Noisy few-shot learning (NFSL) presents novel challenges primarily due to the interplay between noisy labels and limited training data. While data cleansing offers a viable solution to address noisy labels in the general learning settings, it exacerbates information loss in FSL due to limited training data, resulting in inadequate model training. To best recover the underlying task manifold corrupted by the noisy labels, we resort to learning from uniquely designed unsupervised auxiliary tasks to compensate for information loss. Using unsupervised tasks can effectively avoid additional annotation costs and minimize the risk of introducing additional label noises. However, a randomly constructed unsupervised task may misguide the model to learn sample-specific features that are likely to compromise the primary few-shot learning task due to the noisy weak learning signals. We propose to conduct novel auxiliary task selection to ensure the intra-diversity among the unlabeled samples within a task. Domain invariant features are then learned from carefully constructed auxiliary tasks to best recover the original data manifold. We conduct theoretical analysis to derive novel generalization bounds for learning with auxiliary tasks. Extensive experiments are conducted to demonstrate that our method outperforms existing noisy few-shot learning methods under various in-domain and cross-domain few-shot classification benchmarks.
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Hyperspectral image classification, a task that assigns pre-defined classes to each pixel in a hyperspectral image of remote sensing scenes, often faces challenges due to the neglect of correlations between spectrally similar pixels. This oversight can lead to inaccurate edge definitions and difficulties in managing minor spectral variations in contiguous areas. To address these issues, we introduce the novel Dual-stage Spectral Supertoken Classifier (DSTC), inspired by superpixel concepts. DSTC employs spectrum-derivative-based pixel clustering to group pixels with similar spectral characteristics into spectral supertokens. By projecting the classification of these tokens onto the image space, we achieve pixel-level results that maintain regional classification consistency and precise boundary. Moreover, recognizing the diversity within tokens, we propose a class-proportion-based soft label. This label adaptively assigns weights to different categories based on their prevalence, effectively managing data distribution imbalances and enhancing classification performance. Comprehensive experiments on WHU-OHS, IP, KSC, and UP datasets corroborate the robust classification capabilities of DSTC and the effectiveness of its individual components. The code will be made publicly available.
Knowledge distillation (KD) is a particular technique of model compression that exploits a large well-trained {\tt teacher} neural network to train a small {\tt student} network . Treating {\tt teacher}'s feature as knowledge, prevailing methods train {\tt student} by aligning its features with the {\tt teacher}'s, e.g., by minimizing the KL-divergence or L2-distance between their (logits) features. While it is natural to assume that better feature alignment helps distill {\tt teacher}'s knowledge, simply forcing this alignment does not directly contribute to the {\tt student}'s performance, e.g., classification accuracy. For example, minimizing the L2 distance between the penultimate-layer features (used to compute logits for classification) does not necessarily help learn a better {\tt student} classifier. We are motivated to regularize {\tt student} features at the penultimate layer using {\tt teacher} towards training a better {\tt student} classifier. Specifically, we present a rather simple method that uses {\tt teacher}'s class-mean features to align {\tt student} features w.r.t their {\em direction}. Experiments show that this significantly improves KD performance. Moreover, we empirically find that {\tt student} produces features that have notably smaller norms than {\tt teacher}'s, motivating us to regularize {\tt student} to produce large-norm features. Experiments show that doing so also yields better performance. Finally, we present a simple loss as our main technical contribution that regularizes {\tt student} by simultaneously (1) aligning the \emph{direction} of its features with the {\tt teacher} class-mean feature, and (2) encouraging it to produce large-\emph{norm} features. Experiments on standard benchmarks demonstrate that adopting our technique remarkably improves existing KD methods, achieving the state-of-the-art KD performance through the lens of image classification (on ImageNet and CIFAR100 datasets) and object detection (on the COCO dataset).
