Localization and recognition of human action in 3D using transformers.

Understanding a person's behavior from their 3D motion sequence is a fundamental problem in computer vision with many applications. An important component of this problem is 3D action localization, which involves recognizing what actions a person is performing, and when the actions occur in the sequence. To promote the progress of the 3D action localization community, we introduce a new, challenging, and more complex benchmark dataset, BABEL-TAL (BT), for 3D action localization.

Comprehensive Attribute Prediction Learning for Person Search by Language.

Person search by language refers to searching for the interested pedestrian images given natural language sentences, which requires capturing fine-grained differences to accurately distinguish different pedestrians, while still far from being well addressed by most of the current solutions. In this paper, we propose the Comprehensive Attribute Prediction Learning (CAPL) method, which explicitly carries out attribute prediction learning, for improving the modeling capabilities of fine-grained semantic attributes and obtaining more discriminative visual and textual representations. First, we construct the semantic ATTribute Vocabulary (ATT-Vocab) based on sentence analysis.

Improving Inconspicuous Attributes Modeling for Person Search by Language.

Person search by language aims to retrieve the interested pedestrian images based on natural language sentences. Although great efforts have been made to address the cross-modal heterogeneity, most of the current solutions suffer from only capturing salient attributes while ignoring inconspicuous ones, being weak in distinguishing very similar pedestrians. In this work, we propose the Adaptive Salient Attribute Mask Network (ASAMN) to adaptively mask the salient attributes for cross-modal alignments, and therefore induce the model to simultaneously focus on inconspicuous attributes.

Actor and Action Modular Network for Text-Based Video Segmentation.

Text-based video segmentation aims to segment an actor in video sequences by specifying the actor and its performing action with a textual query. Previous methods fail to explicitly align the video content with the textual query in a fine-grained manner according to the actor and its action, due to the problem of semantic asymmetry. The semantic asymmetry implies that two modalities contain different amounts of semantic information during the multi-modal fusion process.

Multi-Modality Self-Distillation for Weakly Supervised Temporal Action Localization.

As a challenging task of high-level video understanding, Weakly-supervised Temporal Action Localization (WTAL) has attracted increasing attention in recent years. However, due to the weak supervisions of whole-video classification labels, it is challenging to accurately determine action instance boundaries. To address this issue, pseudo-label-based methods [Alwassel et al.

Modeling Sub-Actions for Weakly Supervised Temporal Action Localization.

As a challenging task of high-level video understanding, weakly supervised temporal action localization has attracted more attention recently. Due to the usage of video-level category labels, this task is usually formulated as the task of classification, which always suffers from the contradiction between classification and detection. In this paper, we describe a novel approach to alleviate the contradiction for detecting more complete action instances by explicitly modeling sub-actions.

Two-Branch Relational Prototypical Network for Weakly Supervised Temporal Action Localization.

As a challenging task of high-level video understanding, weakly supervised temporal action localization has attracted more attention recently. With only video-level category labels, this task should indistinguishably identify the background and action categories frame by frame. However, it is non-trivial to achieve this in untrimmed videos, due to the unconstrained background, complex and multi-label actions.

[Progressively transformed germinal center in oral and maxillofacial region: report of 3 cases].

Purpose: To discuss the diagnosis and prognosis of progressively transformed germinal center and emphasize the necessity of long term follow-up after treatment.

Methods: Three patients were diagnosed as progressively transformed germinal center (PTGC) from 2010 to 2012. The clinical characteristics, histological features, differential diagnosis, prognosis were analyzed with review of literatures.

[Root canal therapy opportunity in replanted teeth due to injury.].

Purpose: To investigate the opportunity of different root canal therapies in replantation of tooth due to injury.

Methods: 49 cases with teeth luxation were randomly divided into three groups. In group A, the pulp was removed before replanted, and calcium hydroxide was filled in root canals and condensated routinely after half a year.

[Effect of tinidazole-dexamethasone-iodoform paste on controlling endodontic interappointment emergencies].

Purpose: To compare the clinical effect of tinidazole-dexamethasone-iodoform paste on intracanal dressing medication for teeth with chronic periapical periodontitis with that of formocresol.

Methods: 520 permanent teeth with chronic periapical periodontitis were selected and divided randomly into tinidazole-dexamethasone-iodoform paste group (A group) and formocresol group (B group). The periapical signs and symptoms were recorded, radiographs were taken.

[Use of cortical (lag) screws in internal fixation of mental fractures].

Purpose: To evaluate the clinical results of cortical (lag) screws in internal fixation of mental fractures.

Methods: 12 patients with single linear mental fracture underwent internal fixation using cortical screws. The patients were followed up for 6 to 9 months after fixation.