Taming Pre-trained LLMs for Generalised Time Series Forecasting via Cross-modal Knowledge Distillation

Multivariate time series forecasting has recently gained great success with the rapid growth of deep learning models. However, existing approaches usually train models from scratch using limited temporal data, preventing their generalization. Recently, with the surge of the Large Language Models (LLMs), several works have attempted to introduce LLMs into time series forecasting. Despite promising results, these methods directly take time series as the input to LLMs, ignoring the inherent modality gap between temporal and text data. In this work, we propose a novel Large Language Models and time series alignment framework, dubbed LLaTA, to fully unleash the potentials of LLMs in the time series forecasting challenge. Based on cross-modal knowledge distillation, the proposed method exploits both input-agnostic static knowledge and input-dependent dynamic knowledge in pre-trained LLMs. In this way, it empowers the forecasting model with favorable performance as well as strong generalization abilities. Extensive experiments demonstrate the proposed method establishes a new state of the art for both long- and short-term forecasting. Code is available at \url{https://github.com/Hank0626/LLaTA}.

MambaIR: A Simple Baseline for Image Restoration with State-Space Model

Recent years have witnessed great progress in image restoration thanks to the advancements in modern deep neural networks e.g. Convolutional Neural Network and Transformer. However, existing restoration backbones are usually limited due to the inherent local reductive bias or quadratic computational complexity. Recently, Selective Structured State Space Model e.g., Mamba, has shown great potential for long-range dependencies modeling with linear complexity, but it is still under-explored in low-level computer vision. In this work, we introduce a simple but strong benchmark model, named MambaIR, for image restoration. In detail, we propose the Residual State Space Block as the core component, which employs convolution and channel attention to enhance the capabilities of the vanilla Mamba. In this way, our MambaIR takes advantage of local patch recurrence prior as well as channel interaction to produce restoration-specific feature representation. Extensive experiments demonstrate the superiority of our method, for example, MambaIR outperforms Transformer-based baseline SwinIR by up to 0.36dB, using similar computational cost but with a global receptive field. Code is available at \url{https://github.com/csguoh/MambaIR}.

AdaptIR: Parameter Efficient Multi-task Adaptation for Pre-trained Image Restoration Models

Pre-training has shown promising results on various image restoration tasks, which is usually followed by full fine-tuning for each specific downstream task (e.g., image denoising). However, such full fine-tuning usually suffers from the problems of heavy computational cost in practice, due to the massive parameters of pre-trained restoration models, thus limiting its real-world applications. Recently, Parameter Efficient Transfer Learning (PETL) offers an efficient alternative solution to full fine-tuning, yet still faces great challenges for pre-trained image restoration models, due to the diversity of different degradations. To address these issues, we propose AdaptIR, a novel parameter efficient transfer learning method for adapting pre-trained restoration models. Specifically, the proposed method consists of a multi-branch inception structure to orthogonally capture local spatial, global spatial, and channel interactions. In this way, it allows powerful representations under a very low parameter budget. Extensive experiments demonstrate that the proposed method can achieve comparable or even better performance than full fine-tuning, while only using 0.6% parameters. Code is available at https://github.com/csguoh/AdaptIR.

One-stage Low-resolution Text Recognition with High-resolution Knowledge Transfer

Recognizing characters from low-resolution (LR) text images poses a significant challenge due to the information deficiency as well as the noise and blur in low-quality images. Current solutions for low-resolution text recognition (LTR) typically rely on a two-stage pipeline that involves super-resolution as the first stage followed by the second-stage recognition. Although this pipeline is straightforward and intuitive, it has to use an additional super-resolution network, which causes inefficiencies during training and testing. Moreover, the recognition accuracy of the second stage heavily depends on the reconstruction quality of the first stage, causing ineffectiveness. In this work, we attempt to address these challenges from a novel perspective: adapting the recognizer to low-resolution inputs by transferring the knowledge from the high-resolution. Guided by this idea, we propose an efficient and effective knowledge distillation framework to achieve multi-level knowledge transfer. Specifically, the visual focus loss is proposed to extract the character position knowledge with resolution gap reduction and character region focus, the semantic contrastive loss is employed to exploit the contextual semantic knowledge with contrastive learning, and the soft logits loss facilitates both local word-level and global sequence-level learning from the soft teacher label. Extensive experiments show that the proposed one-stage pipeline significantly outperforms super-resolution based two-stage frameworks in terms of effectiveness and efficiency, accompanied by favorable robustness. Code is available at https://github.com/csguoh/KD-LTR.

