Locating an object in a sequence of frames, given its appearance in the first
frame of the sequence, is a hard problem that involves many stages. Usually,
state-of-the-art methods focus on bringing novel ideas in the visual encoding
or relational modelling phases. However, in this work, we show that bounding
box regression from learned joint search and template features is of high
importance as well. While previous methods relied heavily on well-learned
features representing interactions between search and template, we hypothesize
that the receptive field of the input convolutional bounding box network plays
an important role in accurately determining the object location. To this end,
we introduce two novel bounding box regression networks: inception and
deformable. Experiments and ablation studies show that our inception module
installed on the recent ODTrack outperforms the latter on three benchmarks: the
GOT-10k, the UAV123 and the OTB2015.

本研究通过引入两种新的边界框回归网络（inception 和 deformable）来在目标检测中提高定位准确性，并在多个基准测试中取得了优于现有方法的表现。

一种新的边界框回归方法用于单目标跟踪

A Novel Bounding Box Regression Method for Single Object Tracking

Neural reflectance models are capable of accurately reproducing the
spatially-varying appearance of many real-world materials at different scales.
However, existing methods have difficulties handling highly glossy materials.
To address this problem, we introduce a new neural reflectance model which,
compared with existing methods, better preserves not only specular highlights
but also fine-grained details. To this end, we enhance the neural network
performance by encoding input data to frequency space, inspired by NeRF, to
better preserve the details. Furthermore, we introduce a gradient-based loss
and employ it in multiple stages, adaptive to the progress of the learning
phase. Lastly, we utilize an optional extension to the decoder network using
the Inception module for more accurate yet costly performance. We demonstrate
the effectiveness of our method using a variety of synthetic and real examples.

通过将输入数据编码到频率空间来增强神经网络性能，并引入基于梯度的损失在多个阶段使用，使得这种新型神经反射模型在处理高度光泽材料时能够更好地保留镜面高光并提高细节的保留效果。我们最后还使用 Inception 模块作为解码器网络的可选扩展，以获得更精确但成本更高的性能。我们通过使用各种合成和真实示例来证明我们方法的有效性。