Less is More: Fewer Interpretable Region via Submodular Subset Selection

Image attribution algorithms aim to identify important regions that are highly relevant to model decisions. Although existing attribution solutions can effectively assign importance to target elements, they still face the following challenges: 1) existing attribution methods generate inaccurate small regions thus misleading the direction of correct attribution, and 2) the model cannot produce good attribution results for samples with wrong predictions. To address the above challenges, this paper re-models the above image attribution problem as a submodular subset selection problem, aiming to enhance model interpretability using fewer regions. To address the lack of attention to local regions, we construct a novel submodular function to discover more accurate small interpretation regions. To enhance the attribution effect for all samples, we also impose four different constraints on the selection of sub-regions, i.e., confidence, effectiveness, consistency, and collaboration scores, to assess the importance of various subsets. Moreover, our theoretical analysis substantiates that the proposed function is in fact submodular. Extensive experiments show that the proposed method outperforms SOTA methods on two face datasets (Celeb-A and VGG-Face2) and one fine-grained dataset (CUB-200-2011). For correctly predicted samples, the proposed method improves the Deletion and Insertion scores with an average of 4.9% and 2.5% gain relative to HSIC-Attribution. For incorrectly predicted samples, our method achieves gains of 81.0% and 18.4% compared to the HSIC-Attribution algorithm in the average highest confidence and Insertion score respectively. The code is released at https://github.com/RuoyuChen10/SMDL-Attribution.

Lightweight Pixel Difference Networks for Efficient Visual Representation Learning

Recently, there have been tremendous efforts in developing lightweight Deep Neural Networks (DNNs) with satisfactory accuracy, which can enable the ubiquitous deployment of DNNs in edge devices. The core challenge of developing compact and efficient DNNs lies in how to balance the competing goals of achieving high accuracy and high efficiency. In this paper we propose two novel types of convolutions, dubbed \emph{Pixel Difference Convolution (PDC) and Binary PDC (Bi-PDC)} which enjoy the following benefits: capturing higher-order local differential information, computationally efficient, and able to be integrated with existing DNNs. With PDC and Bi-PDC, we further present two lightweight deep networks named \emph{Pixel Difference Networks (PiDiNet)} and \emph{Binary PiDiNet (Bi-PiDiNet)} respectively to learn highly efficient yet more accurate representations for visual tasks including edge detection and object recognition. Extensive experiments on popular datasets (BSDS500, ImageNet, LFW, YTF, \emph{etc.}) show that PiDiNet and Bi-PiDiNet achieve the best accuracy-efficiency trade-off. For edge detection, PiDiNet is the first network that can be trained without ImageNet, and can achieve the human-level performance on BSDS500 at 100 FPS and with $<$1M parameters. For object recognition, among existing Binary DNNs, Bi-PiDiNet achieves the best accuracy and a nearly $2\times$ reduction of computational cost on ResNet18. Code available at \href{https://github.com/hellozhuo/pidinet}{https://github.com/hellozhuo/pidinet}.

Fair Text-to-Image Diffusion via Fair Mapping

In this paper, we address the limitations of existing text-to-image diffusion models in generating demographically fair results when given human-related descriptions. These models often struggle to disentangle the target language context from sociocultural biases, resulting in biased image generation. To overcome this challenge, we propose Fair Mapping, a general, model-agnostic, and lightweight approach that modifies a pre-trained text-to-image model by controlling the prompt to achieve fair image generation. One key advantage of our approach is its high efficiency. The training process only requires updating a small number of parameters in an additional linear mapping network. This not only reduces the computational cost but also accelerates the optimization process. We first demonstrate the issue of bias in generated results caused by language biases in text-guided diffusion models. By developing a mapping network that projects language embeddings into an unbiased space, we enable the generation of relatively balanced demographic results based on a keyword specified in the prompt. With comprehensive experiments on face image generation, we show that our method significantly improves image generation performance when prompted with descriptions related to human faces. By effectively addressing the issue of bias, we produce more fair and diverse image outputs. This work contributes to the field of text-to-image generation by enhancing the ability to generate images that accurately reflect the intended demographic characteristics specified in the text.

