Improving the safety of vision-language models like CLIP via fine-tuning often comes at a steep price, causing significant drops in their generalization performance. We find this trade-off stems from rigid alignment strategies that force unsafe concepts toward single, predefined safe targets, disrupting the model's learned semantic structure. To address this, we propose a proximity-aware approach: redirecting unsafe concepts to their semantically closest safe alternatives to minimize representational change. We introduce SafeR-CLIP, a fine-tuning framework that applies this principle of minimal intervention. SafeR-CLIP successfully reconciles safety and performance, recovering up to 8.0% in zero-shot accuracy over prior methods while maintaining robust safety. To support more rigorous evaluation, we also contribute NSFWCaps, a new benchmark of 1,000 highly-aligned pairs for testing safety under distributional shift. Our work shows that respecting the geometry of pretrained representations is key to achieving safety without sacrificing performance.

Recognizing and comprehending human actions and gestures is a crucial perception requirement for robots to interact with humans and carry out tasks in diverse domains, including service robotics, healthcare, and manufacturing. Event cameras, with their ability to capture fast-moving objects at a high temporal resolution, offer new opportunities compared to standard action recognition in RGB videos. However, previous research on event camera action recognition has primarily focused on sensor-specific network architectures and image encoding, which may not be suitable for new sensors and limit the use of recent advancement in transformer-based architectures. In this study, we employ using a computationally efficient model, namely the video transformer network (VTN), which initially acquires spatial embeddings per event-frame and then utilizes a temporal self-attention mechanism. This approach separates the spatial and temporal operations, resulting in VTN being more computationally efficient than other video transformers that process spatio-temporal volumes directly. In order to better adopt the VTN for the sparse and finegrained nature of event data, we design Event-Contrastive Loss$\left(\mathscr{L}_{E C}\right)$and event specific augmentations. Proposed$\left(\mathscr{L}_{E C}\right)$promotes learning fine-grained spatial cues in the spatial backbone of VTN by contrasting temporally misaligned frames. We evaluate our method on real-world action recognition of N-EPIC Kitchens dataset, and achieve state-of-the-art results on both protocols - testing in seen kitchen (74.9% accuracy) and testing in unseen kitchens (42.43% and 46.66% Accuracy). Our approach also takes less computation time compared to competitive prior approaches. We also evaluate our method on the standard DVS Gesture recognition dataset, achieving a competitive accuracy of 97.9% compared to prior work that uses dedicated architectures and image-encoding for the DVS dataset. These results demonstrate the potential of our framework EventTransAct for real-world applications of event-camera based action recognition. Project Page: https://tristandb8.github.io/EventTransAct_webpage/

Abstract Extensive research has been carried out in the past on face recognition, face detection, and age estimation. However, age‐invariant face recognition (AIFR) has not been explored that thoroughly. The facial appearance of a person changes considerably over time that results in introducing significant intraclass variations, which makes AIFR a very challenging task. Most of the face recognition studies that have addressed the ageing problem in the past have employed complex models and handcrafted features with strong parametric assumptions. In this work, we propose a novel deep learning framework that extracts age‐invariant and generalized features from facial images of the subjects. The proposed model trained on facial images from a minor part (20–30%) of lifespan of subjects correctly identifies them throughout their lifespan. A variety of pretrained 2D convolutional neural networks are compared in terms of accuracy, time, and computational complexity to select the most suitable network for AIFR. Extensive experimental results are carried out on the popular and challenging face and gesture recognition network ageing dataset. The proposed method achieves promising results and outperforms the state‐of‐the‐art AIFR models by achieving an accuracy of 99%, which proves the effectiveness of deep learning in facial ageing research.

Image stabilisation aims to compensate and smoothen the effects of undesired trembling motion of cameras mounted on non‐static platforms. It becomes quite a challenging task in the case of moving platforms, such as ground vehicles, unmanned aerial vehicles, and handheld devices. Many satisfactory solutions to the image stabilisation problem are proposed in the recent literature, but most of these methods are not adaptable for handling a wide range of intentional motions with minimum lag, especially in real‐time scenarios. In this study, the authors propose an online two‐dimensional image stabilisation technique based on dual motion vector integration, which is a novel adaptive motion smoothing technique that employs an average length of motion vectors to estimate the intentional motion. The overall computational cost of the proposed system is significantly reduced by employing frame‐shaking judgment that only allows processing of jittering frames. Promising experimental results have been obtained on challenging videos obtained from hand‐held and vehicle‐mounted cameras which demonstrate the robustness and effectiveness of the proposed technique against feature point mismatching and presence of moving objects within the scene at a frame rate of 30 frames per second.

Multispectral imaging allows for analysis of images in multiple spectral bands. Over the past three decades, airborne and satellite multispectral imaging have been the focus of extensive research in remote sensing. In the recent years, ground based multispectral imaging has gained an immense amount of interest in the fields ranging from computer vision and medical imaging to art, archaeology and computational forensics. The rich information content in multispectral images allows forensic experts to examine the chemical composition of forensic traces. Due to its rapid, non-contact and non-destructive characteristics, multispectral imaging is an effective tool for visualization, age estimation, detection and identification of forensic traces in document images. Ink mismatch is a key indicator of forgery in a document. Inks of different materials exhibit different spectral signature even if they have the same color. Multispectral analysis of questioned documents images allows identification and discrimination of visually similar inks. In this paper, an efficient automatic ink mismatch detection technique is proposed which uses Fuzzy C-Means Clustering to divide the spectral responses of ink pixels in handwritten notes into different clusters which relate to the unique inks used in the document. Sauvola's local thresholding technique is employed to efficiently segment foreground text from the document image. Furthermore, feature selection is used to optimize the performance of the proposed method. The presented method provides better ink discrimination results than state-of-the-art methods when tested on publicly available UWA Writing Inks Dataset.