Dan Zhang

Dan Zhang, Tao Feng, Lilong Xue et al.

This survey delves into the realm of Parameter-Efficient Fine-Tuning (PEFT) within the context of Foundation Models (FMs). PEFT, a cost-effective fine-tuning technique, minimizes parameters and computational complexity while striving for optimal downstream task performance. FMs, like ChatGPT, DALL-E, and LLaVA specialize in language understanding, generative tasks, and multimodal tasks, trained on diverse datasets spanning text, images, and videos. The diversity of FMs guides various adaptation strategies for PEFT. Therefore, this survey aims to provide a comprehensive overview of PEFT techniques applied to diverse FMs and address critical gaps in understanding the techniques, trends, and applications. We start by providing a detailed development of FMs and PEFT. Subsequently, we systematically review the key categories and core mechanisms of PEFT across diverse FMs to offer a comprehensive understanding of trends. We also explore the most recent applications across various FMs to demonstrate the versatility of PEFT, shedding light on the integration of systematic PEFT methods with a range of FMs. Furthermore, we identify potential research and development directions for improving PEFTs in the future. This survey provides a valuable resource for both newcomers and experts seeking to understand and use the power of PEFT across FMs. All reviewed papers are listed at \url{https://github.com/THUDM/Awesome-Parameter-Efficient-Fine-Tuning-for-Foundation-Models}.

Peng Cui, Guande He, Dan Zhang et al.

Datasets collected from the open world unavoidably suffer from various forms of randomness or noiseness, leading to the ubiquity of aleatoric (data) uncertainty. Quantifying such uncertainty is particularly pivotal for object detection, where images contain multi-scale objects with occlusion, obscureness, and even noisy annotations, in contrast to images with centric and similar-scale objects in classification. This paper suggests modeling and exploiting the uncertainty inherent in object detection data with vision foundation models and develops a data-centric reliable training paradigm. Technically, we propose to estimate the data uncertainty of each object instance based on the feature space of vision foundation models, which are trained on ultra-large-scale datasets and able to exhibit universal data representation. In particular, we assume a mixture-of-Gaussian structure of the object features and devise Mahalanobis distance-based measures to quantify the data uncertainty. Furthermore, we suggest two curial and practical usages of the estimated uncertainty: 1) for defining uncertainty-aware sample filter to abandon noisy and redundant instances to avoid over-fitting, and 2) for defining sample adaptive regularizer to balance easy/hard samples for adaptive training. The estimated aleatoric uncertainty serves as an extra level of annotations of the dataset, so it can be utilized in a plug-and-play manner with any model. Extensive empirical studies verify the effectiveness of the proposed aleatoric uncertainty measure on various advanced detection models and challenging benchmarks.

Lars Schmarje, Kaspar Sakman, Reinhard Koch et al.

Autonomous driving (AD) operates in open-world scenarios, where encountering unknown objects is inevitable. However, standard object detectors trained on a limited number of base classes tend to ignore any unknown objects, posing potential risks on the road. To address this, it is important to learn a generic rather than a class specific objectness from objects seen during training. We therefore introduce an occupancy prediction together with bounding box regression. It learns to score the objectness by calculating the ratio of the predicted area occupied by actual objects. To enhance its generalizability, we increase the object diversity by exploiting data from other domains via Mosaic and Mixup augmentation. The objects outside the AD training classes are classified as a newly added out-of-distribution (OOD) class. Our solution UNCOVER, for UNknown Class Object detection for autonomous VEhicles in Real-time, excels at achieving both real-time detection and high recall of unknown objects on challenging AD benchmarks. To further attain very low false positive rates, particularly for close objects, we introduce a post-hoc filtering step that utilizes geometric cues extracted from the depth map, typically available within the AD system.