Vertex Subset Problems (VSPs) are a class of combinatorial optimization problems on graphs where the goal is to find a subset of vertices satisfying a predefined condition. Two prominent approaches for solving VSPs are dynamic programming over tree-like structures, such as tree-decompositions or clique-decompositions, and linear programming. In this work, we establish a sharp connection between both approaches by showing that if a vertex-subset problem Pi admits a solution-preserving dynamic programming algorithm that produces tables of size at most alpha(k,n) when processing a tree decomposition of width at most k of an n-vertex graph G, then the polytope defined as the convex-hull of solutions of Pi in G has extension complexity at most O(alpha(k,n)*n). Additionally, this upper bound is optimal under the exponential time hypothesis (ETH). At the one hand, our results imply that ETH-optimal solution-preserving dynamic programming algorithms for combinatorial problems yield optimal-size parameterized extended formulations for the solution polytopes associated with instances of these problems. At the other hand, unconditional lower bounds obtained in the realm of the theory of extended formulations yield unconditional lower bounds on the table complexity of solution-preserving dynamic programming algorithms.
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Efficient Cost-Minimization Schemes for Electrical Energy Demand Satisfaction by Prosumers in Microgrids with Battery Storage Capabilities
PDF ↗We introduce and study various models for satisfying electrical energy demands of prosumers in a microgrid, while optimizing their costs. Each prosumer has individual demands of electrical energy, which can vary day-by-day, and which they can satisfy by either generating electrical energy through a self-operated mini power plant like a solar panel, through buying from an external energy provider, such as the main grid or by trading with other prosumers. Our models take into account two key aspects motivated by real-life scenarios: first, we consider a daily volatility of prices for buying and selling the energy, and second, the possibility to store the self-generated energy in a battery of finite capacity to be either self-consumed or sold to other prosumers in the future. We provide a thorough complexity analysis, as well as efficient algorithms, so that prosumers can minimize their overall cost over the entire time horizon. As a byproduct, we also solve a new, generalized version of the KNAPSACK problem which may be of independent interest. We complement our theoretical findings by extensive experimental evaluations on realistic data sets.
Knowledge compilation has proven effective in (weighted) model counting, uniquely supporting incrementality and checkability. For incrementality, compiling an input formula once suffices to answer multiple queries, thus reducing the total solving effort. For checkability, the compiled formula is amenable to producing machine-checkable proofs for verification, thus strengthening the solver’s reliability. In this work, we extend knowledge compilation from model counting to stochastic Boolean satisfiability (SSAT) solving by generalizing the dec-DNNF representation to accommodate the SSAT quantifier structure and integrate it into SharpSSAT, a state-of-the-art SSAT solver. We further study proof generation from the compiled representation and extend CPOG, a certified model-counting toolchain, to generate proofs for certifying the results of SharpSSAT. Experimental results show the benefits of the proposed knowledge compilation approach for SSAT in sharing computation efforts for multiple queries and producing checkable dec-DNNF logs with negligible overhead.
This paper addresses the challenge of solution counting for Quantified Boolean Formulas (QBFs), a task distinct from the well-established model counting problem for SAT (\#SAT). Unlike SAT, where models are straightforward assignments to Boolean variables, QBF solution counting involves tree models that capture dependencies among variables within different quantifier blocks. We present a comprehensive top-down tree model counter capable of handling diverse satisfiable QBF formulas. Emphasizing the critical role of the branching heuristic, which must consider variables in the correct order according to quantification blocks, we further demonstrate the importance of addressing connected components, free variables, and caching. Experimental results indicate that our proposed approach for counting tree models of QBF formulas is highly efficient in practice, surpassing existing state-of-the-art methods designed for this specific purpose.
This paper presents NgramMarkov, a variant of the Markov constraints. It is dedicated to text generation in constraint programming (CP). It involves a set of n-grams (i.e., sequence of n words) associated with probabilities given by a large language model (LLM). It limits the product of the probabilities of the n-gram of a sentence. The propagator of this constraint can be seen as an extension of the ElementaryMarkov constraint propagator, incorporating the LLM distribution instead of the maximum likelihood estimation of n-grams. It uses a gliding threshold, i.e., it rejects n-grams whose local probabilities are too low, to guarantee balanced solutions. It can also be combined with a "look-ahead" approach to remove n-grams that are very unlikely to lead to acceptable sentences for a fixed-length horizon. This idea is based on the MDDMarkovProcess constraint propagator, but without explicitly using an MDD (Multi-Valued Decision Diagram). The experimental results show that the generated text is valued in a similar way to the LLM perplexity function. Using this new constraint dramatically reduces the number of candidate sentences produced, improves computation times, and allows larger corpora or smaller n-grams to be used. A real-world problem has been solved for the first time using 4-grams instead of 5-grams.