Seeing is not Believing: An Identity Hider for Human Vision Privacy Protection

Massive captured face images are stored in the database for the identification of individuals. However, the stored images can be observed intentionally or unintentionally by data managers, which is not at the will of individuals and may cause privacy violations. Existing protection works only slightly change the visual content of the face while maintaining the utility of identification, making it susceptible to the inference of the true identity by human vision. In this paper, we propose an identity hider that enables significant visual content change for human vision while preserving high identifiability for face recognizers. Firstly, the identity hider generates a virtual face with new visual content by manipulating the latent space in StyleGAN2. In particular, the virtual face has the same irrelevant attributes as the original face, e.g., pose and expression. Secondly, the visual content of the virtual face is transferred into the original face and then the background is replaced with the original one. In addition, the identity hider has strong transferability, which ensures an arbitrary face recognizer can achieve satisfactory accuracy. Adequate experiments show that the proposed identity hider achieves excellent performance on privacy protection and identifiability preservation.

A GOA-Based Fault-Tolerant Trajectory Tracking Control for an Underwater Vehicle of Multi-Thruster System without Actuator Saturation

This paper proposes an intelligent fault-tolerant control (FTC) strategy to tackle the trajectory tracking problem of an underwater vehicle (UV) under thruster damage (power loss) cases and meanwhile resolve the actuator saturation brought by the vehicle's physical constraints. In the proposed control strategy, the trajectory tracking component is formed by a refined backstepping algorithm that controls the velocity variation and a sliding mode control deducts the torque/force outputs; the fault-tolerant component is established based on a Grasshopper Optimization Algorithm (GOA), which provides fast convergence speed as well as satisfactory accuracy of deducting optimized reallocation of the thruster forces to compensate for the power loss in different fault cases. Simulations with or without environmental perturbations under different fault cases and comparisons to other traditional FTCs are presented, thus verifying the effectiveness and robustness of the proposed GOA-based fault-tolerant trajectory tracking design.

Towards Few-Shot Open-Set Object Detection

Open-set object detection (OSOD) aims to detect the known categories and identify unknown objects in a dynamic world, which has achieved significant attentions. However, previous approaches only consider this problem in data-abundant conditions. We seek a solution for few-shot open-set object detection (FSOSOD), which aims to quickly train a detector based on few samples while detecting all known classes and identifying unknown classes. The main challenge for this task is that few training samples tend to overfit on the known classes, and lead to poor open-set performance. We propose a new FSOSOD algorithm to tackle this issue, named FOOD, which contains a novel class dropout cosine classifier (CDCC) and a novel unknown decoupling learner (UDL). To prevent over-fitting, CDCC randomly inactivates parts of the normalized neurons for the logit prediction of all classes, and then decreases the co-adaptability between the class and its neighbors. Alongside, UDL decouples training the unknown class and enables the model to form a compact unknown decision boundary. Thus, the unknown objects can be identified with a confidence probability without any pseudo-unknown samples for training. We compare our method with several state-of-the-art OSOD methods in few-shot scenes and observe that our method improves the recall of unknown classes by 5%-9% across all shots in VOC-COCO dataset setting.

Collaborative Radio SLAM for Multiple Robots based on WiFi Fingerprint Similarity

Simultaneous Localization and Mapping (SLAM) enables autonomous robots to navigate and execute their tasks through unknown environments. However, performing SLAM in large environments with a single robot is not efficient, and visual or LiDAR-based SLAM requires feature extraction and matching algorithms, which are computationally expensive. In this paper, we present a collaborative SLAM approach with multiple robots using the pervasive WiFi radio signals. A centralized solution is proposed to optimize the trajectory based on the odometry and radio fingerprints collected from multiple robots. To improve the localization accuracy, a novel similarity model is introduced that combines received signal strength (RSS) and detection likelihood of an access point (AP). We perform extensive experiments to demonstrate the effectiveness of the proposed similarity model and collaborative SLAM framework.