WLST: Weak Labels Guided Self-training for Weakly-supervised Domain Adaptation on 3D Object Detection

In the field of domain adaptation (DA) on 3D object detection, most of the work is dedicated to unsupervised domain adaptation (UDA). Yet, without any target annotations, the performance gap between the UDA approaches and the fully-supervised approach is still noticeable, which is impractical for real-world applications. On the other hand, weakly-supervised domain adaptation (WDA) is an underexplored yet practical task that only requires few labeling effort on the target domain. To improve the DA performance in a cost-effective way, we propose a general weak labels guided self-training framework, WLST, designed for WDA on 3D object detection. By incorporating autolabeler, which can generate 3D pseudo labels from 2D bounding boxes, into the existing self-training pipeline, our method is able to generate more robust and consistent pseudo labels that would benefit the training process on the target domain. Extensive experiments demonstrate the effectiveness, robustness, and detector-agnosticism of our WLST framework. Notably, it outperforms previous state-of-the-art methods on all evaluation tasks

Fair Robust Active Learning by Joint Inconsistency

Fair Active Learning (FAL) utilized active learning techniques to achieve high model performance with limited data and to reach fairness between sensitive groups (e.g., genders). However, the impact of the adversarial attack, which is vital for various safety-critical machine learning applications, is not yet addressed in FAL. Observing this, we introduce a novel task, Fair Robust Active Learning (FRAL), integrating conventional FAL and adversarial robustness. FRAL requires ML models to leverage active learning techniques to jointly achieve equalized performance on benign data and equalized robustness against adversarial attacks between groups. In this new task, previous FAL methods generally face the problem of unbearable computational burden and ineffectiveness. Therefore, we develop a simple yet effective FRAL strategy by Joint INconsistency (JIN). To efficiently find samples that can boost the performance and robustness of disadvantaged groups for labeling, our method exploits the prediction inconsistency between benign and adversarial samples as well as between standard and robust models. Extensive experiments under diverse datasets and sensitive groups demonstrate that our method not only achieves fairer performance on benign samples but also obtains fairer robustness under white-box PGD attacks compared with existing active learning and FAL baselines. We are optimistic that FRAL would pave a new path for developing safe and robust ML research and applications such as facial attribute recognition in biometrics systems.Comment: 11 pages, 3 figure

Regulation of CLC-1 chloride channel biosynthesis by FKBP8 and Hsp90β.

Mutations in human CLC-1 chloride channel are associated with the skeletal muscle disorder myotonia congenita. The disease-causing mutant A531V manifests enhanced proteasomal degradation of CLC-1. We recently found that CLC-1 degradation is mediated by cullin 4 ubiquitin ligase complex. It is currently unclear how quality control and protein degradation systems coordinate with each other to process the biosynthesis of CLC-1. Herein we aim to ascertain the molecular nature of the protein quality control system for CLC-1. We identified three CLC-1-interacting proteins that are well-known heat shock protein 90 (Hsp90)-associated co-chaperones: FK506-binding protein 8 (FKBP8), activator of Hsp90 ATPase homolog 1 (Aha1), and Hsp70/Hsp90 organizing protein (HOP). These co-chaperones promote both the protein level and the functional expression of CLC-1 wild-type and A531V mutant. CLC-1 biosynthesis is also facilitated by the molecular chaperones Hsc70 and Hsp90β. The protein stability of CLC-1 is notably increased by FKBP8 and the Hsp90β inhibitor 17-allylamino-17-demethoxygeldanamycin (17-AAG) that substantially suppresses cullin 4 expression. We further confirmed that cullin 4 may interact with Hsp90β and FKBP8. Our data are consistent with the idea that FKBP8 and Hsp90β play an essential role in the late phase of CLC-1 quality control by dynamically coordinating protein folding and degradation

Indoor Depth Completion with Boundary Consistency and Self-Attention

Depth estimation features are helpful for 3D recognition. Commodity-grade depth cameras are able to capture depth and color image in real-time. However, glossy, transparent or distant surface cannot be scanned properly by the sensor. As a result, enhancement and restoration from sensing depth is an important task. Depth completion aims at filling the holes that sensors fail to detect, which is still a complex task for machine to learn. Traditional hand-tuned methods have reached their limits, while neural network based methods tend to copy and interpolate the output from surrounding depth values. This leads to blurred boundaries, and structures of the depth map are lost. Consequently, our main work is to design an end-to-end network improving completion depth maps while maintaining edge clarity. We utilize self-attention mechanism, previously used in image inpainting fields, to extract more useful information in each layer of convolution so that the complete depth map is enhanced. In addition, we propose boundary consistency concept to enhance the depth map quality and structure. Experimental results validate the effectiveness of our self-attention and boundary consistency schema, which outperforms previous state-of-the-art depth completion work on Matterport3D dataset. Our code is publicly available at https://github.com/patrickwu2/Depth-CompletionComment: Accepted by ICCVW (RLQ) 201

Self-correcting LLM-controlled Diffusion Models

Text-to-image generation has witnessed significant progress with the advent of diffusion models. Despite the ability to generate photorealistic images, current text-to-image diffusion models still often struggle to accurately interpret and follow complex input text prompts. In contrast to existing models that aim to generate images only with their best effort, we introduce Self-correcting LLM-controlled Diffusion (SLD). SLD is a framework that generates an image from the input prompt, assesses its alignment with the prompt, and performs self-corrections on the inaccuracies in the generated image. Steered by an LLM controller, SLD turns text-to-image generation into an iterative closed-loop process, ensuring correctness in the resulting image. SLD is not only training-free but can also be seamlessly integrated with diffusion models behind API access, such as DALL-E 3, to further boost the performance of state-of-the-art diffusion models. Experimental results show that our approach can rectify a majority of incorrect generations, particularly in generative numeracy, attribute binding, and spatial relationships. Furthermore, by simply adjusting the instructions to the LLM, SLD can perform image editing tasks, bridging the gap between text-to-image generation and image editing pipelines. We will make our code available for future research and applications.Comment: 16 pages, 10 figure

Flowtable-Free Routing for Data Center Networks: A Software-Defined Approach

The paradigm shift toward SDN has exhibited the following trends: (1) relying on a centralized and more powerful controller to make intelligent decisions, and (2) allowing a set of relatively dumb switches to route packets. Therefore, efficiently looking up the flowtables in forwarding switches to guarantee low latency becomes a critical issue. In this paper, following the similar paradigm, we propose a new routing scheme called KeySet which is flowtable-free and enables constant-time switching at the forwarding switches. Instead of looking up long flowtables, KeySet relies on a residual system to quickly calculate routing paths. A switch only needs to do simple modular arithmetics to obtain a packet's forwarding output port. Moreover, KeySet has a nice fault- tolerant capability because in many cases the controller does not need to update flowtables at switches when a failure occurs. We validate KeySet through extensive simulations by using general as well as Facebook fat-tree topologies. The results show that the KeySet outperforms the KeyFlow scheme [1] by at least 25% in terms of the length of the forwarding label. Moreover, we show that KeySet is very efficient when applied to fat-trees