Image Segmentation by Edge Partitioning over a Nonsubmodular Markov Random Field

Edge weight-based segmentation methods, such as normalized cut or minimum cut, require a partition number specification for their energy formulation. The number of partitions plays an important role in the segmentation overall quality. However, finding a suitable partition number is a nontrivial problem, and the numbers are ordinarily manually assigned. This is an aspect of the general partition problem, where finding the partition number is an important and difficult issue. In this paper, the edge weights instead of the pixels are partitioned to segment the images. By partitioning the edge weights into two disjoints sets, that is, cut and connect, an image can be partitioned into all possible disjointed segments. The proposed energy function is independent of the number of segments. The energy is minimized by iterating the QPBO-α-expansion algorithm over the pairwise Markov random field and the mean estimation of the cut and connected edges. Experiments using the Berkeley database show that the proposed segmentation method can obtain equivalently accurate segmentation results without designating the segmentation numbers

StreamMultiDiffusion: Real-Time Interactive Generation with Region-Based Semantic Control

The enormous success of diffusion models in text-to-image synthesis has made them promising candidates for the next generation of end-user applications for image generation and editing. Previous works have focused on improving the usability of diffusion models by reducing the inference time or increasing user interactivity by allowing new, fine-grained controls such as region-based text prompts. However, we empirically find that integrating both branches of works is nontrivial, limiting the potential of diffusion models. To solve this incompatibility, we present StreamMultiDiffusion, the first real-time region-based text-to-image generation framework. By stabilizing fast inference techniques and restructuring the model into a newly proposed multi-prompt stream batch architecture, we achieve $\times 10$ faster panorama generation than existing solutions, and the generation speed of 1.57 FPS in region-based text-to-image synthesis on a single RTX 2080 Ti GPU. Our solution opens up a new paradigm for interactive image generation named semantic palette, where high-quality images are generated in real-time from given multiple hand-drawn regions, encoding prescribed semantic meanings (e.g., eagle, girl). Our code and demo application are available at https://github.com/ironjr/StreamMultiDiffusion.Comment: 29 pages, 16 figures. v2: typos corrected, references added. Project page: https://jaerinlee.com/research/StreamMultiDiffusio

Extract-and-Adaptation Network for 3D Interacting Hand Mesh Recovery

Understanding how two hands interact with each other is a key component of accurate 3D interacting hand mesh recovery. However, recent Transformer-based methods struggle to learn the interaction between two hands as they directly utilize two hand features as input tokens, which results in distant token problem. The distant token problem represents that input tokens are in heterogeneous spaces, leading Transformer to fail in capturing correlation between input tokens. Previous Transformer-based methods suffer from the problem especially when poses of two hands are very different as they project features from a backbone to separate left and right hand-dedicated features. We present EANet, extract-and-adaptation network, with EABlock, the main component of our network. Rather than directly utilizing two hand features as input tokens, our EABlock utilizes two complementary types of novel tokens, SimToken and JoinToken, as input tokens. Our two novel tokens are from a combination of separated two hand features; hence, it is much more robust to the distant token problem. Using the two type of tokens, our EABlock effectively extracts interaction feature and adapts it to each hand. The proposed EANet achieves the state-of-the-art performance on 3D interacting hands benchmarks. The codes are available at https://github.com/jkpark0825/EANet.Comment: Accepted at ICCVW 202

Cyclic Test-Time Adaptation on Monocular Video for 3D Human Mesh Reconstruction

Despite recent advances in 3D human mesh reconstruction, domain gap between training and test data is still a major challenge. Several prior works tackle the domain gap problem via test-time adaptation that fine-tunes a network relying on 2D evidence (e.g., 2D human keypoints) from test images. However, the high reliance on 2D evidence during adaptation causes two major issues. First, 2D evidence induces depth ambiguity, preventing the learning of accurate 3D human geometry. Second, 2D evidence is noisy or partially non-existent during test time, and such imperfect 2D evidence leads to erroneous adaptation. To overcome the above issues, we introduce CycleAdapt, which cyclically adapts two networks: a human mesh reconstruction network (HMRNet) and a human motion denoising network (MDNet), given a test video. In our framework, to alleviate high reliance on 2D evidence, we fully supervise HMRNet with generated 3D supervision targets by MDNet. Our cyclic adaptation scheme progressively elaborates the 3D supervision targets, which compensate for imperfect 2D evidence. As a result, our CycleAdapt achieves state-of-the-art performance compared to previous test-time adaptation methods. The codes are available at https://github.com/hygenie1228/CycleAdapt_RELEASE.Comment: Published at ICCV 2023, 16 pages including the supplementary materia

Acquired, Bilateral Nevus of Ota-like Macules (ABNOM) Associated with Ota's Nevus: Case Report

Ota's nevus is mongolian spot-like macular blue-black or gray-brown patchy pigmentation that most commonly ocurrs in areas innervated by the first and second division of the trigeminal nerve. Acquired, bilateral nevus of Ota-like macules (ABNOM) is located bilaterally on the face, appears later in life, is blue-brown or slate-gray in color. It is not accompanied by macules on the ocular and mucosal membranes. There is also debate as to whether ABNOM is part of the Ota's nevus spectrum. We report an interesting case of ABNOM associated with Ota's nevus. A 36-yr-old Korean women visited our clinic with dark bluish patch on the right cheek and right conjunctiva since birth. She also had mottled brownish macules on both forehead and both lower eyelids that have developed 3 yr ago. Skin biopsy specimens taken from the right cheek and left forehead all showed scattered, bipolar or irregular melanocytes in the dermis. We diagnosed lesion on the right cheek area as Ota's nevus and those on both forehead and both lower eyelids as ABNOM by clinical and histologic findings. This case may support the view that ABNOM is a separate entity from bilateral Ota's nevus