Text-conditional medical image generation is vital for radiology, augmenting small datasets, preserving data privacy, and enabling patient-specific modeling. However, its applications in 3D medical imaging, such as CT and MRI, which are crucial for critical care, remain unexplored. In this paper, we introduce GenerateCT, the first approach to generating 3D medical imaging conditioned on free-form medical text prompts. GenerateCT incorporates a text encoder and three key components: a novel causal vision transformer for encoding 3D CT volumes, a text-image transformer for aligning CT and text tokens, and a text-conditional super-resolution diffusion model. Without directly comparable methods in 3D medical imaging, we benchmarked GenerateCT against cutting-edge methods, demonstrating its superiority across all key metrics. Importantly, we explored GenerateCT's clinical applications by evaluating its utility in a multi-abnormality classification task. First, we established a baseline by training a multi-abnormality classifier on our real dataset. To further assess the model's generalization to external datasets and its performance with unseen prompts in a zero-shot scenario, we employed an external dataset to train the classifier, setting an additional benchmark. We conducted two experiments in which we doubled the training datasets by synthesizing an equal number of volumes for each set using GenerateCT. The first experiment demonstrated an 11% improvement in the AP score when training the classifier jointly on real and generated volumes. The second experiment showed a 7% improvement when training on both real and generated volumes based on unseen prompts. Moreover, GenerateCT enables the scaling of synthetic training datasets to arbitrary sizes. As an example, we generated 100,000 3D CT volumes, fivefold the number in our real dataset, and trained the classifier exclusively on these synthetic volumes. Impressively, this classifier surpassed the performance of the one trained on all available real data by a margin of 8%. Lastly, domain experts evaluated the generated volumes, confirming a high degree of alignment with the text prompts. Access our code, model weights, training data, and generated data at https://github.com/ibrahimethemhamamci/GenerateCT
Developing self-supervised learning (SSL) models that can learn universal and transferable representations of H&E gigapixel whole-slide images (WSIs) is becoming increasingly valuable in computational pathology. These models hold the potential to advance critical tasks such as few-shot classification, slide retrieval, and patient stratification. Existing approaches for slide representation learning extend the principles of SSL from small images (e.g., 224 x 224 patches) to entire slides, usually by aligning two different augmentations (or \emph{views}) of the slide. Yet the resulting representation remains constrained by the limited clinical and biological relevance of the views. Instead, we postulate that slides stained with multiple markers, such as immunohistochemistry or special stains, can be seen as different views of the same tissue and can constitute a rich task-agnostic training signal. To this end, we introduce MADELEINE, a multimodal pretraining strategy for slide representation learning. MADELEINE is trained with a dual global-local cross-stain alignment objective on large cohorts of breast cancer samples (N=4,211 WSIs across five stains) and kidney transplant samples (N=12,070 WSIs across four stains). We demonstrate the superior quality of slide representations learned by MADELEINE on various downstream evaluations, ranging from morphological and molecular classification to prognostic prediction, on a total of 21 tasks using 7,299 WSIs from multiple medical centers. Code will be released upon acceptance.
Public datasets play a crucial role in advancing data-centric AI, yet they remain vulnerable to illicit uses. This paper presents `undercover bias,' a novel dataset watermarking method that can reliably identify and verify unauthorized data usage. Our approach is inspired by an observation that trained models often inadvertently learn biased knowledge and can function on bias-only data, even without any information directly related to a target task. Leveraging this, we deliberately embed class-wise hidden bias via unnoticeable watermarks, which are unrelated to the target dataset but share the same labels. Consequently, a model trained on this watermarked data covertly learns to classify these watermarks. The model's performance in classifying the watermarks serves as irrefutable evidence of unauthorized usage, which cannot be achieved by chance. Our approach presents multiple benefits: 1) stealthy and model-agnostic watermarks; 2) minimal impact on the target task; 3) irrefutable evidence of misuse; and 4) improved applicability in practical scenarios. We validate these benefits through extensive experiments and extend our method to fine-grained classification and image segmentation tasks. Our implementation is available at here.
Long-tailed object detection faces great challenges because of its extremely imbalanced class distribution. Recent methods mainly focus on the classification bias and its loss function design, while ignoring the subtle influence of the regression branch. This paper shows that the regression bias exists and does adversely and seriously impact the detection accuracy. While existing methods fail to handle the regression bias, the class-specific regression head for rare classes is hypothesized to be the main cause of it in this paper. As a result, three kinds of viable solutions to cater for the rare categories are proposed, including adding a class-agnostic branch, clustering heads and merging heads. The proposed methods brings in consistent and significant improvements over existing long-tailed detection methods, especially in rare and common classes. The proposed method achieves state-of-the-art performance in the large vocabulary LVIS dataset with different backbones and architectures. It generalizes well to more difficult evaluation metrics, relatively balanced datasets, and the mask branch. This is the first attempt to reveal and explore rectifying of the regression bias in long-tailed object detection.
