DiffRef3D: A Diffusion-based Proposal Refinement Framework for 3D Object Detection

Denoising diffusion models show remarkable performances in generative tasks, and their potential applications in perception tasks are gaining interest. In this paper, we introduce a novel framework named DiffRef3D which adopts the diffusion process on 3D object detection with point clouds for the first time. Specifically, we formulate the proposal refinement stage of two-stage 3D object detectors as a conditional diffusion process. During training, DiffRef3D gradually adds noise to the residuals between proposals and target objects, then applies the noisy residuals to proposals to generate hypotheses. The refinement module utilizes these hypotheses to denoise the noisy residuals and generate accurate box predictions. In the inference phase, DiffRef3D generates initial hypotheses by sampling noise from a Gaussian distribution as residuals and refines the hypotheses through iterative steps. DiffRef3D is a versatile proposal refinement framework that consistently improves the performance of existing 3D object detection models. We demonstrate the significance of DiffRef3D through extensive experiments on the KITTI benchmark. Code will be available

PG-RCNN: Semantic Surface Point Generation for 3D Object Detection

One of the main challenges in LiDAR-based 3D object detection is that the sensors often fail to capture the complete spatial information about the objects due to long distance and occlusion. Two-stage detectors with point cloud completion approaches tackle this problem by adding more points to the regions of interest (RoIs) with a pre-trained network. However, these methods generate dense point clouds of objects for all region proposals, assuming that objects always exist in the RoIs. This leads to the indiscriminate point generation for incorrect proposals as well. Motivated by this, we propose Point Generation R-CNN (PG-RCNN), a novel end-to-end detector that generates semantic surface points of foreground objects for accurate detection. Our method uses a jointly trained RoI point generation module to process the contextual information of RoIs and estimate the complete shape and displacement of foreground objects. For every generated point, PG-RCNN assigns a semantic feature that indicates the estimated foreground probability. Extensive experiments show that the point clouds generated by our method provide geometrically and semantically rich information for refining false positive and misaligned proposals. PG-RCNN achieves competitive performance on the KITTI benchmark, with significantly fewer parameters than state-of-the-art models. The code is available at https://github.com/quotation2520/PG-RCNN.Comment: Accepted by ICCV 202

Explore and Match: End-to-End Video Grounding with Transformer

We present a new paradigm named explore-and-match for video grounding, which aims to seamlessly unify two streams of video grounding methods: proposal-based and proposal-free. To achieve this goal, we formulate video grounding as a set prediction problem and design an end-to-end trainable Video Grounding Transformer (VidGTR) that can utilize the architectural strengths of rich contextualization and parallel decoding for set prediction. The overall training is balanced by two key losses that play different roles, namely span localization loss and set guidance loss. These two losses force each proposal to regress the target timespan and identify the target query. Throughout the training, VidGTR first explores the search space to diversify the initial proposals and then matches the proposals to the corresponding targets to fit them in a fine-grained manner. The explore-and-match scheme successfully combines the strengths of two complementary methods, without encoding prior knowledge into the pipeline. As a result, VidGTR sets new state-of-the-art results on two video grounding benchmarks with double the inference speed

Viola rostrata Muhl. var. japonica Ohwi

原著和名: ナガハシスミレ科名: スミレ科 = Violaceae採集地: 新潟県 三島郡 和島村 (越後 和島村)採集日: 1966/4/6採集者: 萩庭丈壽整理番号: JH046880国立科学博物館整理番号: TNS-VS-99688

Pre-Treatment Objective Diagnosis and Post-Treatment Outcome Evaluation in Patients with Vascular Pulsatile Tinnitus Using Transcanal Recording and Spectro-Temporal Analysis - Fig 1

<p>Pre-treatment (A) and post-treatment (B) ear canal signals of seven vascular pulsatile tinnitus subjects and ear canal signals of five control subjects (C) measured with an upright, neutral head position.</p

Demographic characteristics of the included subjects with vascular pulsatile tinnitus.

<p>Demographic characteristics of the included subjects with vascular pulsatile tinnitus.</p

Sound pressure level differences of the pulse-synchronized five spectral bands (~50ms) between pre- and post-treatment ear canal signals of the patient group and ear canal signals of the control group analyzed in the frequency domain.

<p>Asterisks designate statistically significant. n.s., non-significant.</p