In this paper, we focus on training an open-set object detector under the
condition of scarce training samples, which should distinguish the known and
unknown categories. Under this challenging scenario, the decision boundaries of
unknowns are difficult to learn and often ambiguous. To mitigate this issue, we
develop a novel open-set object detection framework, which delves into
conditional evidence decoupling for the unknown rejection. Specifically, we
select pseudo-unknown samples by leveraging the discrepancy in attribution
gradients between known and unknown classes, alleviating the inadequate unknown
distribution coverage of training data. Subsequently, we propose a Conditional
Evidence Decoupling Loss (CEDL) based on Evidential Deep Learning (EDL) theory,
which decouples known and unknown properties in pseudo-unknown samples to learn
distinct knowledge, enhancing separability between knowns and unknowns.
Additionally, we propose an Abnormality Calibration Loss (ACL), which serves as
a regularization term to adjust the output probability distribution,
establishing robust decision boundaries for the unknown rejection. Our method
has achieved the superiority performance over previous state-of-the-art
approaches, improving the mean recall of unknown class by 7.24% across all
shots in VOC10-5-5 dataset settings and 1.38% in VOC-COCO dataset settings. The
code is available via this https URL

在稀缺训练样本条件下，我们通过开放集目标检测框架，采用条件证据解耦来区分已知和未知类别，并通过异常度校准损失来调整输出概率分布，提高未知拒绝的决策边界。在 VOC10-5-5 数据集和 VOC-COCO 数据集设置中，我们的方法表现优于之前的最先进方法，未知类别的平均召回率分别提高了 7.24% 和 1.38%。

揭示未知：未知拒绝的条件证据解耦

Unveiling the Unknown: Conditional Evidence Decoupling for Unknown  Rejection

We introduce Grounded SAM, which uses Grounding DINO as an open-set object
detector to combine with the segment anything model (SAM). This integration
enables the detection and segmentation of any regions based on arbitrary text
inputs and opens a door to connecting various vision models. As shown in Fig.1,
a wide range of vision tasks can be achieved by using the versatile Grounded
SAM pipeline. For example, an automatic annotation pipeline based solely on
input images can be realized by incorporating models such as BLIP and Recognize
Anything. Additionally, incorporating Stable-Diffusion allows for controllable
image editing, while the integration of OSX facilitates promptable 3D human
motion analysis. Grounded SAM also shows superior performance on
open-vocabulary benchmarks, achieving 48.7 mean AP on SegInW (Segmentation in
the wild) zero-shot benchmark with the combination of Grounding DINO-Base and
SAM-Huge models.

引入地面 SAM，使用地面化 DINO 作为开放目标检测器并结合分段任何模型（SAM），实现基于任意文本输入的任何区域的检测和分割，并打开了连接各种视觉模型的大门。