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This paper aims to design better generalized monocular depth estimation models. To this end, we have conducted quantitative analysis and discovered two important insights. First, the Simulation Correlation phenomenon, often found in long-tailed classification problem, also exists in monocular depth estimation, indicating that the imbalanced depth distribution in training data may be the cause of limited generalization ability. Second, the imbalanced and long-tailed distribution of depth values extends beyond the dataset scale, but also manifests within each individual image, further exacerbating the difficulty of monocular depth estimation. Motivated by the above findings, we propose the Distance-aware Multi-Expert (DME) depth estimation model. Unlike prior methods that handle different depth range indiscriminately, DME adopts a divide-and-conquer philosophy, where each expert is responsible for depth estimation of regions within a specific depth range. As such, the depth distribution seen by each expert is more uniform and can be more easily predicted. A router network is further designed and learned to stitch the prediction of all experts into the final depth map. Experiments show that DME achieves state-of-the-art performance on both NYU-Depth v2 and KITTI, and also delivers favorable zero-shot generalization capability on unseen datasets.

AAAI 2024

DME: Unveiling the Bias for Better Generalized Monocular Depth Estimation

scene analysis & understanding

vision for robotics & autonomous driving

3d computer vision

technical paper

We are pleased to announce the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), which will be held in Vancouver, British Columbia at the Vancouver Convention Centre – West Building from 20-27 February, 2024.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-24 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

We expect for AAAI-24 to be an in-person conference – one author of all accepted papers will be expected to present work in person unless there are exceptional circumstances that prevent this.<br><br><br><br>

In order to access the AAAI-24 event page you need to register [here](https://aaai.org/aaai-conference/registration/)

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. 

This study introduces an optimal mechanism in a dynamic stochastic knapsack environment. The model features a single seller who has a fixed quantity of a perfectly divisible item. Impatient buyers with a piece-wise linear utility function arrive randomly and they report the two-dimensional private information: marginal value and demanded quantity. We derive a revenue-maximizing dynamic mechanism in a finite discrete time framework that satisfies incentive compatibility, individual rationality, and feasibility conditions. This is achieved by characterizing buyers' utility and utilizing the Bellman equation. Moreover, we establish the essential penalty scheme for incentive compatibility, as well as the allocation and payment policies. Lastly, we propose algorithms to approximate the optimal policy, based on the Monte Carlo simulation-based regression method and reinforcement learning.

Optimal Mechanism in a Dynamic Stochastic Knapsack Environment

Generalization is very important for pose estimation, especially for 3D pose estimation where small changes in the 2D images could trigger structural changes in the 3D space. To achieve  generalization, the system needs to have the capability of detecting estimation errors by double-checking the projection coherence between the 3D and 2D spaces and adapting its network inference process based on this feedback. Current pose estimation is one-time feed-forward and lacks the capability to gather feedback and adapt the inference outcome. To address this problem, we propose to explore the concept of progressive inference where the network learns an observer to continuously detect the prediction error based on constraints matching, as well as an adjuster to refine its inference outcome based on these constraints errors. Within the context of 3D hand pose estimation, we find that this observer-adjuster design is relatively unstable since the observer is operating in the 2D image domain while the adjuster is operating in the 3D domain. To address this issue, we propose to construct two sets of observers-adjusters with complementary constraints from different perspectives. They  operate in a dynamic sequential manner controlled by a decision network to progressively improve the 3D pose estimation. We refer to this method as Cross-Constrained Progressive Inference (CCPI). Our extensive experimental results on FreiHAND and HO-3D benchmark datasets demonstrate that the proposed CCPI method is able to significantly improve the generalization capability and performance of 3D hand pose estimation.

Cross-Constrained Progressive Inference for 3D Hand Pose Estimation

This paper focuses in the inherent anchoring bias present in sequential reviews-sentiment corpora annotation processes. It proposes employing a limited subset of meticulously chosen reviews at the outset of the process, as a means of calibration, effectively mitigating the phenomenon. Through extensive experimentation we validate the phenomenon of sentiment bias in the annotation process and show that its magnitude can be influenced by pre-calibration. Furthermore, we show that the choice of the calibration set matters, hence the need for effective guidelines for choosing the reviews to be included in it. A comparison of annotators performance with the proposed calibration to annotation processes that do not use calibration or use a randomly-picked calibration set, reveals that indeed the calibration set picked is highly effective---it manages to substantially reduce the average absolute error compared to the other cases. Furthermore, the proposed selection guidelines are found to be highly robust in picking an effective calibration set also for domains different than the one based on which these rules were extracted.

Intelligent Calibration for Bias Reduction in Sentiment Corpora Annotation Process

It is well-known from probability theory that network-based methods like Bayesian networks constitute remarkable frameworks for efficient probabilistic reasoning. In this paper, we focus on qualitative default reasoning based on Spohn’s ranking functions for which network-based methods have not yet been studied satisfactorily. With constraint networks, we develop a framework for iterative calculations of c-representations, a family of ranking models of conditional belief bases which show outstanding properties from a commonsense and formal point of view, that are characterized by assigning possible worlds a degree of implausibility via penalizing the falsification of conditionals. Constraint networks unveil the dependencies among these penalty points (and hence among the conditionals) and make it possible to compute the penalty points locally on so-called safe sub-bases. As an application of our framework, we show that skeptical c-inferences can be drawn locally from safe sub-bases without losing validity.