Traditional audio-visual methods rely on independent audio and visual backbones, which is costly and not scalable. In this work, we investigate using an audio-visual siamese network (AVSiam) for efficient and scalable audio-visual pretraining. Our framework uses a single shared vision transformer backbone to process audio and visual inputs, improving its parameter efficiency, reducing the GPU memory footprint, and allowing us to scale our method to larger datasets and model sizes. We pretrain our model using a contrastive audio-visual matching objective with a multi-ratio random masking scheme, which further speeds up the audio-visual pretraining process and enables our model to process larger audio-visual instance batches, helpful for contrastive learning. Unlike prior audio-visual methods, our method can robustly handle audio-only, visual-only, and audio-visual inputs with a single shared ViT backbone. Furthermore, despite using the shared backbone for both modalities, AVSiam achieves competitive or even better results than prior methods on AudioSet and VGGSound for audio-visual classification and audio-visual retrieval. Our code and models will be made publicly available.
Contrastive vision-language models, such as CLIP, have garnered considerable attention for various dowmsteam tasks, mainly due to the remarkable ability of the learned features for generalization. However, the features they learned often blend content and style information, which somewhat limits their generalization capabilities under distribution shifts. To address this limitation, we adopt a causal generative perspective for multimodal data and propose contrastive learning with data augmentation to disentangle content features from the original representations. To achieve this, we begins with exploring image augmentation techniques and develop a method to seamlessly integrate them into pre-trained CLIP-like models to extract pure content features. Taking a step further, recognizing the inherent semantic richness and logical structure of text data, we explore the use of text augmentation to isolate latent content from style features. This enables CLIP-like model's encoders to concentrate on latent content information, refining the learned representations by pre-trained CLIP-like models. Our extensive experiments across diverse datasets demonstrate significant improvements in zero-shot and few-shot classification tasks, alongside enhanced robustness to various perturbations. These results underscore the effectiveness of our proposed methods in refining vision-language representations and advancing the state-of-the-art in multimodal learning.
IGNORE: Information Gap-based False Negative Loss Rejection for Single Positive Multi-Label Learning
PDF ↗Single Positive Multi-Label Learning (SPML) is a multi-label classification task in which each image is assigned only one positive label but the other labels are not annotated. Most approaches for SPML assume unannotated labels as negatives (``Assumed Negative", AN). However, with this assumption, some positive labels are inevitably regarded as negative (false negative), resulting in model performance degradation. Therefore, identifying false negatives is the most important with AN assumption. Previous approaches identified false negative labels using the model outputs of assumed negative labels. However, models were trained with noisy negative labels, their outputs were not reliable. Therefore, it is necessary to consider effectively utilizing the most reliable information in SPML for identifying false negative labels. In this paper, we propose an Information Gap-based false negative Loss Rejection method (IG-LR) for SPML. We generate the masked image that all parts are removed except the discriminative area of the single positive label. It is reasonable that when there is no information of an object in the masked image, the model’s logit for that object is low. Based on this intuition, we identify the false negative labels if they have a significant model’s logit gap between masked image and original image. By rejecting false negatives in the model training, we can prevent the model from being biased to false negative labels, and build more reliable models. We evaluate our method on four datasets: Pascal VOC 2012, MS COCO, NUSWIDE, and CUB. Compared to the previous state-of-the-art methods in SPML, our method outperforms on most of the datasets.
Stain shifts are prevalent in histopathology images, and typically dealt with by normalization or augmentation. Considering training-time methods are limited in dealing with unseen stains, we propose a test-time stain adaptation method (TT-SaD) with diffusion models that achieves stain adaptation by solving a nonlinear inverse problem during testing. TT-SaD is promising in that it only needs a single domain for training but can adapt well from other domains during testing, preventing models from retraining whenever there are new data available. For tumor classification, stain adaptation by TT-SaD outperforms state-of-the-art diffusion model-based test-time methods. Moreover, TT-SaD beats training-time methods when testing on data that are inaccessible during training. To our knowledge, the study of stain adaptation in diffusion model during testing time is relatively unexplored.
