Canada

Recent advancements in single-stage Panoptic Narrative Grounding (PNG) have demonstrated significant potential. These methods predict pixel-level masks by directly matching pixels and phrases. However, they often neglect the modeling of semantic and visual relationships between phrase-level instances, limiting their ability for complex multi-modal reasoning in PNG. To tackle this issue, we propose XPNG, a &quot;differentiation-refinement-localization&quot; reasoning paradigm for accurately locating instances or regions. In XPNG, we introduce a Semantic Context Convolution (SCC) module to leverage semantic priors for generating distinctive features. This well-crafted module employs a combination of dynamic channel-wise convolution and pixel-wise convolution to embed semantic information and establish inter-object relationships guided by semantics. Subsequently, we propose a Visual Context Verification (VCV) module to provide visual cues, eliminating potential biases introduced by SCC and further refining the visual features generated by the previous module. Extensive experiments on PNG benchmark datasets reveal that our approach achieves state-of-the-art performance, significantly outperforming existing methods by a considerable margin and yielding a 3.9-point improvement in overall metrics. The source code will be available in the supplementary materials and released to the public.

AAAI 2024

Improving Panoptic Narrative Grounding by Harnessing Semantic Relationships and Visual Confirmation

cv: language and vision cv: multi-modal vision cv: segmentation

Recent advancements in single-stage Panoptic Narrative Grounding (PNG) have demonstrated significant potential. These methods predict pixel-level masks by directly matching pixels and phrases. However, they often neglect the modeling of semantic and visual relationships between phrase-level instances, limiting their ability for complex multi-modal reasoning in PNG. To tackle this issue, we propose XPNG, a "differentiation-refinement-localization" reasoning paradigm for accurately locating instances or regions. In XPNG, we introduce a Semantic Context Convolution (SCC) module to leverage semantic priors for generating distinctive features. This well-crafted module employs a combination of dynamic channel-wise convolution and pixel-wise convolution to embed semantic information and establish inter-object relationships guided by semantics. Subsequently, we propose a Visual Context Verification (VCV) module to provide visual cues, eliminating potential biases introduced by SCC and further refining the visual features generated by the previous module. Extensive experiments on PNG benchmark datasets reveal that our approach achieves state-of-the-art performance, significantly outperforming existing methods by a considerable margin and yielding a 3.9-point improvement in overall metrics. The source code will be available in the supplementary materials and released to the public.

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. 

In this paper we study the computational complexity of several reasoning tasks centered around the bounded plan existence problem. We do this for standard classical planning and hierarchical task network (HTN) planning and each for a grounded and a lifted representation. Whereas bounded plan existence complexity is known for classical planning, it has not yet been studied for HTN planning. For plan verification, results were available for both formalisms except for the lifted HTN planning. We will present lower and upper bounds of the complexity of plan verification in lifted HTN planning and provide novel insights into its grounded counterpart, in which we show that verification is not just NP-complete in the general case, but already for a severely restricted special case. Finally, we show the complexity concerning verifying the optimality of a given plan and discuss its connection to the bounded plan existence problem.

On the Computational Complexity of Plan Verification, (Bounded) Plan-Optimality Verification, and Bounded Plan Existence

Model-based offline reinforcement learning (RL) algorithms have emerged as a promising paradigm for offline RL.
These algorithms usually learn a dynamics model from a static dataset of transitions, use the model to generate synthetic trajectories, and perform conservative policy optimization within these trajectories. 
However, our observations indicate that policy optimization methods used in these model-based offline RL algorithms are not effective at exploring the learned model and induce biased exploration, which ultimately impairs the performance of the algorithm.
To address this issue, we propose Offline Conservative ExplorAtioN (OCEAN),  a novel rollout approach to model-based offline RL.
In our method, we incorporate additional exploration techniques and introduce three conservative constraints based on uncertainty estimation to mitigate the potential impact of significant dynamic errors resulting from exploratory transitions. 
Our work is a plug-in method and can be combined with classical model-based RL algorithms, such as MOPO, COMBO, and RAMBO.
Experiment results of our method on the D4RL MuJoCo benchmark show that OCEAN significantly improves the performance of existing algorithms.