Few-shot anomaly classification (FSAC) is a vital task in manufacturing industry. Recent methods focus on utilizing CLIP in zero/few normal shot anomaly detection instead of custom models. However, there is a lack of specific text prompts in anomaly classification and most of them ignore the modality gap between image and text. Meanwhile, there is distribution discrepancy between the pre-trained and the target data. To provide a remedy, in this paper, we propose a method to boost CLIP for few-normal-shot anomaly classification, dubbed CLIP-FSAC, which contains two-stage of training and alternating fine-tuning with two modality-specific adapters. Specifically, in the first stage, we train image adapter with text representation output from text encoder and introduce an image-to-text tuning to enhance multi-modal interaction and facilitate a better language-compatible visual representation. In the second stage, we freeze the image adapter to train the text adapter. Both of them are constrained by fusion-text contrastive loss. Comprehensive experiment results are provided for evaluating our method in few-normal-shot anomaly classification, which outperforms the state-of-the-art method by 12.2%, 10.9%, 10.4% AUROC on VisA for 1, 2, and 4-shot settings.
Jointly using text and sketch for scene-level image retrieval utilizes the complementary between text and sketch to describe the fine-grained scene content and retrieve the target image, which plays a pivotal role in accurate image retrieval. Existing methods directly fuse the features of sketch and text and thus suffer from the bottleneck of limited utilization for crucial semantic and structural information, leading to inaccurate matching with images. In this paper, we propose SceneDiff, a novel retrieval network that leverages a pre-trained diffusion model to establish a shared generative latent space, enabling a joint latent representation learning for both sketch and text features and precise alignment with the corresponding image. Specifically, we encode text, sketch and image features, and project them into the diffusion-based share space, conditioning the denoising process on sketch and text features to generate latent fusion features, while employing the pre-trained autoencoder for latent image features. Within this space, we introduce the content-aware feature transformation module to reconcile encoded sketch and image features with the diffusion latent space's dimensional requirements and preserve their visual content information. Then we augment the representation capability of the generated latent fusion features by integrating multiple samplings with partition attention, and utilize contrastive learning to align both direct fusion features and generated latent fusion features with corresponding image representations. Our method outperforms the state-of-the-art works through extensive experiments, providing a novel insight into the related retrieval field.
Vision-language models (VLMs) show promising generalization and zero-shot capabilities, offering a potential solution to the impracticality and cost of enabling robots to comprehend diverse human instructions and scene semantics in the real world. Existing approaches most directly integrate the semantic representations from pre-trained VLMs with policy learning. However, these methods are limited to the labeled data learned, resulting in poor generalization ability to unseen instructions and objects. To address the above limitation, we propose a simple method called "Attention before Manipulation" (ABM), which fully leverages the object knowledge encoded in CLIP to extract information about the target object in the image. It constructs an Object Mask Field, serving as a better representation of the target object for the model to separate visual grounding from action prediction and acquire specific manipulation skills effectively. We train ABM for 8 RLBench tasks and 2 real-world tasks via behavior cloning. Extensive experiments show that our method significantly outperforms the baselines in the zero-shot and compositional generalization experiment settings.
Class-consistent Contrastive Learning Driven Cross-dimensional Transformer for 3D Medical Image Classification
PDF ↗Transformer emerges as an active research topic in medical image analysis. Yet, three substantial challenges limit the effectiveness of both 2D and 3D Transformers in 3D medical image classification: 1) Challenge in capturing spatial structure correlation due to the unreasonable flattening operation; 2) Challenge in burdening the high computational complexity and memory consumption due to the quadratic growth of computational complexity and memory consumption for 3D medical data; 3) Challenge in discriminative representation learning, due to data-sensitivity. To address the above challenges, a novel Cross-dimensional Transformer (CdTransformer) and a creative Class-consistent Contrastive Learning (CcCL) are proposed. Specifically, CdTransformer consists of two novel modules: 1) Cross-dimensional Attention Module (CAM), which breaks the limitation that Transformer cannot reasonably establish spatial structure correlation when meeting 3D medical data, meanwhile, reduces the computational complexity and memory consumption. 2) Inter-dimensional Feed-forward Network (IdFN), which addresses the challenge of traditional feed-forward networks not being able to learn depth dimension information that is unique to 3D medical data. CcCL innovatively takes full advantage of the inter-class and intra-class features from the slice-distorted samples to boost Transformer in learning feature representation. CdTransformer and CcCL are validated on six 3D medical image classification tasks. Extensive experimental results demonstrate that CdTransformer outperforms state-of-the-art CNNs and Transformers on 3D medical image classification, and CcCL enables significantly improving Transformer in discriminative representation learning.
