CVPR 2024 Papers

Adapters provide an efficient and lightweight mechanism for adapting trained transformer models to a variety of different tasks. However they have often been found to be outperformed by other adaptation mechanisms including low-rank adaptation. In this paper we provide an in-depth study of adapters their internal structure as well as various implementation choices. We uncover pitfalls for using adapters and suggest a concrete improved adapter architecture called Adapter+ that not only outperforms previous adapter implementations but surpasses a number of other more complex adaptation mechanisms in several challenging settings. Despite this our suggested adapter is highly robust and unlike previous work requires little to no manual intervention when addressing a novel scenario. Adapter+ reaches state-of-the-art average accuracy on the VTAB benchmark even without a per-task hyperparameter optimization.

Domain adaptation adapts models to various scenes with different appearances. In this field active domain adaptation is crucial in effectively sampling a limited number of data in the target domain. We propose an active domain adaptation method for object detection focusing on quantifying the undetectability of objects. Existing methods for active sampling encounter challenges in considering undetected objects while estimating the uncertainty of model predictions. Our proposed active sampling strategy addresses this issue using an active learning approach that simultaneously accounts for uncertainty and undetectability. Our newly proposed False Negative Prediction Module evaluates the undetectability of images containing undetected objects enabling more informed active sampling. This approach considers previously overlooked undetected objects thereby reducing false negative errors. Moreover using unlabeled data our proposed method utilizes uncertainty-guided pseudo-labeling to enhance domain adaptation further. Extensive experiments demonstrate that the performance of our proposed method closely rivals that of fully supervised learning while requiring only a fraction of the labeling efforts needed for the latter.

Continual test-time domain adaptation (CTTA) aims to adapt the source pre-trained model to a continually changing target domain without additional data acquisition or labeling costs. This issue necessitates an initial performance enhancement within the present domain without labels while concurrently averting an excessive bias toward the current domain. Such bias exacerbates catastrophic forgetting and diminishes the generalization ability to future domains. To tackle the problem this paper designs a versatile framework to capture high-quality supervision signals from three aspects: 1) The adaptive thresholds are employed to determine the reliability of pseudo-labels; 2) The knowledge from the source pre-trained model is utilized to adjust the unreliable one and 3) By evaluating past supervision signals we calculate a diversity score to ensure subsequent generalization. In this way we form a complete supervisory signal generation framework which can capture the current domain discriminative and reserve generalization in future domains. Finally to avoid catastrophic forgetting we design a weighted soft parameter alignment method to explore the knowledge from the source model. Extensive experimental results demonstrate that our method performs well on several benchmark datasets.

High-end lenses although offering high-quality images suffer from both insufficient affordability and bulky design which hamper their applications in low-budget scenarios or on low-payload platforms. A flexible scheme is to tackle the optical aberration of low-end lenses computationally. However it is highly demanded but quite challenging to build a general model capable of handling non-stationary aberrations and covering diverse lenses especially in a blind manner. To address this issue we propose a universal solution by extensively utilizing the physical properties of camera lenses: (i) reducing the complexity of lens aberrations i.e. lens-specific non-stationary blur by warping annual-ring-shaped sub-images into rectangular stripes to transform non-uniform degenerations into a uniform one (ii) building a low-dimensional non-negative orthogonal representation of lens blur kernels to cover diverse lenses; (iii) designing a decoupling network to decompose the input low-quality image into several components degenerated by above kernel bases and applying corresponding pre-trained deconvolution networks to reverse the degeneration. Benefiting from the proper incorporation of lenses' physical properties and unique network design the proposed method achieves superb imaging quality wide applicability for various lenses high running efficiency and is totally free of kernel calibration. These advantages bring great potential for scenarios requiring lightweight high-quality photography.

Efficient transfer learning (ETL) is receiving increasing attention to adapt large pre-trained language-vision models on downstream tasks with a few labeled samples. While significant progress has been made we reveal that state-of-the-art ETL approaches exhibit strong performance only in narrowly-defined experimental setups and with a careful adjustment of hyperparameters based on a large corpus of labeled samples. In particular we make two interesting and surprising empirical observations. First to outperform a simple Linear Probing baseline these methods require to optimize their hyper-parameters on each target task. And second they typically underperform --sometimes dramatically-- standard zero-shot predictions in the presence of distributional drifts. Motivated by the unrealistic assumptions made in the existing literature i.e. access to a large validation set and case-specific grid-search for optimal hyperparameters we propose a novel approach that meets the requirements of real-world scenarios. More concretely we introduce a CLass-Adaptive linear Probe (CLAP) objective whose balancing term is optimized via an adaptation of the general Augmented Lagrangian method tailored to this context. We comprehensively evaluate CLAP on a broad span of datasets and scenarios demonstrating that it consistently outperforms SoTA approaches while yet being a much more efficient alternative.