Facial Expression Analysis (FEA) remains a challenging task due to unexpected task-irrelevant noise, such as identity, head pose, and background. To address this issue, this paper proposes a novel framework, called Norface, that is unified for both Action Unit (AU) analysis and Facial Emotion Recognition (FER) tasks. Norface consists of a normalization network and a classification network. First, the carefully designed normalization network struggles to directly remove the above task-irrelevant noise, by maintaining facial expression consistency but normalizing all original images to a common identity with consistent pose, and background. Then, these additional normalized images are fed into the classification network. Due to consistent identity and other factors (e.g. head pose, background, etc.), the normalized images enable the classification network to extract useful expression information more effectively. Additionally, the classification network incorporates a Mixture of Experts to refine the latent representation, including handling the input of facial representations and the output of multiple (AU or emotion) labels. Extensive experiments validate the carefully designed framework with the insight of identity normalization. The proposed method outperforms existing SOTA methods in multiple facial expression analysis tasks, including AU detection, AU intensity estimation, and FER tasks, as well as their cross-dataset tasks. For the normalized datasets and code please visit project page.
Generating medical reports for X-ray images is a challenging task, particularly in an unpaired scenario where paired image-report data is unavailable for training. To address this challenge, we propose a novel model that leverages the available information in two distinct datasets, one comprising reports and the other consisting of images. The core idea of our model revolves around the notion that combining auto-encoding report generation with multi-modal (report-image) alignment can offer a solution. However, the challenge persists regarding how to achieve this alignment when pair correspondence is absent. Our proposed solution involves the use of auxiliary tasks, particularly contrastive learning and classification, to position related images and reports in close proximity to each other. This approach differs from previous methods that rely on pre-processing steps using external information stored in a knowledge graph. Our model, named MedRAT, surpasses previous state-of-the-art methods, demonstrating the feasibility of generating comprehensive medical reports without the need for paired data or external tools.
The inherent richness of geometric information in point cloud underscores the necessity of leveraging group equivariance, as preserving the topological structure of the point cloud up to the feature space provides an intuitive inductive bias for solving problems in 3D space. Since manifesting the symmetry by means of model architecture has an advantage over the dependence on the augmentation, it has been a crucial research topic in the point cloud field. However, existing methods have limitations in the non-continuity of groups or the complex architecture causing computational inefficiency. In this paper, we propose CSEConv: a novel point convolution layer equivariant under continuous SO(3) actions. Its structure is founded on the framework of group theory, realizing the convolution module defined on a sphere. Implementing its filters to be explicit, continuous, and rigorously equivariant functions defined upon the double coset space is the distinctive factor which makes our method more scalable than previous approaches. From the classification experiments on synthetic and real-world point cloud datasets, our method achieves the best accuracy, to the best of our knowledge, amidst point-based models equivariant against continuous rotation group.
This paper, for the first time, marries large foundation models with human sketch understanding. We demonstrate what this brings -- a paradigm shift in terms of generalised sketch representation learning (e.g., classification). This generalisation happens on two fronts: (i) generalisation across unknown categories (i.e., open-set), and (ii) generalisation traversing abstraction levels (i.e., good and bad sketches), both being timely challenges that remain unsolved in the sketch literature. Our design is intuitive and centred around transferring the already stellar generalisation ability of CLIP to benefit generalised learning for sketches. We first "condition" the vanilla CLIP model by learning sketch-specific prompts using a novel auxiliary head of raster to vector sketch conversion. This importantly makes CLIP "sketch-aware". We then make CLIP acute to the inherently different sketch abstraction levels. This is achieved by learning a codebook of abstraction-specific prompt biases, a weighted combination of which facilitates the representation of sketches across abstraction levels -- low abstract edge-maps, medium abstract sketches in TU-Berlin, and highly abstract doodles in QuickDraw. Our framework surpasses popular sketch representation learning algorithms in both zero-shot and few-shot setups and in novel settings across different abstraction boundaries.
Language-Assisted Skeleton Action Understanding for Skeleton-Based Temporal Action Segmentation
PDF ↗Skeleton-based Temporal Action Segmentation (STAS) aims at densely segmenting and classifying human actions in long untrimmed skeletal motion sequences. Existing STAS methods primarily model the spatial dependencies among joints and the temporal relationships among frames to generate frame-level one-hot classifications. However, these research overlook the deep mining of semantic relations among joints as well as actions at a linguistic level, which limits the comprehensiveness of skeleton action understanding. In this work, we propose a Language-assisted Skeleton Action Understanding (LaSA) method, leveraging Large-scale Language Models (LLM) to assist in learning semantic relationships among joints and actions. Specifically, in terms of joint relationships, the Joint Relationships Establishment (JRE) module establishes correlations among joints in the feature sequence through attention between joint texts and embeds joint texts as position embeddings to differentiate distinct joints. Regarding action relationships, the Action Relationships Supervision (ARS) module enhances the discrimination across action classes through contrastive learning of single-class action-text pairs and temporally models the semantic associations of adjacent actions by contrasting mixed-class clip-text pairs. Performance evaluation on five public datasets demonstrates that LaSA has achieved state-of-the-art performance.