Towards Robust Scene Text Image Super-resolution via Explicit Location Enhancement

Scene text image super-resolution (STISR), aiming to improve image quality while boosting downstream scene text recognition accuracy, has recently achieved great success. However, most existing methods treat the foreground (character regions) and background (non-character regions) equally in the forward process, and neglect the disturbance from the complex background, thus limiting the performance. To address these issues, in this paper, we propose a novel method LEMMA that explicitly models character regions to produce high-level text-specific guidance for super-resolution. To model the location of characters effectively, we propose the location enhancement module to extract character region features based on the attention map sequence. Besides, we propose the multi-modal alignment module to perform bidirectional visual-semantic alignment to generate high-quality prior guidance, which is then incorporated into the super-resolution branch in an adaptive manner using the proposed adaptive fusion module. Experiments on TextZoom and four scene text recognition benchmarks demonstrate the superiority of our method over other state-of-the-art methods. Code is available at https://github.com/csguoh/LEMMA.

MGTR: End-to-End Mutual Gaze Detection with Transformer

People's looking at each other or mutual gaze is ubiquitous in our daily interactions, and detecting mutual gaze is of great significance for understanding human social scenes. Current mutual gaze detection methods focus on two-stage methods, whose inference speed is limited by the two-stage pipeline and the performance in the second stage is affected by the first one. In this paper, we propose a novel one-stage mutual gaze detection framework called Mutual Gaze TRansformer or MGTR to perform mutual gaze detection in an end-to-end manner. By designing mutual gaze instance triples, MGTR can detect each human head bounding box and simultaneously infer mutual gaze relationship based on global image information, which streamlines the whole process with simplicity. Experimental results on two mutual gaze datasets show that our method is able to accelerate mutual gaze detection process without losing performance. Ablation study shows that different components of MGTR can capture different levels of semantic information in images. Code is available at https://github.com/Gmbition/MGTR

Peek Inside the Closed World: Evaluating Autoencoder-Based Detection of DDoS to Cloud

Machine-learning-based anomaly detection (ML-based AD) has been successful at detecting DDoS events in the lab. However published evaluations of ML-based AD have only had limited data and have not provided insight into why it works. To address limited evaluation against real-world data, we apply autoencoder, an existing ML-AD model, to 57 DDoS attack events captured at 5 cloud IPs from a major cloud provider. To improve our understanding for why ML-based AD works or not works, we interpret this data with feature attribution and counterfactual explanation. We show that our version of autoencoders work well overall: our models capture nearly all malicious flows to 2 of the 4 cloud IPs under attacks (at least 99.99%) but generate a few false negatives (5% and 9%) for the remaining 2 IPs. We show that our models maintain near-zero false positives on benign flows to all 5 IPs. Our interpretation of results shows that our models identify almost all malicious flows with non-whitelisted (non-WL) destination ports (99.92%) by learning the full list of benign destination ports from training data (the normality). Interpretation shows that although our models learn incomplete normality for protocols and source ports, they still identify most malicious flows with non-WL protocols and blacklisted (BL) source ports (100.0% and 97.5%) but risk false positives. Interpretation also shows that our models only detect a few malicious flows with BL packet sizes (8.5%) by incorrectly inferring these BL sizes as normal based on incomplete normality learned. We find our models still detect a quarter of flows (24.7%) with abnormal payload contents even when they do not see payload by combining anomalies from multiple flow features. Lastly, we summarize the implications of what we learn on applying autoencoder-based AD in production.