Revisiting DETR Pre-training for Object Detection

Motivated by that DETR-based approaches have established new records on COCO detection and segmentation benchmarks, many recent endeavors show increasing interest in how to further improve DETR-based approaches by pre-training the Transformer in a self-supervised manner while keeping the backbone frozen. Some studies already claimed significant improvements in accuracy. In this paper, we take a closer look at their experimental methodology and check if their approaches are still effective on the very recent state-of-the-art such as $\mathcal{H}$-Deformable-DETR. We conduct thorough experiments on COCO object detection tasks to study the influence of the choice of pre-training datasets, localization, and classification target generation schemes. Unfortunately, we find the previous representative self-supervised approach such as DETReg, fails to boost the performance of the strong DETR-based approaches on full data regimes. We further analyze the reasons and find that simply combining a more accurate box predictor and Objects$365$ benchmark can significantly improve the results in follow-up experiments. We demonstrate the effectiveness of our approach by achieving strong object detection results of AP=$59.3\%$ on COCO val set, which surpasses $\mathcal{H}$-Deformable-DETR + Swin-L by +$1.4\%$. Last, we generate a series of synthetic pre-training datasets by combining the very recent image-to-text captioning models (LLaVA) and text-to-image generative models (SDXL). Notably, pre-training on these synthetic datasets leads to notable improvements in object detection performance. Looking ahead, we anticipate substantial advantages through the future expansion of the synthetic pre-training dataset

Rank-DETR for High Quality Object Detection

Modern detection transformers (DETRs) use a set of object queries to predict a list of bounding boxes, sort them by their classification confidence scores, and select the top-ranked predictions as the final detection results for the given input image. A highly performant object detector requires accurate ranking for the bounding box predictions. For DETR-based detectors, the top-ranked bounding boxes suffer from less accurate localization quality due to the misalignment between classification scores and localization accuracy, thus impeding the construction of high-quality detectors. In this work, we introduce a simple and highly performant DETR-based object detector by proposing a series of rank-oriented designs, combinedly called Rank-DETR. Our key contributions include: (i) a rank-oriented architecture design that can prompt positive predictions and suppress the negative ones to ensure lower false positive rates, as well as (ii) a rank-oriented loss function and matching cost design that prioritizes predictions of more accurate localization accuracy during ranking to boost the AP under high IoU thresholds. We apply our method to improve the recent SOTA methods (e.g., H-DETR and DINO-DETR) and report strong COCO object detection results when using different backbones such as ResNet-$50$, Swin-T, and Swin-L, demonstrating the effectiveness of our approach. Code is available at \url{https://github.com/LeapLabTHU/Rank-DETR}.Comment: NeurIPS 202

Multidifferential study of identified charged hadron distributions in ZZ-tagged jets in proton-proton collisions at s=\sqrt{s}=13 TeV

Jet fragmentation functions are measured for the first time in proton-proton collisions for charged pions, kaons, and protons within jets recoiling against a $Z$ boson. The charged-hadron distributions are studied longitudinally and transversely to the jet direction for jets with transverse momentum 20 $< p_{\textrm{T}} < 100$ GeV and in the pseudorapidity range $2.5 < \eta < 4$. The data sample was collected with the LHCb experiment at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 1.64 fb$^{-1}$. Triple differential distributions as a function of the hadron longitudinal momentum fraction, hadron transverse momentum, and jet transverse momentum are also measured for the first time. This helps constrain transverse-momentum-dependent fragmentation functions. Differences in the shapes and magnitudes of the measured distributions for the different hadron species provide insights into the hadronization process for jets predominantly initiated by light quarks.Comment: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2022-013.html (LHCb public pages

Study of the B−→Λc+Λˉc−K−B^{-} \to \Lambda_{c}^{+} \bar{\Lambda}_{c}^{-} K^{-} decay

The decay $B^{-} \to \Lambda_{c}^{+} \bar{\Lambda}_{c}^{-} K^{-}$ is studied in proton-proton collisions at a center-of-mass energy of $\sqrt{s}=13$ TeV using data corresponding to an integrated luminosity of 5 $\mathrm{fb}^{-1}$ collected by the LHCb experiment. In the $\Lambda_{c}^+ K^{-}$ system, the $\Xi_{c}(2930)^{0}$ state observed at the BaBar and Belle experiments is resolved into two narrower states, $\Xi_{c}(2923)^{0}$ and $\Xi_{c}(2939)^{0}$, whose masses and widths are measured to be $$m(\Xi_{c}(2923)^{0}) = 2924.5 \pm 0.4 \pm 1.1 \,\mathrm{MeV}, \\ m(\Xi_{c}(2939)^{0}) = 2938.5 \pm 0.9 \pm 2.3 \,\mathrm{MeV}, \\ \Gamma(\Xi_{c}(2923)^{0}) = \phantom{000}4.8 \pm 0.9 \pm 1.5 \,\mathrm{MeV},\\ \Gamma(\Xi_{c}(2939)^{0}) = \phantom{00}11.0 \pm 1.9 \pm 7.5 \,\mathrm{MeV},$$ where the first uncertainties are statistical and the second systematic. The results are consistent with a previous LHCb measurement using a prompt $\Lambda_{c}^{+} K^{-}$ sample. Evidence of a new $\Xi_{c}(2880)^{0}$ state is found with a local significance of $3.8\,\sigma$, whose mass and width are measured to be $2881.8 \pm 3.1 \pm 8.5\,\mathrm{MeV}$ and $12.4 \pm 5.3 \pm 5.8 \,\mathrm{MeV}$, respectively. In addition, evidence of a new decay mode $\Xi_{c}(2790)^{0} \to \Lambda_{c}^{+} K^{-}$ is found with a significance of $3.7\,\sigma$. The relative branching fraction of $B^{-} \to \Lambda_{c}^{+} \bar{\Lambda}_{c}^{-} K^{-}$ with respect to the $B^{-} \to D^{+} D^{-} K^{-}$ decay is measured to be $2.36 \pm 0.11 \pm 0.22 \pm 0.25$, where the first uncertainty is statistical, the second systematic and the third originates from the branching fractions of charm hadron decays.Comment: All figures and tables, along with any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2022-028.html (LHCb public pages