Box supervised instance segmentation (BSIS) aims to achieve an effective trade-off between annotation costs and model performance by solely relying on bounding box annotations during training process. However, we observe that BSIS model is bottlenecked by the intricate objective under limited guidance, and tends to sacrifice segmentation capability in order to effectively recognize multiple instances. To boost the BSIS model's perceptual ability for object shape and contour, we introduce MISA, that is, MIning Saliency-Aware semantic prior from a well-optimized box supervised semantic segmentation (BSSS) network, and incorporating cross-model guidance into the learning process of BSIS. Specifically, we first design a Frequency-Space Distillation (FSD) module to extract assorted salient prior knowledge from BSSS model, and perform cross-model alignment for transfering the prior to BSIS model. Furthermore, we introduce Semantic-Enhanced Pairwise Affinity (SEPA), which borrows the object perceptual ability of BSSS model to emphasize the contribution of salient objects for pairwise affinity, providing more accurate guidance for the BSIS network. Extensive experiments show that our proposed MISA consistently surpasses the existing state-of-the-art methods by a large margin in the BSIS scenario.
Image-to-video (I2V) generation aims to create a video sequence from a single image, which requires high temporal coherence and visual fidelity. However, existing approaches suffer from inconsistency of character appearances and poor preservation of fine details. Moreover, they require a large amount of video data for training, which can be computationally demanding. To address these limitations, we propose PoseAnimate, a novel zero-shot I2V framework for character animation. PoseAnimate contains three key components: 1) a Pose-Aware Control Module (PACM) that incorporates diverse pose signals into text embeddings, to preserve character-independent content and maintain precise alignment of actions. 2) a Dual Consistency Attention Module (DCAM) that enhances temporal consistency and retains character identity and intricate background details. 3) a Mask-Guided Decoupling Module (MGDM) that refines distinct feature perception abilities, improving animation fidelity by decoupling the character and background. We also propose a Pose Alignment Transition Algorithm (PATA) to ensure smooth action transition. Extensive experiment results demonstrate that our approach outperforms the state-of-the-art training-based methods in terms of character consistency and detail fidelity. Moreover, it maintains a high level of temporal coherence throughout the generated animations.
Zero-shot sketch-based image retrieval (ZS-SBIR), aiming to recognize and retrieve relevant photos based on freehand sketch queries that belong to unseen categories in the search set, has sparked considerable interest, benefiting from the rapid advancements in multimodal learning and feature representation research. Despite the recent improvements in performance, there are still rooms for refining feature representation and thus enhancing the generalization capabilities of the models. Most of the existing research efforts have primarily focused on learning the feature distribution of modalities within specific datasets, without considering the broader dataset-agnostic `population distribution' of relevant modalities. In this paper, we investigate the modality population distribution and apply such knowledge to guide feature learning. Specifically, we propose a modality capacity constraint loss to control the learning of population distribution for sketches and photos. This loss can be effectively combined with retrieval loss (e.g., triplet loss) or classification loss (e.g., InfoNCE loss) to enhance the performance of ZS-SBIR, through the fine-tuning process of pre-trained models like CLIP and DINO. Extensive experiment results have demonstrated our significant performance improvements, achieving an increase of 7.3%/3.2% and 19.9%/10.3% in terms of mAP@200/P@200 compared to the state-of-the-art models on CLIP and DINO, respectively, on the Sketchy-ext dataset (split 2). Data, code, and supplementary information are available at https://github.com/YHdian0716/ZS-SBIR-MCC.git.
