The objective of weakly supervised temporal action localization (WTAL) is to accurately identify the temporal intervals of actions using only video-level annotations for training. Existing cross-modal WTAL methods integrate vision-language models to provide rich semantic supervision, aiming to alleviate the inherent supervision limitations in weakly supervised scenarios. However, it is crucial to acknowledge that contemporary cross-modal methods incorporate textual information simultaneously, which inevitably introduces uncertainty in the alignment of cross-modal semantics. Moreover, previous approaches typically output deterministic temporal localization results, while neglecting to evaluate the predictive uncertainty and confidence of localization results. To address the above issues, we propose a novel Modeling Semantic and Localization Uncertainty (MSLU) framework for WTAL, which can simultaneously model the semantic uncertainty in cross-modal representations and the uncertainty of localization results to achieve more precise and robust temporal action localization. Specifically, we propose the Probabilistic Semantic Uncertainty Modeling (PSUM) module, which utilizes probabilistic encoding to capture diverse cross-modal feature representations, effectively mitigating semantic ambiguity in feature alignment. In addition, we propose the Uncertainty-guided Localization Estimation (ULE) module, which leverages evidential deep learning to estimate predictive uncertainty of localization results in weakly supervised scenarios. Through extensive experiments on benchmark datasets including THUMOS14, ActivityNet1.2, ActivityNet1.3, and FineAction, our framework demonstrates superior performance compared to existing state-of-the-art methods. The empirical results validate the effectiveness of simultaneously modeling both semantic and localization uncertainty.

Deep learning-based object detection methods have shown significant success, particularly in robotic vision tasks like autonomous navigation and object manipulation. However, their performance drops sharply in low-light conditions, challenging robots in poorly lit environments. To address this, we propose Dark-DENet, a lightweight detection-driven enhancement network specifically designed for low-light conditions. Dark-DENet introduces an Improved Global Enhancement Module for low-frequency components to capture multiscale features, and a multi-layer convolutional structure in the Detail Enhancement Module to enhance high-frequency components. Additionally, the Scale-Aware Pooling Fusion Module enriches the semantic information of HF components. Dark-DENet is a plug-and-play network that can be easily integrated into the backbone of various detectors for joint training. Integrated with YOLOv5 as DD-YOLO, and combined with other models like YOLO series, RT-DETR, RetinaNet, and Faster R-CNN, experimental results show Dark-DENet consistently improves detection performance across all models. It effectively enhances latent features under limited runtime, making it a robust solution for robotic vision in low-light environments.

Weakly supervised temporal action localization (WTAL) aims to localize action instances with only video-level labels for supervision. Recent methods convert category labels to natural language through prompting and utilize pre-trained vision-language models to generate text representation from natural language for supervision. This is because natural language can provide more prosperous and generalized semantic supervision to compensate for the lack of supervision in weakly supervised scenarios. However, it should be noted that current prompting methods face limitations in generating dynamic prompts that adapt to each video, which leads to difficulties in accurately aligning text and video representations. In this work, we propose a novel Text-Video Knowledge Guided Prompting (TVKP) framework for WTAL, which generates video-aware prompts based on text-video knowledge to enhance semantic alignment between text and video representations and introduce more video-related external category labels to enrich semantic supervision. We introduce the video-aware prompting (VAP) module to learn text-video knowledge from the joint distribution of text and video representations to generate video-aware text representation. Meanwhile, to make VAP more effectively learn text-video knowledge, a text-video contrastive loss is proposed to ensure semantic consistency between text and video representations. In addition, we propose the external knowledge prompting (EKP) module to introduce more video-related text labels from an external knowledge base to enrich prompts for accurate semantic alignment. Extensive experiments are conducted on three public datasets, THUMOS14, ActivityNet1.2, and ActivityNet1.3, demonstrating that our approach outperforms state-of-the-art methods.

Weakly-supervised temporal action localization aims to localize action instances from untrimmed videos with only video-level labels. Due to the lack of frame-wise annotations, most methods embrace a localization-by-classification paradigm. However, the large supervision gap between classification and localization hinders models from obtaining accurate snippet-wise classification sequences and action proposals. We propose a snippet-to-prototype contrastive consensus network (SPCC-Net) to simultaneously generate feature-level and label-level supervision information to narrow the supervision gap between classification and localization. Specifically, the network adopts a two-stream framework incorporating the optical flow and fusion streams to fully leverage the motion and complementary information from multiple modalities. Firstly, the snippet-to-prototype contrast module is executed within each stream to learn prototypes for all categories and contrast them with action snippets to guarantee intra-class compactness and inter-class separability of snippet features. Secondly, for generating accurate label-level supervision information through complementary information of multimodal features, the multi-modality consensus module ensures not only category consistency through knowledge distillation but also semantic consistency through contrastive learning. Finally, we introduce the auxiliary multiple instance learning (MIL) loss to alleviate the issue that existing MIL-based methods only localize sparse discriminative snippets. Extensive experiments are conducted on two public datasets, THUMOS-14 and ActivityNet-1.3, to demonstrate the superior performance of our method over state-of-the-art methods.

In industrial enterprises, effective retrieval of three-dimensional (3-D) computer-aided design (CAD) models can greatly save time and cost in new product development and manufacturing, thus, many researchers have focused on it. Recently, many view-based 3D model retrieval methods have been proposed and have achieved state-of-the-art performance. However, most of these methods focus on extracting more discriminative view-level features and effectively aggregating the multi-view images of a 3D model, and the latent relationship among these multi-view images is not fully explored. Thus, we tackle this problem from the perspective of exploiting the relationships between patch features to capture long-range associations among multi-view images. To capture associations among views, in this work, we propose a novel patch convolutional neural network (PCNN ) for view-based 3D model retrieval. Specifically, we first employ a CNN to extract patch features of each view image separately. Second, a novel neural network module named PatchConv is designed to exploit intrinsic relationships between neighboring patches in the feature space to capture long-range associations among multi-view images. Then, an adaptive weighted view layer is further embedded into PCNN to automatically assign a weight to each view according to the similarity between each view feature and the view-pooling feature. Finally, a discrimination loss function is employed to extract the discriminative 3D model feature, which consists of softmax loss values generated by the fusion classifier and the specific classifier. Extensive experimental results on two public 3D model retrieval benchmarks, namely, the ModelNet40, and ModelNet10, demonstrate that our proposed PCNN can outperform state-of-the-art approaches, with mAP values of 93.67%, and 96.23%, respectively.