Measurement of the ratios of branching fractions R(D∗)\mathcal{R}(D^{*}) and R(D0)\mathcal{R}(D^{0})

The ratios of branching fractions $\mathcal{R}(D^{*})\equiv\mathcal{B}(\bar{B}\to D^{*}\tau^{-}\bar{\nu}_{\tau})/\mathcal{B}(\bar{B}\to D^{*}\mu^{-}\bar{\nu}_{\mu})$ and $\mathcal{R}(D^{0})\equiv\mathcal{B}(B^{-}\to D^{0}\tau^{-}\bar{\nu}_{\tau})/\mathcal{B}(B^{-}\to D^{0}\mu^{-}\bar{\nu}_{\mu})$ are measured, assuming isospin symmetry, using a sample of proton-proton collision data corresponding to 3.0 fb${ }^{-1}$ of integrated luminosity recorded by the LHCb experiment during 2011 and 2012. The tau lepton is identified in the decay mode $\tau^{-}\to\mu^{-}\nu_{\tau}\bar{\nu}_{\mu}$. The measured values are $\mathcal{R}(D^{*})=0.281\pm0.018\pm0.024$ and $\mathcal{R}(D^{0})=0.441\pm0.060\pm0.066$, where the first uncertainty is statistical and the second is systematic. The correlation between these measurements is $\rho=-0.43$. Results are consistent with the current average of these quantities and are at a combined 1.9 standard deviations from the predictions based on lepton flavor universality in the Standard Model.Comment: All figures and tables, along with any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2022-039.html (LHCb public pages

The timescale of plume-driven cratonization: A complete record from Tarim

ABSTRACT: Cratonization of the Tarim block in central Asia is finalized by the Permian Tarim plume that welded two cratonic nuclei together. Hence, the over-10-km-thick Tarim basin preserves a complete record of deformation and growth strata before, during, and after the plume-driven cratonization. Here we use seismic reflection data from the central Tarim basin to quantify the timing and style of the Paleozoic–Mesozoic deformation. The thrust and strike-slip faults there all underwent an early, intense deformation stage in the earliest Ordovician–Middle Devonian, a hiatus stage from Late Devonian to Late Permian, and a newly-discovered stage of weak activity throughout the Mesozoic. The intracontinental deformation is controlled by the subduction and accretion surrounding the Tarim block. The minor, but non-zero, Mesozoic strains reflect the ongoing adjustment to far-field compressions during the cooling and strengthening of the plume-stitched continental lithosphere. The cessation of interior deformation marks that the Tarim cratonization is finally attained ~200 Myr after the plume waned

SUPPLEMENTARY DATA FIGURES from Receptor-interacting Protein Kinase 2 Is an Immunotherapy Target in Pancreatic Cancer

Supplementary Figure S1. In vivo CRISPR screens to identify critical drivers of immune evasion; Supplementary Figure S2. Effects of Ripk2 depletion on tumor growth; Supplementary Figure S3. RIPK2 is overexpressed in human and mouse PDAC tissues; Supplementary Figure S4. Ablation of RIPK2 disrupts the desmoplastic TME; Supplementary Figure S5. RIPK2 modulates the immune profile and impairs anti-tumor T cell response; Supplementary Figure S6. RIPK2 restricts the activation and effector states of CD8+ T cells by impairing antigen presentation; Supplementary Figure S7. RIPK2 promotes MHC-I trafficking to lysosomes via NBR1; Supplementary Figure S8. RIPK2 ubiquitination promotes NBR1-mediated MHC-I degradation; Supplementary Figure S9. RIPK2 ablation potentiates the efficacy of PD-1 blockade; Supplementary Figure S10. Diagram illustrating RIPK2-mediated degradation of MHC I through autophagy–lysosome system.</p

Supplementary Table S4 from Receptor-interacting Protein Kinase 2 Is an Immunotherapy Target in Pancreatic Cancer

Supplementary Table S4. Primers used for qPCR or generating knockout/knockdown constructs.</p

Supplementary Table S2 from Receptor-interacting Protein Kinase 2 Is an Immunotherapy Target in Pancreatic Cancer

Supplementary Table S2. Detailed information of human pancreatic tissue array.</p

Supplementary Table S1 from Receptor-interacting Protein Kinase 2 Is an Immunotherapy Target in Pancreatic Cancer

Supplementary Table S1. MAGeCK analysis of CRISPR screen results to identify regulator of immune evasion in orthotopic PDAC mouse models.</p