VeCAF: VLM-empowered Collaborative Active Finetuning with Training Objective Awareness

Finetuning a pretrained vision model (PVM) is a common technique for learning downstream vision tasks. The conventional finetuning process with the randomly sampled data points results in diminished training efficiency. To address this drawback, we propose a novel approach, VLM-empowered Collaborative Active Finetuning (VeCAF). VeCAF optimizes a parametric data selection model by incorporating the training objective of the model being tuned. Effectively, this guides the PVM towards the performance goal with improved data and computational efficiency. As vision-language models (VLMs) have achieved significant advancements by establishing a robust connection between image and language domains, we exploit the inherent semantic richness of the text embedding space and utilize text embedding of pretrained VLM models to augment PVM image features for better data selection and finetuning. Furthermore, the flexibility of text-domain augmentation gives VeCAF a unique ability to handle out-of-distribution scenarios without external augmented data. Extensive experiments show the leading performance and high efficiency of VeCAF that is superior to baselines in both in-distribution and out-of-distribution image classification tasks. On ImageNet, VeCAF needs up to 3.3x less training batches to reach the target performance compared to full finetuning and achieves 2.8% accuracy improvement over SOTA methods with the same number of batches.

Efficient Deweather Mixture-of-Experts with Uncertainty-aware Feature-wise Linear Modulation

The Mixture-of-Experts (MoE) approach has demonstrated outstanding scalability in multi-task learning including low-level upstream tasks such as concurrent removal of multiple adverse weather effects. However, the conventional MoE architecture with parallel Feed Forward Network (FFN) experts leads to significant parameter and computational overheads that hinder its efficient deployment. In addition, the naive MoE linear router is suboptimal in assigning task-specific features to multiple experts which limits its further scalability. In this work, we propose an efficient MoE architecture with weight sharing across the experts. Inspired by the idea of linear feature modulation (FM), our architecture implicitly instantiates multiple experts via learnable activation modulations on a single shared expert block. The proposed Feature Modulated Expert (FME) serves as a building block for the novel Mixture-of-Feature-Modulation-Experts (MoFME) architecture, which can scale up the number of experts with low overhead. We further propose an Uncertainty-aware Router (UaR) to assign task-specific features to different FM modules with well-calibrated weights. This enables MoFME to effectively learn diverse expert functions for multiple tasks. The conducted experiments on the multi-deweather task show that our MoFME outperforms the baselines in the image restoration quality by 0.1-0.2 dB and achieves SOTA-compatible performance while saving more than 72% of parameters and 39% inference time over the conventional MoE counterpart. Experiments on the downstream segmentation and classification tasks further demonstrate the generalizability of MoFME to real open-world applications.

Split-Ensemble: Efficient OOD-aware Ensemble via Task and Model Splitting

Uncertainty estimation is crucial for machine learning models to detect out-of-distribution (OOD) inputs. However, the conventional discriminative deep learning classifiers produce uncalibrated closed-set predictions for OOD data. A more robust classifiers with the uncertainty estimation typically require a potentially unavailable OOD dataset for outlier exposure training, or a considerable amount of additional memory and compute to build ensemble models. In this work, we improve on uncertainty estimation without extra OOD data or additional inference costs using an alternative Split-Ensemble method. Specifically, we propose a novel subtask-splitting ensemble training objective, where a common multiclass classification task is split into several complementary subtasks. Then, each subtask's training data can be considered as OOD to the other subtasks. Diverse submodels can therefore be trained on each subtask with OOD-aware objectives. The subtask-splitting objective enables us to share low-level features across submodels to avoid parameter and computational overheads. In particular, we build a tree-like Split-Ensemble architecture by performing iterative splitting and pruning from a shared backbone model, where each branch serves as a submodel corresponding to a subtask. This leads to improved accuracy and uncertainty estimation across submodels under a fixed ensemble computation budget. Empirical study with ResNet-18 backbone shows Split-Ensemble, without additional computation cost, improves accuracy over a single model by 0.8%, 1.8%, and 25.5% on CIFAR-10, CIFAR-100, and Tiny-ImageNet, respectively. OOD detection for the same backbone and in-distribution datasets surpasses a single model baseline by, correspondingly, 2.2%, 8.1%, and 29.6% mean AUROC. Codes will be publicly available at https://antonioo-c.github.io/projects/split-ensemble

Concurrent Misclassification and Out-of-Distribution Detection for Semantic Segmentation via Energy-Based Normalizing Flow

Recent semantic segmentation models accurately classify test-time examples that are similar to a training dataset distribution. However, their discriminative closed-set approach is not robust in practical data setups with distributional shifts and out-of-distribution (OOD) classes. As a result, the predicted probabilities can be very imprecise when used as confidence scores at test time. To address this, we propose a generative model for concurrent in-distribution misclassification (IDM) and OOD detection that relies on a normalizing flow framework. The proposed flow-based detector with an energy-based inputs (FlowEneDet) can extend previously deployed segmentation models without their time-consuming retraining. Our FlowEneDet results in a low-complexity architecture with marginal increase in the memory footprint. FlowEneDet achieves promising results on Cityscapes, Cityscapes-C, FishyScapes and SegmentMeIfYouCan benchmarks in IDM/OOD detection when applied to pretrained DeepLabV3+ and SegFormer semantic segmentation models.

Cross-Domain Object Detection with Mean-Teacher Transformer

Recently, DEtection TRansformer (DETR), an end-to-end object detection pipeline, has achieved promising performance. However, it requires large-scale labeled data and suffers from domain shift, especially when no labeled data is available in the target domain. To solve this problem, we propose an end-to-end cross-domain detection transformer based on the mean teacher knowledge transfer (MTKT), which transfers knowledge between domains via pseudo labels. To improve the quality of pseudo labels in the target domain, which is a crucial factor for better domain adaptation, we design three levels of source-target feature alignment strategies based on the architecture of the Transformer, including domain query-based feature alignment (DQFA), bi-level-graph-based prototype alignment (BGPA), and token-wise image feature alignment (TIFA). These three levels of feature alignment match the global, local, and instance features between source and target, respectively. With these strategies, more accurate pseudo labels can be obtained, and knowledge can be better transferred from source to target, thus improving the cross-domain capability of the detection transformer. Extensive experiments demonstrate that our proposed method achieves state-of-the-art performance on three domain adaptation scenarios, especially the result of Sim10k to Cityscapes scenario is remarkably improved from 52.6 mAP to 57.9 mAP. Code will be released.

RandomNet: Towards Fully Automatic Neural Architecture Design for Multimodal Learning

Almost all neural architecture search methods are evaluated in terms of performance (i.e. test accuracy) of the model structures that it finds. Should it be the only metric for a good autoML approach? To examine aspects beyond performance, we propose a set of criteria aimed at evaluating the core of autoML problem: the amount of human intervention required to deploy these methods into real world scenarios. Based on our proposed evaluation checklist, we study the effectiveness of a random search strategy for fully automated multimodal neural architecture search. Compared to traditional methods that rely on manually crafted feature extractors, our method selects each modality from a large search space with minimal human supervision. We show that our proposed random search strategy performs close to the state of the art on the AV-MNIST dataset while meeting the desirable characteristics for a fully automated design process.