Minimalist AdaBoost for blemish identification in potatoes

We present a multi-class solution based on minimalist Ad- aBoost for identifying blemishes present in visual images of potatoes. Using training examples we use Real AdaBoost to rst reduce the fea- ture set by selecting ve features for each class, then train binary clas- siers for each class, classifying each testing example according to the binary classier with the highest certainty. Against hand-drawn ground truth data we achieve a pixel match of 83% accuracy in white potatoes and 82% in red potatoes. For the task of identifying which blemishes are present in each potato within typical industry dened criteria (10% coverage) we achieve accuracy rates of 93% and 94%, respectively

Towards Omni-supervised Referring Expression Segmentation

Referring Expression Segmentation (RES) is an emerging task in computer vision, which segments the target instances in images based on text descriptions. However, its development is plagued by the expensive segmentation labels. To address this issue, we propose a new learning task for RES called Omni-supervised Referring Expression Segmentation (Omni-RES), which aims to make full use of unlabeled, fully labeled and weakly labeled data, e.g., referring points or grounding boxes, for efficient RES training. To accomplish this task, we also propose a novel yet strong baseline method for Omni-RES based on the recently popular teacher-student learning, where the weak labels are not directly transformed into supervision signals but used as a yardstick to select and refine high-quality pseudo-masks for teacher-student learning. To validate the proposed Omni-RES method, we apply it to a set of state-of-the-art RES models and conduct extensive experiments on a bunch of RES datasets. The experimental results yield the obvious merits of Omni-RES than the fully-supervised and semi-supervised training schemes. For instance, with only 10% fully labeled data, Omni-RES can help the base model achieve 100% fully supervised performance, and it also outperform the semi-supervised alternative by a large margin, e.g., +14.93% on RefCOCO and +14.95% on RefCOCO+, respectively. More importantly, Omni-RES also enable the use of large-scale vision-langauges like Visual Genome to facilitate low-cost RES training, and achieve new SOTA performance of RES, e.g., 80.66 on RefCOCO

Towards Efficient Visual Adaption via Structural Re-parameterization

Parameter-efficient transfer learning (PETL) is an emerging research spot aimed at inexpensively adapting large-scale pre-trained models to downstream tasks. Recent advances have achieved great success in saving storage costs for various vision tasks by updating or injecting a small number of parameters instead of full fine-tuning. However, we notice that most existing PETL methods still incur non-negligible latency during inference. In this paper, we propose a parameter-efficient and computationally friendly adapter for giant vision models, called RepAdapter. Specifically, we prove that the adaption modules, even with a complex structure, can be seamlessly integrated into most giant vision models via structural re-parameterization. This property makes RepAdapter zero-cost during inference. In addition to computation efficiency, RepAdapter is more effective and lightweight than existing PETL methods due to its sparse structure and our careful deployment. To validate RepAdapter, we conduct extensive experiments on 27 benchmark datasets of three vision tasks, i.e., image and video classifications and semantic segmentation. Experimental results show the superior performance and efficiency of RepAdapter than the state-of-the-art PETL methods. For instance, by updating only 0.6% parameters, we can improve the performance of ViT from 38.8 to 55.1 on Sun397. Its generalizability is also well validated by a bunch of vision models, i.e., ViT, CLIP, Swin-Transformer and ConvNeXt. Our source code is released at https://github.com/luogen1996/RepAdapter

Approximated Prompt Tuning for Vision-Language Pre-trained Models

Prompt tuning is a parameter-efficient way to deploy large-scale pre-trained models to downstream tasks by adding task-specific tokens. In terms of vision-language pre-trained (VLP) models, prompt tuning often requires a large number of learnable tokens to bridge the gap between the pre-training and downstream tasks, which greatly exacerbates the already high computational overhead. In this paper, we revisit the principle of prompt tuning for Transformer-based VLP models and reveal that the impact of soft prompt tokens can be actually approximated via independent information diffusion steps, thereby avoiding the expensive global attention modeling and reducing the computational complexity to a large extent. Based on this finding, we propose a novel Approximated Prompt Tuning (APT) approach towards efficient VL transfer learning. To validate APT, we apply it to two representative VLP models, namely ViLT and METER, and conduct extensive experiments on a bunch of downstream tasks. Meanwhile, the generalization of APT is also validated on CLIP for image classification. The experimental results not only show the superior performance gains and computation efficiency of APT against the conventional prompt tuning methods, e.g., +6.6% accuracy and -64.62% additional computation overhead on METER, but also confirm its merits over other parameter-efficient transfer learning approaches

Multilayer Photonic Crystal for Spectral Narrowing of Emission

Multilayer colloidal crystal has been prepared by the layer-by-layer deposition of silica microspheres on a glass slide. Each layer is a slab consisting of a fcc close-packed colloidal arrays. By properly choosing the sizes of spheres, the whole spectral feature of multilayer colloidal crystal can be tuned. Here, we engineered a multilayer superlattice structure with an effective passband (380 nm) between two stop bands (366 nm and 400 nm). This gives a strong narrowing effect on emission spectrum (378 nm). With the stop bands at the shortwave and longwave edges of emission spectrum, the passband in the central wavelength region can be regarded as a strong decrease of suppression effect and enhancement of a narrow wavelength region of emission. The FWHM values of stop band modified emission spectrum were narrowed from 59 nm to 22 nm. The spectral narrowing modification effect of suitably engineered colloidal crystals shows up their importance in potential application as optical filters and lasing devices

DDX5 facilitates HIV-1 replication as a cellular co-factor of Rev.

HIV-1 Rev plays an important role in the late phase of HIV-1 replication, which facilitates export of unspliced viral mRNAs from the nucleus to cytoplasm in infected cells. Recent studies have shown that DDX1 and DDX3 are co-factors of Rev for the export of HIV-1 transcripts. In this report, we have demonstrated that DDX5 (p68), which is a multifunctional DEAD-box RNA helicase, functions as a new cellular co-factor of HIV-1 Rev. We found that DDX5 affects Rev function through the Rev-RRE axis and subsequently enhances HIV-1 replication. Confocal microscopy and co-immunoprecipitation analysis indicated that DDX5 binds to Rev and this interaction is largely dependent on RNA. If the DEAD-box motif of DDX5 is mutated, DDX5 loses almost all of its ability to bind to Rev, indicating that the DEAD-box motif of DDX5 is required for the interaction between DDX5 and Rev. Our data indicate that interference of DDX5-Rev interaction could reduce HIV-1 replication and potentially provide a new molecular target for anti-HIV-1 therapeutics