We present a novel end-to-end method for long-form video temporal grounding
to locate specific moments described by natural language queries. Prior
long-video methods for this task typically contain two stages: proposal
selection and grounding regression. However, the proposal selection of these
methods is disjoint from the grounding network and is not trained end-to-end,
which limits the effectiveness of these methods. Moreover, these methods
operate uniformly over the entire temporal window, which is suboptimal given
redundant and irrelevant features in long videos. In contrast to these prior
approaches, we introduce RGNet, a unified network designed for jointly
selecting proposals from hour-long videos and locating moments specified by
natural language queries within them. To achieve this, we redefine proposal
selection as a video-text retrieval task, i.e., retrieving the correct
candidate videos given a text query. The core component of RGNet is a unified
cross-modal RG-Encoder that bridges the two stages with shared features and
mutual optimization. The encoder strategically focuses on relevant time frames
using a sparse sampling technique. RGNet outperforms previous methods,
demonstrating state-of-the-art performance on long video temporal grounding
datasets MAD and Ego4D. The code is released at
this https URL

通过统一跨模态 RG-Encoder 和稀疏采样技术，RGNet 方法实现了对长视频进行端到端的特定时刻定位，从而超过了以往在长视频时间定位数据集 MAD 和 Ego4D 上的方法，展示了最先进的性能。

RGNet：一个用于长视频的统一检索与定位网络

RGNet: A Unified Retrieval and Grounding Network for Long Videos

Tracking-by-detection approaches are some of the most successful object
trackers in recent years. Their success is largely determined by the detector
model they learn initially and then update over time. However, under
challenging conditions where an object can undergo transformations, e.g.,
severe rotation, these methods are found to be lacking. In this paper, we
address this problem by formulating it as a proposal selection task and making
two contributions. The first one is introducing novel proposals estimated from
the geometric transformations undergone by the object, and building a rich
candidate set for predicting the object location. The second one is devising a
novel selection strategy using multiple cues, i.e., detection score and
edgeness score computed from state-of-the-art object edges and motion
boundaries. We extensively evaluate our approach on the visual object tracking
2014 challenge and online tracking benchmark datasets, and show the best
performance.

本文提出了一种通过采用基于几何变换估算的新建议并运用多种线索的选择策略，来跟踪在对象可能经历严重旋转等多种变换下的的目标进行追踪，并在多个数据集中获得最佳性能。