Zunran Wang

Lexicon-based retrieval has gained siginificant popularity in text retrieval due to its efficient and robust performance. To further enhance performance of lexicon-based retrieval, researchers have been diligently incorporating state-of-the-art methodologies like Neural retrieval and text-level contrastive learning approaches. Nonetheless, despite the promising outcomes, current lexicon-based retrieval methods have received limited attention in exploring the potential benefits of feature context representations and term-level knowledge guidance. In this paper, we introduce an innovative method by introducing FEature Context and TErm-level Knowledge modules(FecTek). To effectively enrich the feature context representations of term weight, the Feature Context Module (FCM) is introduced, which leverages the power of BERT's representation to determine dynamic weights for each element in the embedding. Additionally, we develop a term-level knowledge guidance module (TKGM) for effectively utilizing term-level knowledge to intelligently guide the modeling process of term weight. Evaluation of the proposed method on MS Marco benchmark demonstrates its superiority over the previous state-of-the-art approaches.

6D pose estimation with individual modality encounters difficulties due to the limitations of modalities, such as RGB information on textureless objects and depth on reflective objects. This can be improved by exploiting the complementarity between modalities. Most of the previous methods only consider the correspondence between point clouds and RGB images and directly extract the features of the corresponding two modalities for fusion, which ignore the information of the modality itself and are negatively affected by erroneous background information when introducing more features for fusion. To enhance the complementarities between multiple modalities, we propose a neighbor-based cross-modalities attention mechanism for multi-modal 6D pose estimation. Neighbors represent that the RGB features of multiple neighbor are applied for fusion, which expands the receptive field. The cross-modalities attention mechanism leverages the similarities between the different modal features to help modal feature fusion, which reduces the negative impact of incorrect background information. Moreover, we design some features between the rendered image and the original image to obtain the confidence of pose estimation results. Experimental results on LM, LM-O and YCB-V datasets demonstrate the effectiveness of our methods. Video is available at https://www.youtube.com/watch?v=ApNBcX6NEGs. Note to Practitioners —Introducing the information of surrounding points during multi-modal fusion improves the performance of 6D pose estimation. For example, the RGB image corresponding to some point clouds on the object may lack rich texture features while the neighbors exist. However, most methods of modal fusion based on RGBD for 6D pose estimation only simply consider the corresponding between RGB images and point clouds for feature fusion, which may bring redundant information or the wrong background information when introducing neighbor information. In this paper, we propose a cross-modal attention mechanism based on neighbor information. By introducing the information of the modality itself to obtain the weight of the neighbor information of another modality in the encoding and decoding stages, the receptive field is expanded and the complementarities between different modalities are enhanced. The experiment shows our effectiveness. In addition, we provide a pose confidence estimator for predicted pose results. Specifically, the rendered image with the predicted pose and the real image are applied to extract features for the decision tree. The experimental results show that the result of the wrong estimation can be eliminated with high accuracy and recall. The 6D pose confidence can provide a reference for real-world grasping. However, the current method can only estimate objects with known models. In the future, we will consider applying the method to unseen objects.

Existing methods of cross-modal domain adaptation for 3D semantic segmentation predict results only via 2D-3D complementarity that is obtained by cross-modal feature matching. However, as lacking supervision in the target domain, the complementarity is not always reliable. The results are not ideal when the domain gap is large. To solve the problem of lacking supervision, we introduce masked modeling into this task and propose a method Mx2M, which utilizes masked cross-modality modeling to reduce the large domain gap. Our Mx2M contains two components. One is the core solution, cross-modal removal and prediction (xMRP), which makes the Mx2M adapt to various scenarios and provides cross-modal self-supervision. The other is a new way of cross-modal feature matching, the dynamic cross-modal filter (DxMF) that ensures the whole method dynamically uses more suitable 2D-3D complementarity. Evaluation of the Mx2M on three DA scenarios, including Day/Night, USA/Singapore, and A2D2/SemanticKITTI, brings large improvements over previous methods on many metrics.

Existing methods of cross-modal domain adaptation for 3D semantic segmentation predict results only via 2D-3D complementarity that is obtained by cross-modal feature matching. However, as lacking supervision in the target domain, the complementarity is not always reliable. The results are not ideal when the domain gap is large. To solve the problem of lacking supervision, we introduce masked modeling into this task and propose a method Mx2M, which utilizes masked cross-modality modeling to reduce the large domain gap. Our Mx2M contains two components. One is the core solution, cross-modal removal and prediction (xMRP), which makes the Mx2M adapt to various scenarios and provides cross-modal self-supervision. The other is a new way of cross-modal feature matching, the dynamic cross-modal filter (DxMF) that ensures the whole method dynamically uses more suitable 2D-3D complementarity. Evaluation of the Mx2M on three DA scenarios, including Day/Night, USA/Singapore, and A2D2/SemanticKITTI, brings large improvements over previous methods on many metrics.