United States

We introduce a neural network conformal prediction method for time series that enhances adaptivity in non-stationary environments, improves consistency in prediction intervals, and facilitates few-shot learning. Unlike existing methods, our approach  incorporates auxiliary multi-view data with neural network encoders in an end-to-end fashion and seamlessly integrate constraints on prediction intervals to meet consistency requirements from domain experts and decision-makers, and it leverages data from related tasks to improve few-shot learning performance. We prove that our method maintains long-term coverage guarantees and, using real-world datasets from epidemics, electric demand, weather, and others, we empirically demonstrate improvements in coverage and probabilistic accuracy of up to 51% and 64%, respectively. Additionally, our method is the only one that combines good calibration with consistency in prediction intervals.

AAAI 2025

Neural Conformal Control for Time Series Forecasting

quantification

uncertainty

calibration

We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

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-25 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.

### [Invited Speakers](https://aaai.org/conference/aaai/aaai-25/aaai-25-invited-speakers/)

Register [here](https://aaai.org/conference/aaai/aaai-25/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. AAAI-25 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.



poster

The goal of image-based virtual try-on is to generate an image of the target person naturally wearing the given clothing. However, existing methods solely focus on the frontal try-on using the frontal clothing. When the views of the clothing and person are significantly inconsistent, particularly when the person's view is non-frontal, the results are unsatisfactory. To address this challenge, we introduce Multi-View Virtual Try-ON (MV-VTON), which aims to reconstruct the dressing results from multiple views using the given clothes. Given that single-view clothes provide insufficient information for MV-VTON, we instead employ two images, i.e., the frontal and back views of the clothing, to encompass the complete view as much as possible. Moreover, we adopt diffusion models that have demonstrated superior abilities to perform our MV-VTON. In particular, we propose a view-adaptive selection method where hard-selection and soft-selection are applied to the global and local clothing feature extraction, respectively. This ensures that the clothing features are roughly fit to the person's view. Subsequently, we suggest joint attention blocks to align and fuse clothing features with person features. Additionally, we collect a MV-VTON dataset MVG, in which each person has multiple photos with diverse views and poses. Experiments show that the proposed method not only achieves state-of-the-art results on MV-VTON task using our MVG dataset, but also has superiority on frontal-view virtual try-on task using VITON-HD and DressCode datasets. The code and dataset will be publicly available.

MV-VTON: Multi-View Virtual Try-On with Diffusion Models

Gaussian Splatting has emerged as a prominent 3D representation in novel view synthesis, but it still suffers from appearance variations, which are caused by various factors, such as modern camera ISPs, different time of day, weather conditions, and local light changes. These variations can lead to floaters and color distortions in the rendered images/videos. Recent appearance modeling approaches in Gaussian Splatting are either tightly coupled with the rendering process, hindering real-time rendering, or they only account for mild global variations, performing poorly in scenes with local light changes. In this paper, we propose DAVIGS, a method that decouples appearance variations in a plug-and-play and efficient manner. By transforming the rendering results at the image level instead of the Gaussian level, our approach can model appearance variations with minimal optimization time and memory overhead. Furthermore, our method gathers appearance-related information in 3D space to transform the rendered images, thus building 3D consistency across views implicitly. We validate our method on several appearance-variant scenes, and demonstrate that it achieves state-of-the-art rendering quality with minimal training time and memory usage, without compromising rendering speeds. Additionally, it provides performance improvements for different Gaussian Splatting baselines in a plug-and-play manner.

Decoupling Appearance Variations with 3D Consistent Features in Gaussian Splatting

3D scene understanding is an important task, and there has been a recent surge of research interest in aligning 3D representations of point clouds with text to empower embodied AI. However, due to the lack of comprehensive 3D benchmarks, the capabilities of 3D models in real-world scenes, particularly those that are challenging with subtly distinguished objects, remain insufficiently investigated. To facilitate a more thorough evaluation of 3D models' capabilities, we propose a scheme, ObjVariantEnsemble, to systematically introduce more scenes with specified object classes, colors, shapes, quantities, and spatial relationships to meet model evaluation needs. More importantly, we intentionally construct scenes with similar objects to a certain degree and design an LLM-VLM-cooperated annotator to capture key distinctions as annotations. The resultant benchmark can better challenge 3D models, reveal their shortcomings in understanding, and potentially aid in the further development of 3D models.

