Improving Anomaly Segmentation with Multi-Granularity Cross-Domain Alignment

Anomaly segmentation plays a crucial role in identifying anomalous objects within images, which facilitates the detection of road anomalies for autonomous driving. Although existing methods have shown impressive results in anomaly segmentation using synthetic training data, the domain discrepancies between synthetic training data and real test data are often neglected. To address this issue, the Multi-Granularity Cross-Domain Alignment (MGCDA) framework is proposed for anomaly segmentation in complex driving environments. It uniquely combines a new Multi-source Domain Adversarial Training (MDAT) module and a novel Cross-domain Anomaly-aware Contrastive Learning (CACL) method to boost the generality of the model, seamlessly integrating multi-domain data at both scene and sample levels. Multi-source domain adversarial loss and a dynamic label smoothing strategy are integrated into the MDAT module to facilitate the acquisition of domain-invariant features at the scene level, through adversarial training across multiple stages. CACL aligns sample-level representations with contrastive loss on cross-domain data, which utilizes an anomaly-aware sampling strategy to efficiently sample hard samples and anchors. The proposed framework has decent properties of parameter-free during the inference stage and is compatible with other anomaly segmentation networks. Experimental conducted on Fishyscapes and RoadAnomaly datasets demonstrate that the proposed framework achieves state-of-the-art performance.Comment: Accepted to ACM Multimedia 202

Incorporating Visual Experts to Resolve the Information Loss in Multimodal Large Language Models

Multimodal Large Language Models (MLLMs) are experiencing rapid growth, yielding a plethora of noteworthy contributions in recent months. The prevailing trend involves adopting data-driven methodologies, wherein diverse instruction-following datasets are collected. However, a prevailing challenge persists in these approaches, specifically in relation to the limited visual perception ability, as CLIP-like encoders employed for extracting visual information from inputs. Though these encoders are pre-trained on billions of image-text pairs, they still grapple with the information loss dilemma, given that textual captions only partially capture the contents depicted in images. To address this limitation, this paper proposes to improve the visual perception ability of MLLMs through a mixture-of-experts knowledge enhancement mechanism. Specifically, we introduce a novel method that incorporates multi-task encoders and visual tools into the existing MLLMs training and inference pipeline, aiming to provide a more comprehensive and accurate summarization of visual inputs. Extensive experiments have evaluated its effectiveness of advancing MLLMs, showcasing improved visual perception achieved through the integration of visual experts

Perspective on Reform of Death Sentence Review System (Chinese)

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Cellulase Recycling after High-Solids Simultaneous Saccharification and Fermentation of Combined Pretreated Corncob

Despite the advantageous prospect of second-generation bioethanol, its final commercialization must overcome the primary cost impediment due to enzyme assumption. To solve this problem, this work achieves high-concentration ethanol fermentation and multi-round cellulase recycling through process integration. The optimal time and temperature of the re-adsorption process were determined by monitoring the adsorption kinetics of cellulases. Both glucose and cellobiose inhibited cellulase adsorption. After 96 h of ethanol fermentation, 40% of the initial cellulase remained in the broth, from which 62.5% of the cellulase can be recycled and reused in fresh substrate re-adsorption for 90 min. Under optimum conditions, i.e., pH 5.0, dry matter loading of 15 wt%, cellulase loading of 45 FPU/g glucan, two cycles of fermentation and re-adsorption can yield two-fold increased ethanol outputs and reduce enzyme costs by over 50%. The ethanol concentration in each cycle can be achieved at levels greater than 40 g/L