Action Quality Assessment (AQA) is pivotal for quantifying actions across domains like sports and medical care. Existing methods often rely on pre-trained backbones from large-scale action recognition datasets to boost performance on smaller AQA datasets. However, this common strategy yields suboptimal results due to the inherent struggle of these backbones to capture the subtle cues essential for AQA. Moreover, fine-tuning on smaller datasets risks overfitting. To address these issues, we propose Coarse-to-Fine Instruction Alignment (CoFInAl). Inspired by recent advances in large language model tuning, CoFInAl aligns AQA with broader pre-trained tasks by reformulating it as a coarse-to-fine classification task. Initially, it learns grade prototypes for coarse assessment and then utilizes fixed sub-grade prototypes for fine-grained assessment. This hierarchical approach mirrors the judging process, enhancing interpretability within the AQA framework. Experimental results on two long-term AQA datasets demonstrate CoFInAl achieves state-of-the-art performance with significant correlation gains of 5.49% and 3.55% on Rhythmic Gymnastics and Fis-V, respectively. Our Code is available at https://github.com/ZhouKanglei/CoFInAl_AQA.
Recently, scene text recognition (STR) models have shown significant performance improvements. However, existing models still encounter difficulties in recognizing challenging texts that involve factors such as severely distorted and perspective characters. These challenging texts mainly cause two problems: (1) Large Intra-Class Variance. (2) Small Inter-Class Variance. An extremely distorted character may prominently differ visually from other characters within the same category, while the variance between characters from different classes is relatively small. To address the above issues, we propose a novel method that enriches the character features to enhance the discriminability of characters. Firstly, we propose the Character-Aware Constraint Encoder (CACE) with multiple blocks stacked. CACE introduces a decay matrix in each block to explicitly guide the attention region for each token. By continuously employing the decay matrix, CACE enables tokens to perceive morphological information at the character level. Secondly, an Intra-Inter Consistency Loss (I^2CL) is introduced to consider intra-class compactness and inter-class separability at feature space. I^2CL improves the discriminative capability of features by learning a long-term memory unit for each character category. Trained with synthetic data, our model achieves state-of-the-art performance on common benchmarks (94.1% accuracy) and Union14M-Benchmark (61.6% accuracy). Code is available at https://github.com/bang123-box/CFE.
Few-Shot Learning (FSL) aims to train a model that can generalize to recognize new classes, with each new class having only very limited training samples. Since extracting discriminative features for new classes with few samples is challenging, existing FSL methods leverage visual and semantic prior knowledge to guide discriminative feature learning. However, for meta-learning purposes, the semantic knowledge of the query set is unavailable, so their features lack discriminability. To address this problem, we propose a novel Multi-Attention based Visual-Semantic Interaction (MAVSI) approach for FSL. Specifically, we utilize spatial and channel attention mechanisms to effectively select discriminative visual features for the support set based on its ground-truth semantics while using all the support set semantics for each query set sample. Then, a relation module with class prototypes of the support set is employed to supervise and select discriminative visual features for the query set. To further enhance the discriminability of the support set, we introduce a visual-semantic contrastive learning module to promote the similarity between visual features and their corresponding semantic features. Extensive experiments on four benchmark datasets demonstrate that our proposed MAVSI could outperform existing state-of-the-art FSL methods.
Human-AI interactivity is a critical aspect that reflects the usability of Multimodal Large Language Models (MLLMs). However, existing end-to-end MLLMs only allow users to interact with them through language instructions, leading to the limitation of the interactive accuracy and efficiency. In this study, we present precise referring instructions that utilize diverse reference representations such as points and boxes as referring prompts to refer to the special region. This enables MLLMs to focus on the region of interest and achieve finer-grained interaction. Based on precise referring instruction, we propose ChatSpot, a unified end-to-end MLLM that supports diverse forms of interactivity including mouse clicks, drag-and-drop, and drawing boxes, which provides a more flexible and seamless interactive experience. We also construct a multi-grained vision-language instruction-following dataset based on existing datasets and GPT-4 generating. Furthermore, we design a series of evaluation tasks to assess the effectiveness of region recognition and interaction. Experimental results showcase ChatSpot's promising performance. Project page: https://github.com/Ahnsun/ChatSpot.
