Open-world class-agnostic object detection aims to localize all objects in images regardless of whether their categories are known during training. Most existing studies focus on unannotated objects in training images, neglecting objects that are absent from the training set, namely, unseen-unknown objects. Although large-scale foundation models can potentially detect such unseen-unknown objects, their high computational demands limit their feasibility on real-world applications. Feature distillation can enable small yet effective detectors; however, existing methods struggle to transfer feature representations between detector pairs with differing architectures. To address these limitations, we propose a novel feature distillation approach that transfers feature representations from a foundation model to a small structurally different student detector. The proposed method effectively transfers these feature representations by explicitly adapting them to a student detector. Experimental results demonstrated that our approach significantly improves the detection performance of unseen-unknown objects across various detector types, including two-stage, one-stage, and anchor-free detectors.

Mamba, a lightweight sequence modeling framework offering near-linear complexity, presents a promising alternative to Transformers. In this work, we introduce MOGO (Mamba Only Glances Once), an end-to-end framework for efficient video action detection built entirely on the Mamba architecture. In MOGO, our newly designed Mamba-based decoder can even use just one Mamba layer to effectively perform action detection. It uses neither Transformer structures nor RCNN-like methods for proposal detection. Our framework introduces two key innovations. First, we propose a pure Mamba-based encoder-decoder architecture. The encoder processes cross-frame video information, while the decoder incorporates two novel Mamba-based structures that leverage Mamba’s intrinsic capabilities to detect actions. Theoretical analysis and ablation experiments confirm their synergy and the necessity of each structure. Second, we design a video token construction mechanism to improve the model's performance. The token importance block can ensure that the retained token information is highly relevant to the predicted targets. These two innovations make MOGO both efficient and accurate, as demonstrated on the JHMDB and UCF101-24 benchmark datasets. Compared to SOTA action detection methods, MOGO achieves superior performance in terms of GFLOPs, model parameters, and inference speed (latency) with comparable detection precision. Additionally, it requires significantly less GPU memory than some SOTA token reconstruction methods. Code is available at https://github.com/YunqingLiu-ML/MOGO.

Scene graph generation is a fundamental task for visual understanding, and the generated scene graphs are leveraged in various downstream tasks. Most existing methods rely only on the features cropped by bounding boxes to predict the relationship between subject and object. This indicates that the region outside the subject and object is ignored. Therefore, these methods lack contextually important information existing outside of the subject and object regions. Furthermore, recently, it has been pointed out that unannotated instances in the training dataset can lead to false suppression of the valid predictions of the model. To this end, we propose a novel transformer-based network and a training scheme for our model. We introduce the relational attention module to overcome the cropped feature problem. This module can adaptively extract the contextually important regions from the entire image via the attention mechanism for each entity pair whose relationship is predicted. Moreover, to train our model, we design a training strategy with instance-level pseudotargets. This design can solve the incomplete annotation problem by appending the instances generated by the trained model as a pseudo-target. Extensive quantitative and qualitative experiments demonstrate the effectiveness of our key designs, and our model achieves state-of-the-art or competitive performance in all tasks: PredCls, SGCls, and SGDet on the Visual Genome dataset.

The authors describe a reliable feature-based stereo matching method which determines more reliable pairs of matching edges earlier than less reliable ones and makes use of previous matching results. The contrast of an edge point is used as the measure of reliability of the match because an edge with high contrast has high positional directional accuracy. The decisions on whether there are correspondences between pairs of lower contrast edges or not become easier because previously found more reliable matching results are available to reduce the occurrences of ambiguous matches. Experiments with complicated indoor scenes proved that the proposed method is better than those which do not consider reliability.>

We introduce utilizations of T- junctions for stereo matching and detecition of T-junctions. By identifying T-junctions we can improve matchings near occluding con4ours. T- junction is also useful for a horizontal line to de- termtine whether or not the disparity can be inter- polated because the interpolation is allowed only if none of the terminals of the horizontal line relate with T-junctions. Edges are extracted as zero-crossings of a Lapla- cian Gaussian filtered image. We analyze the re- sponse of a Laplacian of a Gaussian filtered image at ideal T-junctions and obtain a method to de- tected T-junctions from a monocular image:.

Describes two topics: (1) a reliable feature-based stereo matching method which determines the more reliable matching pairs earlier and then determines the less reliable ones later using the previously obtained results; and (2) a reliable calibration method for small vertical displacements. The feature point is an edge. The contrast, the position, and the direction of the edge are used to evaluate the matching reliability. Experiments with complicated indoor scenes proved that the proposed method is better than conventional methods.>