CVPR 2024 Papers

Few-shot semantic segmentation (FSS) endeavors to segment unseen classes with only a few labeled samples. Current FSS methods are commonly built on the assumption that their training and application scenarios share similar domains and their performances degrade significantly while applied to a distinct domain. To this end we propose to leverage the cutting-edge foundation model the Segment Anything Model (SAM) for generalization enhancement. The SAM however performs unsatisfactorily on domains that are distinct from its training data which primarily comprise natural scene images and it does not support automatic segmentation of specific semantics due to its interactive prompting mechanism. In our work we introduce APSeg a novel auto-prompt network for cross-domain few-shot semantic segmentation (CD-FSS) which is designed to be auto-prompted for guiding cross-domain segmentation. Specifically we propose a Dual Prototype Anchor Transformation (DPAT) module that fuses pseudo query prototypes extracted based on cycle-consistency with support prototypes allowing features to be transformed into a more stable domain-agnostic space. Additionally a Meta Prompt (MPG) module is introduced to automatically generate prompt embeddings eliminating the need for manual visual prompts. We build an efficient model which can be applied directly to target domains without fine-tuning. Extensive experiments on four cross-domain datasets show that our model outperforms the state-of-the-art CD-FSS method by 5.24% and 3.10% in average accuracy on 1-shot and 5-shot settings respectively.

Test-time adaptation (TTA) has emerged as a viable solution to adapt pre-trained models to domain shifts using unlabeled test data. However TTA faces challenges of adaptation failures due to its reliance on blind adaptation to unknown test samples in dynamic scenarios. Traditional methods for out-of-distribution performance estimation are limited by unrealistic assumptions in the TTA context such as requiring labeled data or re-training models. To address this issue we propose AETTA a label-free accuracy estimation algorithm for TTA. We propose the prediction disagreement as the accuracy estimate calculated by comparing the target model prediction with dropout inferences. We then improve the prediction disagreement to extend the applicability of AETTA under adaptation failures. Our extensive evaluation with four baselines and six TTA methods demonstrates that AETTA shows an average of 19.8%p more accurate estimation compared with the baselines. We further demonstrate the effectiveness of accuracy estimation with a model recovery case study showcasing the practicality of our model recovery based on accuracy estimation. The source code is available at https://github.com/taeckyung/AETTA.

Deep Neural Networks (DNNs) have become pivotal in various fields especially in computer vision outperforming previous methodologies. A critical challenge in their deployment is the bias inherent in data across different domains such as image style and environmental conditions leading to domain gaps. This necessitates techniques for learning general representations from biased training data known as domain generalization. This paper presents Attend to eXpert Prompts (A2XP) a novel approach for domain generalization that preserves the privacy and integrity of the network architecture. A2XP consists of two phases: Expert Adaptation and Domain Generalization. In the first phase prompts for each source domain are optimized to guide the model towards the optimal direction. In the second phase two embedder networks are trained to effectively amalgamate these expert prompts aiming for an optimal output. Our extensive experiments demonstrate that A2XP achieves state-of-the-art results over existing non-private domain generalization methods. The experimental results validate that the proposed approach not only tackles the domain generalization challenge in DNNs but also offers a privacy-preserving efficient solution to the broader field of computer vision.

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.