Great advancements in semantic, instance, and panoptic segmentation have been made in recent years, yet the top-performing models remain vulnerable to imperceptible adversarial perturbation. Current attacks on segmentation primarily focus on a single task, and these methods typically rely on iterative instance-specific strategies, resulting in limited attack transferability and low efficiency. In this paper, we propose GenSeg, a Generative paradigm that creates unified adversaries for Segmentation tasks. In particular, we propose an intermediate-level objective to enhance attack transferability, including a mutual agreement loss for feature deviation, and a prototype obfuscating loss to disrupt intra-class and inter-class relationships. Moreover, GenSeg crafts an adversary in a single forward pass, significantly boosting the attack efficiency. Besides, we unify multiple segmentation tasks to GenSeg in a novel category-and-mask view, which makes it possible to attack these segmentation tasks within this unified framework, and conduct cross-domain and cross-task attacks as well. Extensive experiments demonstrate the superiority of GenSeg in black-box attacks compared with state-of-the-art attacks. To our best knowledge, GenSeg is the first approach capable of conducting cross-domain and cross-task attacks on segmentation tasks, which are closer to real-world scenarios.
Compositional Zero-Shot Learning (CZSL) aims to recognize unseen compositions with the knowledge learned from seen compositions, where each composition is composed of two primitives (attribute and object). However, existing CZSL methods are designed to learn compositions from fixed primitive set, which cannot handle the continually expanding primitive set in real-world applications. In this paper, we propose a new CZSL setting, named Continual Compositional Zero-Shot Learning (CCZSL), which requires the model to recognize unseen compositions composed of learned primitive set while continually increasing the size of learned primitive set. Contextuality and catastrophic forgetting are the main issues to be addressed in this setting. Specifically, we capture similar contextuality in compositions through several learnable Super-Primitives that can modify the invariant primitive embedding to better adapt the contextuality in the corresponding composition. Then we introduce a dual knowledge distillation loss which aims at maintaining old knowledge learned from previous sessions and avoiding overfitting of new session. We design the CCZSL evaluation protocol and conduct extensive experiments on widely used benchmarks, demonstrating the superiority of our method compared to the state-of-the-art CZSL methods.
Adversarial robustness and interpretability are longstanding challenges of computer vision. Deep neural networks are vulnerable to adversarial perturbations that are incomprehensible and imperceptible to humans. However, the opaqueness of networks prevents one from theoretically addressing adversarial robustness. As a human-comprehensible approach, the frequency perspective has been adopted in recent works to investigate the properties of neural networks and adversarial examples. In this paper, we investigate the frequency properties of feature extraction and analyze the stability of different frequency features when attacking different frequencies. Therefore, we propose an attack method, F-PGD, based on the projected gradient descent to attack the specified frequency bands. Utilizing this method, we find many intriguing properties of neural networks and adversarial perturbations. We experimentally show that contrary to the low-frequency bias of neural networks, the effective features of the same class are distributed across all frequency bands. Meanwhile, the high-frequency features often dominate when the neural networks make conflicting decisions on different frequency features. Furthermore, the attack experiments show that the low-frequency features are more robust to the attacks on different frequencies, but the interference to the high frequencies makes the network unable to make the right decision. These properties indicate that the decision-making process of neural networks tends to use as few low-frequency features as possible and cannot integrate features of different frequencies.
Evaluating the performance of Multi-modal Large Language Models (MLLMs), integrating both point cloud and language, presents significant challenges. The lack of a comprehensive assessment hampers determining whether these models truly represent advancements, thereby impeding further progress in the field. Current evaluations heavily rely on classification and caption tasks, falling short in providing a thorough assessment of MLLMs. A pressing need exists for a more sophisticated evaluation method capable of thoroughly analyzing the spatial understanding and expressive capabilities of these models. To address these issues, we introduce a scalable 3D benchmark, accompanied by a large-scale instruction-tuning dataset known as 3DBench, providing an extensible platform for a comprehensive evaluation of MLLMs. Specifically, we establish the benchmark that spans a wide range of spatial and semantic scales, from object-level to scene-level, addressing both perception and planning tasks. Furthermore, we present a rigorous pipeline for automatically constructing scalable 3D instruction-tuning datasets, covering 10 diverse multi-modal tasks with more than 0.23 million QA pairs generated in total. Thorough experiments evaluating trending MLLMs, comparisons against existing datasets, and variations of training protocols demonstrate the superiority of 3DBench, offering valuable insights into current limitations and potential research directions. Codes are available at https://github.com/Inshsang/3DBench.