Decomposing Constraint Networks for Calculating c-Representations | VIDEO

Adversarial examples are commonly created by solving a constrained optimization problem, typically using sign-based methods like Fast Gradient Sign Method (FGSM). These attacks can benefit from momentum with a constant parameter, such as Momentum Iterative FGSM (MI-FGSM), to enhance black-box transferability. However, the monotonic time-varying momentum parameter is required to guarantee convergence in theory, creating a theory-practice gap. Additionally, recent work shows that sign-based methods fail to converge to the optimum in several convex settings, exacerbating the issue. To address these concerns, we propose a novel method which incorporates both an innovative adaptive momentum parameter without monotonicity assumptions  and an adaptive step-size scheme that replaces the sign operation. Furthermore, we derive a regret upper bound for general convex functions. Experiments on multiple models demonstrate the efficacy of our method in generating adversarial examples with human-imperceptible noise while achieving high attack success rates, indicating its superiority over previous adversarial example generation methods.

On the Convergence of an Adaptive Momentum Method for Adversarial Attacks

Image hiding aims to conceal one or more secret images within a cover image of the same resolution. Due to strict capacity requirements, image hiding is commonly called large-capacity steganography. In this paper, we propose StegFormer, a novel autoencoder-based image-hiding model. StegFormer can conceal one or multiple secret images within a cover image of the same resolution while preserving the high visual quality of the stego image. In addition, to mitigate the limitations of current steganographic models in real-world scenarios, we propose a normalizing training strategy and a restrict loss to improve the reliability of the steganographic models under realistic conditions. Furthermore, we propose an efficient steganographic capacity expansion method to increase the capacity of steganography and enhance the efficiency of secret communication. Through this approach, we can increase the relative payload of StegFormer to 96 bits per pixel without any training strategy modifications. Experiments demonstrate that our StegFormer outperforms existing state-of-the-art (SOTA) models. In the case of single-image steganography, there is an improvement of more than 3 dB and 5 dB in PSNR for secret/recovery image pairs and cover/stego image pairs.

StegFormer: Rebuilding the Glory of Autoencoder-Based Steganography

This paper studies the problem of continual learning in an open-world scenario, referred to as Open-world Continual Learning (OwCL). OwCL is increasingly rising while it is highly challenging in two-fold: i) learning a sequence of tasks without forgetting knowns in the past, and ii) identifying unknowns (novel objects/classes) in the future. Existing OwCL methods suffer from the adaptability of task-aware boundaries between knowns and unknowns, and do not consider the mechanism of knowledge transfer. In this work, we propose Pro-KT, a novel prompt-enhanced knowledge transfer model for OwCL. Pro-KT includes two key components: (1) a prompt bank to encode and transfer both task-generic and task-specific knowledge, and (2) a task-aware open-set boundary to identify unknowns in the new tasks. Experimental results using two real-world datasets demonstrate that the proposed Pro-KT outperforms the state-of-the-art counterparts in both the detection of unknowns and the classification of knowns markedly.

Learning to Prompt Knowledge Transfer for Open-World Continual Learning

Despite significant advancements in multi-label text classification, the ability of existing models to generalize to novel and seldom-encountered complex concepts, which are compositions of elementary ones, remains underexplored. This research addresses this gap. By creating unique data splits across three benchmarks, we assess the compositional generalization ability of existing multi-label text classification models. Our results show that these models often fail to generalize to compositional concepts encountered infrequently during training, leading to inferior performance on tests with these new combinations. To address this, we introduce a data augmentation method that leverages two innovative text generation models designed to enhance the classification models' capacity for compositional generalization. Our experiments show that this data augmentation approach significantly improves the compositional generalization capabilities of classification models on our benchmarks, with both generation models surpassing other text generation baselines.

Compositional Generalization for Multi-Label Text Classification: A Data-Augmentation Approach

Low dimensional models like PCA are often used to simplify complex datasets by learning a single approximating subspace. This paradigm has expanded to union of subspaces models, like those learned by subspace clustering. In this paper, we present Principle Component Trees (PCTs), a graph structure that generalizes these ideas to identify mixtures of components that together describe the subspace structure of high-dimensional datasets. Each node in a PCT corresponds to a principle component of the data, and the edges between nodes indicate the components that must be mixed to produce a subspace that approximates a portion of the data. In order to construct PCTs, we propose two angle-distribution hypothesis tests to detect subspace clusters in the data. To analyze, compare, and select the best PCT model, we define two persistent homology measures that describe their shape. We show our construction yields two key properties of PCTs, namely ancestral orthogonality and non-decreasing singular values. Our main theoretical results show that learning PCTs reduces to PCA under multivariate normality, and that PCTs are efficient parameterizations of intersecting union of subspaces. Finally, we use PCTs to analyze neural network latent space, word embeddings, and reference image datasets.

Principle Component Trees and Their Persistent Homology

To better understand the output of deep neural networks (DNN), attribution based methods have been an important approach for model interpretability, which assign a score for each input dimension to indicate its importance towards the model outcome. Notably, the attribution methods use the ax- ioms of sensitivity and implementation invariance to ensure the validity and reliability of attribution results. Yet, the ex- isting attribution methods present challenges for effective in- terpretation and efficient computation. In this work, we in- troduce MFABA, an attribution algorithm that adheres to ax- ioms, as a novel method for interpreting DNN. Addition- ally, we provide the theoretical proof and in-depth analy- sis for MFABA algorithm, and conduct a large scale exper- iment. The results demonstrate its superiority by achieving over 101.5142 times faster speed than the state-of-the-art at- tribution algorithms. The effectiveness of MFABA is thor- oughly evaluated through the statistical analysis in compar- ison to other methods, and the full implementation package is open-source at: https://github.com/LMBTough/MFABA.

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DME: Unveiling the Bias for Better Generalized Monocular Depth Estimation

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