Hierarchical Reinforcement Learning based on Planning Operators

Long-horizon manipulation tasks such as stacking represent a longstanding challenge in the field of robotic manipulation, particularly when using reinforcement learning (RL) methods which often struggle to learn the correct sequence of actions for achieving these complex goals. To learn this sequence, symbolic planning methods offer a good solution based on high-level reasoning, however, planners often fall short in addressing the low-level control specificity needed for precise execution. This paper introduces a novel framework that integrates symbolic planning with hierarchical RL through the cooperation of high-level operators and low-level policies. Our contribution integrates planning operators (e.g. preconditions and effects) as part of the hierarchical RL algorithm based on the Scheduled Auxiliary Control (SAC-X) method. We developed a dual-purpose high-level operator, which can be used both in holistic planning and as independent, reusable policies. Our approach offers a flexible solution for long-horizon tasks, e.g., stacking a cube. The experimental results show that our proposed method obtained an average of 97.2% success rate for learning and executing the whole stack sequence, and the success rate for learning independent policies, e.g. reach (98.9%), lift (99.7%), stack (85%), etc. The training time is also reduced by 68% when using our proposed approach.

Diversifying Question Generation over Knowledge Base via External Natural Questions

Previous methods on knowledge base question generation (KBQG) primarily focus on enhancing the quality of a single generated question. Recognizing the remarkable paraphrasing ability of humans, we contend that diverse texts should convey the same semantics through varied expressions. The above insights make diversifying question generation an intriguing task, where the first challenge is evaluation metrics for diversity. Current metrics inadequately assess the above diversity since they calculate the ratio of unique n-grams in the generated question itself, which leans more towards measuring duplication rather than true diversity. Accordingly, we devise a new diversity evaluation metric, which measures the diversity among top-k generated questions for each instance while ensuring their relevance to the ground truth. Clearly, the second challenge is how to enhance diversifying question generation. To address this challenge, we introduce a dual model framework interwoven by two selection strategies to generate diverse questions leveraging external natural questions. The main idea of our dual framework is to extract more diverse expressions and integrate them into the generation model to enhance diversifying question generation. Extensive experiments on widely used benchmarks for KBQG demonstrate that our proposed approach generates highly diverse questions and improves the performance of question answering tasks.

Multi-dimension Queried and Interacting Network for Stereo Image Deraining

Eliminating the rain degradation in stereo images poses a formidable challenge, which necessitates the efficient exploitation of mutual information present between the dual views. To this end, we devise MQINet, which employs multi-dimension queries and interactions for stereo image deraining. More specifically, our approach incorporates a context-aware dimension-wise queried block (CDQB). This module leverages dimension-wise queries that are independent of the input features and employs global context-aware attention (GCA) to capture essential features while avoiding the entanglement of redundant or irrelevant information. Meanwhile, we introduce an intra-view physics-aware attention (IPA) based on the inverse physical model of rainy images. IPA extracts shallow features that are sensitive to the physics of rain degradation, facilitating the reduction of rain-related artifacts during the early learning period. Furthermore, we integrate a cross-view multi-dimension interacting attention mechanism (CMIA) to foster comprehensive feature interaction between the two views across multiple dimensions. Extensive experimental evaluations demonstrate the superiority of our model over EPRRNet and StereoIRR, achieving respective improvements of 4.18 dB and 0.45 dB in PSNR. Code and models are available at \url{https://github.com/chdwyb/MQINet}.

Decompose Semantic Shifts for Composed Image Retrieval

Composed image retrieval is a type of image retrieval task where the user provides a reference image as a starting point and specifies a text on how to shift from the starting point to the desired target image. However, most existing methods focus on the composition learning of text and reference images and oversimplify the text as a description, neglecting the inherent structure and the user's shifting intention of the texts. As a result, these methods typically take shortcuts that disregard the visual cue of the reference images. To address this issue, we reconsider the text as instructions and propose a Semantic Shift network (SSN) that explicitly decomposes the semantic shifts into two steps: from the reference image to the visual prototype and from the visual prototype to the target image. Specifically, SSN explicitly decomposes the instructions into two components: degradation and upgradation, where the degradation is used to picture the visual prototype from the reference image, while the upgradation is used to enrich the visual prototype into the final representations to retrieve the desired target image. The experimental results show that the proposed SSN demonstrates a significant improvement of 5.42% and 1.37% on the CIRR and FashionIQ datasets, respectively, and establishes a new state-of-the-art performance. Codes will be publicly available.

BEVTrack: A Simple Baseline for 3D Single Object Tracking in Bird's-Eye View

3D single object tracking (SOT) in point clouds is still a challenging problem due to appearance variation, distractors, and high sparsity of point clouds. Notably, in autonomous driving scenarios, the target object typically maintains spatial adjacency across consecutive frames, predominantly moving horizontally. This spatial continuity offers valuable prior knowledge for target localization. However, existing trackers, which often employ point-wise representations, struggle to efficiently utilize this knowledge owing to the irregular format of such representations. Consequently, they require elaborate designs and solving multiple subtasks to establish spatial correspondence. In this paper, we introduce BEVTrack, a simple yet strong baseline framework for 3D SOT. After converting consecutive point clouds into the common Bird's-Eye View representation, BEVTrack inherently encodes spatial proximity and adeptly captures motion cues for tracking via a simple element-wise operation and convolutional layers. Additionally, to better deal with objects having diverse sizes and moving patterns, BEVTrack directly learns the underlying motion distribution rather than making a fixed Laplacian or Gaussian assumption as in previous works. Without bells and whistles, BEVTrack achieves state-of-the-art performance on KITTI and NuScenes datasets while maintaining a high inference speed of 122 FPS. The code will be released at https://github.com/xmm-prio/BEVTrack.

