a fixed set of concepts (concepts are either annotated by the dataset or queried from language models). However, this closed-world assumption is unrealistic in practice, as users may wonder about the role of any desired concept in decision-making after the model is deployed. Inspired by the large success of recent vision-language pre-trained models such as CLIP in zero-shot classification, we propose OpenCBM'' to equip the CBM with open vocabulary concepts via: (1) Aligning the feature space of a trainable image feature extractor with that of a CLIP's image encoder via a prototype based feature alignment; (2) Simultaneously training an image classifier on the downstream dataset; (3) Reconstructing the trained classification head via any set of user-desired textual concepts encoded by CLIP's text encoder. To reveal potentially missing concepts from users, we further propose to iteratively find the closest concept embedding to the residual parameters during the reconstruction until the residual is small enough. To the best of our knowledge, ourOpenCBM'' is the first CBM with concepts of open vocabularies, providing users the unique benefit such as removing, adding, or replacing any desired concept to explain the model's prediction even after a model is trained. Moreover, our model significantly outperforms the previous state-of-the-art CBM by 9% in the classification accuracy on the benchmark dataset CUB-200-2011. The code will be released upon acceptance.
Fast Adversarial Training (FAT) has gained increasing attention within the research community owing to its efficacy in improving adversarial robustness. Particularly noteworthy is the challenge posed by catastrophic overfitting (CO) in this field. Although existing FAT approaches have made strides in mitigating CO, the ascent of adversarial robustness occurs with a non-negligible decline in classification accuracy on clean samples. To tackle this issue, we initially employ the feature activation differences between clean and adversarial examples to analyze the underlying causes of CO. Intriguingly, our findings reveal that CO can be attributed to the feature coverage induced by a few specific pathways. By intentionally manipulating feature activation differences in these pathways with well-designed regularization terms, we can effectively mitigate and induce CO, providing further evidence for this observation. Notably, models trained stably with these terms exhibit superior performance compared to prior FAT work. On this basis, we harness CO to achieve `attack obfuscation', aiming to bolster model performance. Consequently, the models suffering from CO can attain optimal classification accuracy on both clean and adversarial data when adding random noise to inputs during evaluation. We also validate their robustness against transferred adversarial examples and the necessity of inducing CO to improve robustness. Hence, CO may not be a problem that has to be solved.
Computer vision methods that explicitly detect object parts and reason on them are a step towards inherently interpretable models. Existing approaches that perform part discovery driven by a fine-grained classification task make very restrictive assumptions on the geometric properties of the discovered parts; they should be small and compact. Although this prior is useful in some cases, in this paper we show that pre-trained transformer-based vision models, such as self-supervised DINOv2 ViT, enable the relaxation of these constraints. In particular, we find that a total variation (TV) prior, which allows for multiple connected components of any size, substantially outperforms previous work. We test our approach on three fine-grained classification benchmarks: CUB, PartImageNet and Oxford Flowers, and compare our results to previously published methods as well as a re-implementation of the state-of-the-art method PDiscoNet with a transformer-based backbone. We consistently obtain substantial improvements across the board, both on part discovery metrics and the downstream classification task, showing that the strong inductive biases in self-supervised ViT models require to rethink the geometric priors that can be used for unsupervised part discovery.