ObjVariantEnsemble: Advancing Point Cloud LLM Evaluation in Challenging Scenes with Subtly Distinguished Objects

Existing knowledge distillation (KD) studies for streaming automatic speech recognition (ASR) adopt a non-streaming model as the teacher and a streaming model as the student, respectively.
Since the non-streaming teacher usually has less emission latency compared to the streaming student, the teacher's prediction is typically shifted by $\tau$ frames, where the parameter $\tau$ is selected heuristically.
In this paper, we observe that this manual shifting is sub-optimal and propose a novel framework, namely Heuristic-free KD.
Instead of leveraging knowledge from the non-streaming teacher model, we employ a self-distillation setup, distilling the knowledge within the streaming architecture itself.
Since the teacher and student share the same streaming ASR backbone, the alignment mismatch issue can be effectively mitigated without requiring any time shifting by $\tau$.
Additionally, we incorporate full-context textual information as an auxiliary multi-modal input for the proposed teacher.
Although the streaming architecture lacks future context, the additional linguistic input enables it to generate more accurate knowledge for self-distillation.
We empirically demonstrate that the proposed KD approach significantly improves the performance of the streaming ASR model, outperforming conventional methods that rely on the offline teacher and heuristic parameter.

Heuristic-free Knowledge Distillation for Streaming ASR via Multi-modal Training

Conventional multi-source domain few-shot adaptation (MFDA) faces the challenge of further reducing edge-side device load in low-resource scenarios. Considering the native language-supervised advantage of CLIP and the plug-and-play nature of prompt to transfer CLIP efficiently, this paper introduces an uploadable multi-source few-shot domain adaptation (UMFDA) schema. It belongs to a decentralized edge collaborative learning in the edge-side models that must maintain low computational and transmission loads, where only a limited amount of annotations in source domain data is provided, with most of the data being annotated. Further, this paper proposes a vision-aware multimodal prompt tuning framework (VAMP) under the decentralized schema, where the vision-aware prompt guides the text domain-specific prompt to maintain semantic discriminability and perceive the domain information. The cross-modal semantic and domain distribution alignment losses optimize each edge-side model, while text classifier consistency and semantic diversity losses promote collaborative learning among edge-side models. Extensive experiments were conducted on OfficeHome and DomainNet datasets, outperforming the previous prompt tuning methods in the UMFDA. It demonstrates the effectiveness and significance of the proposed VAMP framework. The code is publicly available at https://anonymous.4open.science/r/VAMP-UMFDA-38FE.

Vision-aware Multimodal Prompt Tuning for Uploadable Multi-source Few-shot Domain Adaptation

As a strategy for sustainability of deep learning, reusing an existing model by retraining it rather than training a new model from scratch is critical. In this paper, we propose REpresentation Shift QUantifying Estimator (ReSQuE), a predictive quantifier to estimate the retraining cost of a model to distributional shifts or change of tasks. It provides a single concise index for an estimate of resources required for retraining the model. Through extensive experiments, we show that ReSQuE has strong correlation with various retraining measures. Our results validate that ReSQuE is an effective indicator in terms of epochs, gradient norms, changes of parameter magnitude, energy, and carbon emissions. These measures align well with ReSQuE for new tasks, multiple noise types, and varying noise intensities. As a result, ReSQuE enables users to make informed decisions for retraining to different tasks/distribution shifts and determine the most cost-effective and sustainable option, allowing for the reuse of a model with a much smaller footprint in the environment.

RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability

3D super-resolution aims to reconstruct high-fidelity 3D models from low-resolution (LR) multi-view images. Early studies primarily focused on single-image super-resolution (SISR) models to upsample LR images into high-resolution images. However, these methods often lack view consistency because they operate independently on each image. Although various post-processing techniques have been extensively explored to mitigate these inconsistencies, they have yet to fully resolve the issues. In this paper, we perform a comprehensive study of 3D super-resolution by leveraging video super-resolution (VSR) models. By utilizing VSR models, we ensure a higher degree of spatial consistency and can reference surrounding spatial information, leading to more accurate and detailed reconstructions. Our findings reveal that VSR models can perform remarkably well even on sequences that lack precise spatial alignment. Given this observation, we propose a simple yet practical approach to align LR images without involving fine-tuning or generating `smooth' trajectory from the trained 3D models over LR images. The experimental results show that the surprisingly simple algorithms can achieve the state-of-the-art results of 3D super-resolution tasks on standard benchmark datasets, such as the NeRF-synthetic and Mip-NeRF 360 datasets.