BEVTrack: A Simple Baseline for 3D Single Object Tracking in Birds's-Eye-View

3D single object tracking (SOT) in point clouds is still a challenging problem due to appearance variation, distractors, and high sparsity of point clouds. Notably, in autonomous driving scenarios, the target object typically maintains spatial adjacency across consecutive frames, predominantly moving horizontally. This spatial continuity offers valuable prior knowledge for target localization. However, existing trackers, which often employ point-wise representations, struggle to efficiently utilize this knowledge owing to the irregular format of such representations. Consequently, they require elaborate designs and solving multiple subtasks to establish spatial correspondence. In this paper, we introduce BEVTrack, a simple yet strong baseline framework for 3D SOT. After converting consecutive point clouds into the common Bird's-Eye-View representation, BEVTrack inherently encodes spatial proximity and adeptly captures motion cues for tracking via a simple element-wise operation and convolutional layers. Additionally, to better deal with objects having diverse sizes and moving patterns, BEVTrack directly learns the underlying motion distribution rather than making a fixed Laplacian or Gaussian assumption as in previous works. Without bells and whistles, BEVTrack achieves state-of-the-art performance on KITTI and NuScenes datasets while maintaining a high inference speed of 122 FPS. The code will be released at https://github.com/xmm-prio/BEVTrack.

Diffusion Model is Secretly a Training-free Open Vocabulary Semantic Segmenter

Recent research has explored the utilization of pre-trained text-image discriminative models, such as CLIP, to tackle the challenges associated with open-vocabulary semantic segmentation. However, it is worth noting that the alignment process based on contrastive learning employed by these models may unintentionally result in the loss of crucial localization information and object completeness, which are essential for achieving accurate semantic segmentation. More recently, there has been an emerging interest in extending the application of diffusion models beyond text-to-image generation tasks, particularly in the domain of semantic segmentation. These approaches utilize diffusion models either for generating annotated data or for extracting features to facilitate semantic segmentation. This typically involves training segmentation models by generating a considerable amount of synthetic data or incorporating additional mask annotations. To this end, we uncover the potential of generative text-to-image conditional diffusion models as highly efficient open-vocabulary semantic segmenters, and introduce a novel training-free approach named DiffSegmenter. Specifically, by feeding an input image and candidate classes into an off-the-shelf pre-trained conditional latent diffusion model, the cross-attention maps produced by the denoising U-Net are directly used as segmentation scores, which are further refined and completed by the followed self-attention maps. Additionally, we carefully design effective textual prompts and a category filtering mechanism to further enhance the segmentation results. Extensive experiments on three benchmark datasets show that the proposed DiffSegmenter achieves impressive results for open-vocabulary semantic segmentation.

BEVTrack: A Simple Baseline for Point Cloud Tracking in Bird's-Eye-View

3D single object tracking (SOT) in point clouds is still a challenging problem due to appearance variation, distractors, and high sparsity of point clouds. Notably, in autonomous driving scenarios, the target object typically maintains spatial adjacency across consecutive frames, predominantly moving horizontally. This spatial continuity offers valuable prior knowledge for target localization. However, existing trackers, which often employ point-wise representations, struggle to efficiently utilize this knowledge owing to the irregular format of such representations. Consequently, they require elaborate designs and solving multiple subtasks to establish spatial correspondence. In this paper, we introduce BEVTrack, a simple yet strong baseline framework for 3D SOT. After converting consecutive point clouds into the common Bird's-Eye-View representation, BEVTrack inherently encodes spatial proximity and adeptly captures motion cues for tracking via a simple element-wise operation and convolutional layers. Additionally, to better deal with objects having diverse sizes and moving patterns, BEVTrack directly learns the underlying motion distribution rather than making a fixed Laplacian or Gaussian assumption as in previous works. Without bells and whistles, BEVTrack achieves state-of-the-art performance on KITTI and NuScenes datasets while maintaining a high inference speed of 122 FPS. The code will be released at https://github.com/xmm-prio/BEVTrack.

Transformer Compression via Subspace Projection

We propose TCSP, a novel method for compressing a transformer model by focusing on reducing the hidden size of the model. By projecting the whole transform model into a subspace, we enable matrix operations between the weight matrices in the model and features in a reduced-dimensional space, leading to significant reductions in model parameters and computing resources. To establish this subspace, we decompose the feature matrix, derived from different layers of sampled data instances, into a projection matrix. For evaluation, TCSP is applied to compress T5 and BERT models on the GLUE and SQuAD benchmarks. Experimental results demonstrate that TCSP achieves a compression ratio of 44\% with at most 1.6\% degradation in accuracy, surpassing or matching prior compression methods. Furthermore, TCSP exhibits compatibility with other methods targeting filter and attention head size compression.

PartSeg: Few-shot Part Segmentation via Part-aware Prompt Learning

In this work, we address the task of few-shot part segmentation, which aims to segment the different parts of an unseen object using very few labeled examples. It is found that leveraging the textual space of a powerful pre-trained image-language model (such as CLIP) can be beneficial in learning visual features. Therefore, we develop a novel method termed PartSeg for few-shot part segmentation based on multimodal learning. Specifically, we design a part-aware prompt learning method to generate part-specific prompts that enable the CLIP model to better understand the concept of ``part'' and fully utilize its textual space. Furthermore, since the concept of the same part under different object categories is general, we establish relationships between these parts during the prompt learning process. We conduct extensive experiments on the PartImageNet and Pascal$\_$Part datasets, and the experimental results demonstrated that our proposed method achieves state-of-the-art performance.