Harnessing Temporal Causality for Advanced Temporal Action Detection

As a fundamental task in long-form video understanding, temporal action detection (TAD) aims to capture inherent temporal relations in untrimmed videos and identify candidate actions with precise boundaries. Over the years, various networks, including convolutions, graphs, and transformers, have been explored for effective temporal modeling for TAD. However, these modules typically treat past and future information equally, overlooking the crucial fact that changes in action boundaries are essentially causal events. Inspired by this insight, we propose leveraging the temporal causality of actions to enhance TAD representation by restricting the model's access to only past or future context. We introduce CausalTAD, which combines causal attention and causal Mamba to achieve state-of-the-art performance on multiple benchmarks. Notably, with CausalTAD, we ranked 1st in the Action Recognition, Action Detection, and Audio-Based Interaction Detection tracks at the EPIC-Kitchens Challenge 2024, as well as 1st in the Moment Queries track at the Ego4D Challenge 2024. Our code is available at https://github.com/sming256/OpenTAD/.

NMS Threshold matters for Ego4D Moment Queries -- 2nd place solution to the Ego4D Moment Queries Challenge 2023

This report describes our submission to the Ego4D Moment Queries Challenge 2023. Our submission extends ActionFormer, a latest method for temporal action localization. Our extension combines an improved ground-truth assignment strategy during training and a refined version of SoftNMS at inference time. Our solution is ranked 2nd on the public leaderboard with 26.62% average mAP and 45.69% Recall@1x at tIoU=0.5 on the test set, significantly outperforming the strong baseline from 2023 challenge. Our code is available at https://github.com/happyharrycn/actionformer_release.

Minimum Efforts to Build an End-to-End Spatial-Temporal Action Detector

Spatial-temporal action detection is a vital part of video understanding. Current spatial-temporal action detection methods will first use an object detector to obtain person candidate proposals. Then, the model will classify the person candidates into different action categories. So-called two-stage methods are heavy and hard to apply in real-world applications. Some existing methods use a unified model structure, But they perform badly with the vanilla model and often need extra modules to boost the performance. In this paper, we explore the strategy to build an end-to-end spatial-temporal action detector with minimal modifications. To this end, we propose a new method named ME-STAD, which solves the spatial-temporal action detection problem in an end-to-end manner. Besides the model design, we propose a novel labeling strategy to deal with sparse annotations in spatial-temporal datasets. The proposed ME-STAD achieves better results (2.2% mAP boost) than original two-stage detectors and around 80% FLOPs reduction. Moreover, our proposed ME-STAD only has minimum modifications with previous methods and does not require extra components. Our code will be made public.

Salvage of Supervision in Weakly Supervised Detection

Weakly supervised object detection (WSOD) has recently attracted much attention. However, the method, performance and speed gaps between WSOD and fully supervised detection prevent WSOD from being applied in real-world tasks. To bridge the gaps, this paper proposes a new framework, Salvage of Supervision (SoS), with the key idea being to harness every potentially useful supervisory signal in WSOD: the weak image-level labels, the pseudo-labels, and the power of semi-supervised object detection. This paper shows that each type of supervisory signal brings in notable improvements, outperforms existing WSOD methods (which mainly use only the weak labels) by large margins. The proposed SoS-WSOD method achieves 64.4 $m\text{AP}_{50}$ on VOC2007, 61.9 $m\text{AP}_{50}$ on VOC2012 and 16.4 $m\text{AP}_{50:95}$ on MS-COCO, and also has fast inference speed. Ablations and visualization further verify the effectiveness of SoS.