Semantic segmentation is an important task for many applications but it is still quite challenging to achieve advanced performance with limited computational costs. In this paper, we present CGRSeg, an efficient yet competitive segmentation framework based on context-guided spatial feature reconstruction. In it, a Rectangular Self-Calibration Module is carefully designed for spatial feature reconstruction and pyramid context extraction. It captures the global context in both horizontal and vertical directions and gets the axial global context to explicitly model rectangular key areas. A shape self-calibration function is designed to make the key areas more close to the foreground object. Besides, a lightweight Dynamic Prototype Guided head is proposed to improve the classification of foreground objects by explicit class embedding. Our CGRSeg is extensively evaluated on ADE20K, COCO-Stuff, and Pascal Context benchmarks, and achieves state-of-the-art semantic performance. Specifically, it achieves 43.6% mIoU on ADE20K with only 4.0 GFLOPs, which is 0.9% and 2.5% mIoU better than SeaFormer and SegNeXt but with about 38.0% fewer GFLOPs.
The binary cross-entropy (BCE) loss function is widely utilized in multi-label classification (MLC) tasks, treating each label independently. The log-sum-exp pairwise (LSEP) loss, which emphasizes higher logits for positive classes over negative ones within a sample and accounts for label dependencies, has demonstrated effectiveness for MLC. However, our experiments suggest that its performance in long-tailed multi-label classification (LTMLC) appears to be inferior to that of BCE. In this study, we investigate the impact of the log-sum-exp operation on recognition and explore optimization avenues. Our observations reveal two primary shortcomings of LSEP that lead to its poor performance in LTMLC: 1) the indiscriminate use of label dependencies without consideration of the distribution shift between training and test sets, and 2) the overconfidence in negative labels with features similar to those of positive labels. To mitigate these problems, we propose a distributionally robust loss (DR), which includes class-wise LSEP and a negative gradient constraint. Additionally, our findings indicate that the BCE-based loss is somewhat complementary to the LSEP-based loss, offering enhanced performance upon integration. Extensive experiments conducted on two LTMLC datasets, VOC-LT and COCO-LT, demonstrate the consistent effectiveness of our proposed method. The code will be made publicly available shortly.
Label dependencies have been widely studied in multi-label image recognition for improving performances. Previous methods mainly considered label co-occurrences as label correlations. In this paper, we show that label co-occurrences may be insufficient to represent label correlations, and modeling label correlations relies on latent context information. To this end, we propose a latent context embedding information network for multi-label image recognition. Our proposal is straightforward and contains three key modules to correspondingly tackle three questions, \ie, where to locate the latent context information, how to utilize the latent context information, and how to model label correlations with context-aware features. First, the multi-level context feature fusion module fuses the multi-level feature pyramids to obtain sufficient latent context information. Second, the latent context information embedding module aggregates the latent context information into categorical features, and thus the label correlation can be directly established. Moreover, we use the label correlation capturing module to model label correlations with full and partial manners, respectively. Comprehensive experiments validate the correctness of our arguments and the effectiveness of our method. In both generic multi-label classification and partial-label multi-label classification, our proposed method consistently achieves promising results.
Deep neural networks (DNNs) have demonstrated effectiveness in various fields. However, DNNs are vulnerable to backdoor attacks, which inject a unique pattern, called trigger, in the input to cause misclassification to an attack-chosen target label. While existing works have proposed various methods to mitigate backdoor effects in poisoned models, they tend to be less effective against recent advanced attacks. In this paper, we introduce a novel post-training defense technique UNIT that can effectively remove backdoors for a variety of attacks. In specific, UNIT approximates a unique and tight activation distribution for each neuron in the model. It then proactively dispels substantially large activation values that exceed the approximated boundaries. Our experimental results demonstrate that UNIT outperforms 9 popular defense methods against 14 existing backdoor attacks, including 2 advanced attacks, using only 5\% of clean training data.