Inkjet-Printed Silver CPW with Narrow Gap

Inkjet-printed silver coplanar waveguide on a glass substrate with narrow gap is firstly realized by using a selective surface treatment. The measured gap between signal and ground is 16.7 mm. Insertion loss is measured to be 2.04 dB/cm and 4.40 dB/cm at 10 GHz and 40 GHz, respectively

DNA methylation loss promotes immune evasion of tumours with high mutation and copy number load

Mitotic cell division increases tumour mutation burden and copy number load, predictive markers of the clinical benefit of immunotherapy. Cell division correlates also with genomic demethylation involving methylation loss in late-replicating partial methylation domains. Here we find that immunomodulatory pathway genes are concentrated in these domains and transcriptionally repressed in demethylated tumours with CpG island promoter hypermethylation. Global methylation loss correlated with immune evasion signatures independently of mutation burden and aneuploidy. Methylome data of our cohort (n = 60) and a published cohort (n = 81) in lung cancer and a melanoma cohort (n = 40) consistently demonstrated that genomic methylation alterations counteract the contribution of high mutation burden and increase immunotherapeutic resistance. Higher predictive power was observed for methylation loss than mutation burden. We also found that genomic hypomethylation correlates with the immune escape signatures of aneuploid tumours. Hence, DNA methylation alterations implicate epigenetic modulation in precision immunotherapy

A randomized, phase II study of gefitinib alone versus nimotuzumab plus gefitinib after platinum-based chemotherapy in advanced non-small cell lung cancer (KCSG LU12-01)

We aimed to evaluate the efficacy of dual inhibition of epidermal growth factor receptor (EGFR) with nimotuzumab (EGFR monoclonal antibody) plus gefitinib (EGFR-tyrosine kinase inhibitor) in advanced non-small cell lung cancer (NSCLC) after platinum-based chemotherapy. An open label, randomized, phase II trial was conducted at 6 centers; 160 patients were randomized (1:1) to either gefitinib alone or nimotuzumab (200 mg, i. v. weekly) plus gefitinib (250 mg p. o. daily) until disease progression or intolerable toxicity. The primary endpoint was progression-free survival (PFS) at 3 months. Of the total 160 enrolled patients, 155 (77: gefitinib, 78: nimotuzumab plus gefitinib) received at least one dose and could be evaluated for efficacy and toxicity. The majority had adenocarcinoma (65.2%) and ECOG performance status of 0 to 1 (83.5%). The median follow-up was 22.1 months, and the PFS rate at 3 months was 48.1% in gefitinib and 37.2% in nimotuzumab plus gefitinib (P = not significant, NS). The median PFS and OS were 2.8 and 13.2 months in gefitinib and 2.0 and 14.0 months in nimotuzumab plus gefitinib. Combined treatment was not associated with superior PFS to gefitinib alone in patients with EGFR mutation (13.5 vs. 10.2 months in gefitinib alone, P=NS) or those with wild-type EGFR (0.9 vs. 2.0 months in gefitinib alone, P=NS). Combined treatment did not increase EGFR inhibition-related adverse events with manageable toxicities. The dual inhibition of EGFR with nimotuzumab plus gefitinib was not associated with better outcomes than gefitinib alone as a second-line treatment of advanced NSCLC (NCT01498562).

Who Are Less Likely to Receive Subsequent Chemotherapy Beyond First-Line Therapy for Advanced Non-small Cell Lung Cancer?: Implications for Selection of Patients for Maintenance Therapy

BackgroundProspective studies have implied that maintenance therapy for non-small cell lung cancer (NSCLC) has its effect by giving active drugs earlier to patients who otherwise die without receiving second-line therapy. The purpose of this study was to select patients with NSCLC who could most benefit from maintenance therapy, by evaluating which patients would be less likely to receive second-line therapy.MethodsClinicopathologic data of patients with advanced NSCLC who received four cycles of first-line chemotherapy followed by time-off therapy and eventual disease progression or death were reviewed retrospectively. Patients were grouped into ones with first-line therapy only or ones with more than first-line therapy. Clinical characteristics between the two groups were compared.ResultsA total of 271 patients were eligible for analysis, and 39 patients (14.4%) received only first-line therapy. Patients significantly more likely to receive only first-line therapy had performance status of two or three after first-line therapy, large volume of initial target lesions (sum of long diameters ≥70 mm), or smaller decrease in target lesions (decrease <20%) after first-line therapy. Median overall survival of the 143 patients (52.8%) with at least one of these characteristics (16.3 months) was significantly shorter than that of patients without any of these characteristics (23.5 months, p = 0.007).ConclusionMaintenance therapy may be of greater benefit to patients with NSCLC who have clinical characteristics including poor performance status after first-line therapy, large initial target lesions, or smaller decrease in target lesions after first-line therapy