弱监督

We introduce SEAS using ShapE-Aligned Supervision to enhance appearance-based person re-identification. When recognizing an individual's identity existing methods primarily rely on appearance which can be influenced by the background environment due to a lack of body shape awareness. Although some methods attempt to incorporate other modalities such as gait or body shape they encode the additional modality separately resulting in extra computational costs and lacking an inherent connection with appearance. In this paper we explore the use of implicit 3-D body shape representations as pixel-level guidance to augment the extraction of identity features with body shape knowledge in addition to appearance. Using body shape as supervision rather than as input provides shape-aware enhancements without any increase in computational cost and delivers coherent integration with pixel-wise appearance features. Moreover for video-based person re-identification we align pixel-level features across frames with shape awareness to ensure temporal consistency. Our results demonstrate that incorporating body shape as pixel-level supervision reduces rank-1 errors by 1.4% for frame-based and by 2.5% for video-based re-identification tasks respectively and can also be generalized to other existing appearance-based person re-identification methods.

Single point-supervised object detection is gaining attention due to its cost-effectiveness. However existing approaches focus on generating horizontal bounding boxes (HBBs) while ignoring oriented bounding boxes (OBBs) commonly used for objects in aerial images. This paper proposes PointOBB the first single Point-based OBB generation method for oriented object detection. PointOBB operates through the collaborative utilization of three distinctive views: an original view a resized view and a rotated/flipped (rot/flp) view. Upon the original view we leverage the resized and rot/flp views to build a scale augmentation module and an angle acquisition module respectively. In the former module a Scale-Sensitive Consistency (SSC) loss is designed to enhance the deep network's ability to perceive the object scale. For accurate object angle predictions the latter module incorporates self-supervised learning to predict angles which is associated with a scale-guided Dense-to-Sparse (DS) matching strategy for aggregating dense angles corresponding to sparse objects. The resized and rot/flp views are switched using a progressive multi-view switching strategy during training to achieve coupled optimization of scale and angle. Experimental results on the DIOR-R and DOTA-v1.0 datasets demonstrate that PointOBB achieves promising performance and significantly outperforms potential point-supervised baselines.

The premise for the great advancement of molecular machine learning is dependent on a considerable amount of labeled data. In many real-world scenarios the labeled molecules are limited in quantity or laborious to derive. Recent pseudo-labeling methods are usually designed based on a single domain knowledge thereby failing to understand the comprehensive molecular configurations and limiting their adaptability to generalize across diverse biochemical context. To this end we introduce an innovative paradigm for dealing with the molecule pseudo-labeling named as Molecular Data Programming (MDP). In particular we adopt systematic supervision sources via crafting multiple graph labeling functions which covers various molecular structural knowledge of graph kernels molecular fingerprints and topological features. Each of them creates an uncertain and biased labels for the unlabeled molecules. To address the decision conflicts among the diverse pseudo-labels we design a label synchronizer to differentiably model confidences and correlations between the labeling functions which yields probabilistic molecular labels to adapt for specific applications. These probabilistic molecular labels are used to train a molecular classifier for improving its generalization capability. On eight benchmark datasets we empirically demonstrate the effectiveness of MDP on the weakly supervised molecule classification tasks.

DETR accomplishes end-to-end object detection through iteratively generating multiple object candidates based on image features and promoting one candidate for each ground-truth object. The traditional training procedure using one-to-one supervision in the original DETR lacks direct supervision for the object detection candidates. We aim at improving the DETR training efficiency by explicitly supervising the candidate generation procedure through mixing one-to-one supervision and one-to-many supervision. Our approach namely MS-DETR is simple and places one-to-many supervision to the object queries of the primary decoder that is used for inference. In comparison to existing DETR variants with one-to-many supervision such as Group DETR and Hybrid DETR our approach does not need additional decoder branches or object queries. The object queries of the primary decoder in our approach directly benefit from one-to-many supervision and thus are superior in object candidate prediction. Experimental results show that our approach outperforms related DETR variants such as DN-DETR Hybrid DETR and Group DETR and the combination with related DETR variants further improves the performance.

Improving the detection of distant 3d objects is an important yet challenging task. For camera-based 3D perception the annotation of 3d bounding relies heavily on LiDAR for accurate depth information. As such the distance of annotation is often limited due to the sparsity of LiDAR points on distant objects which hampers the capability of existing detectors for long-range scenarios. We address this challenge by considering only 2D box supervision for distant objects since they are easy to annotate. We propose LR3D a framework that learns to recover the missing depth of distant objects. LR3D adopts an implicit projection head to learn the generation of mapping between 2D boxes and depth using the 3D supervision on close objects. This mapping allows the depth estimation of distant objects conditioned on their 2D boxes making long-range 3D detection with 2D supervision feasible. Experiments show that without distant 3D annotations LR3D allows camera-based methods to detect distant objects (over 200m) with comparable accuracy to full 3D supervision. Our framework is general and could widely benefit 3D detection methods to a large extent.

Continual learning (CL) aims to empower models to learn new tasks without forgetting previously acquired knowledge. Most prior works concentrate on the techniques of architectures replay data regularization etc. However the category name of each class is largely neglected. Existing methods commonly utilize the one-hot labels and randomly initialize the classifier head. We argue that the scarce semantic information conveyed by the one-hot labels hampers the effective knowledge transfer across tasks. In this paper we revisit the role of the classifier head within the CL paradigm and replace the classifier with semantic knowledge from pretrained language models (PLMs). Specifically we use PLMs to generate semantic targets for each class which are frozen and serve as supervision signals during training. Such targets fully consider the semantic correlation between all classes across tasks. Empirical studies show that our approach mitigates forgetting by alleviating representation drifting and facilitating knowledge transfer across tasks. The proposed method is simple to implement and can seamlessly be plugged into existing methods with negligible adjustments. Extensive experiments based on eleven mainstream baselines demonstrate the effectiveness and generalizability of our approach to various protocols. For example under the class-incremental learning setting on ImageNet-100 our method significantly improves the Top-1 accuracy by 3.2% to 6.1% while reducing the forgetting rate by 2.6% to 13.1%.

Identifying the chemical structure from a graphical representation or image of a molecule is a challenging pattern recognition task that would greatly benefit drug development. Yet existing methods for chemical structure recognition do not typically generalize well and show diminished effectiveness when confronted with domains where data is sparse or costly to generate such as hand-drawn molecule images. To address this limitation we propose a new chemical structure recognition tool that delivers state-of-the-art performance and can adapt to new domains with a limited number of data samples and supervision. Unlike previous approaches our method provides atom-level localization and can therefore segment the image into the different atoms and bonds. Our model is the first model to perform OCSR with atom-level entity detection with only SMILES supervision. Through rigorous and extensive benchmarking we demonstrate the preeminence of our chemical structure recognition approach in terms of data efficiency accuracy and atom-level entity prediction.