Sequence Matters: Harnessing Video Models in 3D Super-Resolution

Partially Relevant Video Retrieval (PRVR) addresses the challenges of text-to-video retrieval in real-world scenarios where untrimmed videos are prevalent.
Traditional PRVR methods encode videos at two feature scales: (1) frame-level to capture fine details, and (2) clip-level to recognize broader content.
However, these approaches align both scales with a single sentence representation, leading to suboptimal performance. 
In particular, we point out the level mismatch in aligning frame-level video features with a sentence representation, as the entire meaning of a sentence contains broader and more diverse content than what frame-level features can encode.
This misalignment causes frame-level features to capture broader contexts and overlook local fine details.
To tackle this issue, we propose a framework that represents a sentence as a set of multiple components, where each component aligns with frame-level semantics.
Specifically, we introduce Semantic-Decomposed Matching (SDM) to adjust the granularity of the text description to match them with frame-level video features. 
In addition to the matching process, we develop the Adaptive Local Aggregator (ALA) to enhance video encoding in capturing finer local details, ensuring precise text-video alignment at the frame level.
ALA adaptively integrates multi-scale local details within short temporal spans obtained by enforcing a strict temporal aggregation range.
Finally, we reinforce detailed encoding at the frame level with newly designed objectives for both modalities.
Extensive experiments integrating our framework with existing clip branches demonstrate its effectiveness and applicability, highlighting significant improvements in PRVR performance.

Bridging the Semantic Granularity Gap Between Text and Frame Representations for Partially Relevant Video Retrieval

In science and engineering, machine learning techniques are increasingly successful in physical systems modeling (predicting future states of physical systems). Effectively integrating PDE loss as a constraint of system transition can improve the model's prediction by overcoming generalization issues due to data scarcity, especially when data acquisition is costly. However, in many real-world scenarios, due to sensor limitations, the data we can obtain is often only partial observation, making the calculation of PDE loss seem to be infeasible, as the PDE loss heavily relies on high-resolution states. We carefully study this problem and propose a novel framework named Re-enable PDE Loss under Partial Observation (RPLPO). The key idea is that although enabling PDE loss to constrain system transition solely is infeasible, we can re-enable PDE loss by reconstructing the learnable high-resolution state and constraining system transition simultaneously. Specifically, RPLPO combines an encoding module for reconstructing learnable high-resolution states with a transition module for predicting future states. The two modules are jointly trained by data and PDE loss. We conduct experiments in various physical systems to demonstrate that RPLPO has significant improvement in generalization, even when observation is sparse, irregular, noisy, and PDE is inaccurate.

How to Re-enable PDE Loss for Physical Systems Modeling Under Partial Observation

Noisy labels are both inevitable and problematic in machine learning methods, as they negatively impact models' generalization ability by causing overfitting. In the context of learning with noise, the transition matrix plays a crucial role in designing statistically consistent algorithms. However, the transition matrix is often considered unidentifiable. One strand of methods typically address this problem by assuming that the transition matrix is instance-independent; that is, the probability of mislabeling a particular instance is not influenced by its characteristics or attributes. This assumption is clearly invalid in complex real-world scenarios. To better understand the transition relationship and relax this assumption, we propose to study the generation process of noisy labels from a causal perspective. We discover that an unobservable latent variable can affect either the instance itself, the label annotation procedure, or both, which complicates the identification of the transition matrix. To address various scenarios, we have unified these observations within a new causal graph. this graph, the input instance is divided into a noise-resistant component and a noise-sensitive component based on whether they are affected by the latent variable. These two components contribute to identifying the ``causal transition matrix,'' which approximates the true transition matrix with a theoretical guarantee. In line with this, we have designed a novel training framework that explicitly models this causal relationship. As a result, our framework achieves a more accurate model for inferring the clean label.

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