OCEAN-MBRL: Offline Conservative Exploration for Model-Based Offline Reinforcement Learning

Blind face restoration under extreme conditions involves reconstructing high-quality face images from severely degraded inputs. These input images are often in poor quality and have extreme facial poses, leading to errors in facial structure and unnatural artifacts within the restored images. In this paper, we show that utilizing 3D priors effectively compensates for structure knowledge deficiencies in 2D priors while preserving the texture details. Based on this, we introduce FREx (Face Restoration under Extreme conditions) that combines structure-accurate 3D priors and texture-rich 2D priors in pretrained generative networks for blind face restoration under extreme conditions. To fuse the different information in 3D and 2D priors, we introduce an adaptive weight module that adjusts the importance of features based on the input image's condition. With this approach, our model can restore structure-accurate and natural-looking faces even when the images have lost a lot of information due to degradation and extreme pose. Extensive experimental results on synthetic and real-world datasets validate the effectiveness of our methods.

Blind Face Restoration under Extreme Conditions: Leveraging 3D-2D Prior Fusion for Superior Structural and Texture Recovery

Among existing Neural Architecture Search methods, DARTS is known for its efficiency and simplicity. This approach applies continuous relaxation of network representation to construct a weight-sharing supernet and enables the identification of excellent subnets in just a few GPU days. However, performance collapse in DARTS results in deteriorating architectures filled with parameter-free operations and remains a great challenge to the robustness. To resolve this problem, we reveal that the fundamental reason is the biased estimation of the candidate importance in the search space through theoretical and experimental analysis, and more precisely select operations via information-based measurements. Furthermore, we demonstrate that the excessive concern over the supernet and inefficient utilization of data in bi-level optimization also account for suboptimal results. We adopt a more realistic objective focusing on the performance of subnets and simplify it with the help of the informationbased measurements. Finally, we explain theoretically why progressively shrinking the width of the supernet is necessary and reduce the approximation error of optimal weights in DARTS. Our proposed method, named IS-DARTS, comprehensively improves DARTS and resolves the aforementioned problems. Extensive experiments on NAS-Bench-201 and DARTS-based search space demonstrate the effectiveness of IS-DARTS.

IS-DARTS: Stabilizing DARTS through Precise Measurement on Candidate Importance

In this paper, we focus on the open-set panoptic segmentation (OPS) task to circumvent the data explosion problem. Different from the close-set setting, OPS targets to detect both known and unknown categories, where the latter is not annotated during training. Different from existing work that only selects a few common categories as unknown ones, we move forward to the real-world scenario by considering the various tail categories (~1k). To this end, we first build a new dataset with long-tail distribution for the OPS task. Based on this dataset, we additionally add a new class type for unknown classes and re-define the training annotations to make the OPS definition more complete and reasonable. Moreover, we analyze the influence of several significant factors in the OPS task and explore the upper bound of performance on unknown classes with different settings. Furthermore, based on the analyses, we design an effective two-phase framework for the OPS task, including thing-agnostic map generation and unknown segment mining. We further adopt semi-supervised learning to improve the OPS performance. Experimental results on different datasets validate the effectiveness of our method.

Revisiting Open-Set Panoptic Segmentation

Text-based Person Re-identification (T-ReID), which aims at retrieving a specific pedestrian image from a collection of images via text-based information, has received significant attention. However, previous research has overlooked a challenging yet practical form of T-ReID: dealing with image galleries mixed with occluded and inconsistent personal visuals, instead of ideal visuals with a full-body and clear view. Its major challenges lay in the insufficiency of benchmark datasets and the enlarged semantic gap incurred by arbitrary occlusions and modality gap between text description and visual representation of the target person. To alleviate these issues, we first design an Occlusion Generator (OGor) for the automatic generation of artificial occluded images from generic surveillance images. Then, a fine-granularity token selection mechanism is proposed to minimize the negative impact of occlusion for robust feature learning, and a novel multi-granularity contrastive consistency alignment framework is designed to leverage intra-/inter-granularity of visual-text representations for semantic alignment of occluded visuals and query texts. Experimental results demonstrate that our method exhibits superior performance. We believe this work could inspire the community to investigate more dedicated designs for implementing T-ReID in real-world scenarios. The source code is available at https://github.com/littlexinyi/MGCC.

Text-Based Occluded Person Re-identification via Multi-Granularity Contrastive Consistency Learning

The performance of deep learning models often degrades rapidly when faced with imbalanced data characterized by a long-tailed distribution. Researchers have found that the fully connected layer trained by cross-entropy loss has large weight-norms for classes with many samples, but not for classes with few samples. How to address the data imbalance problem with both the encoder and the classifier seems an under-researched problem. In this paper, we propose an inverse weight-balancing (IWB) approach to guide model training and alleviate the data imbalance problem in two stages. In the first stage, an encoder and classifier (the fully connected layer) are trained using conventional cross-entropy loss. In the second stage, with a fixed encoder, the classifier is finetuned through an adaptive distribution for IWB in the decision space. Unlike existing inverse image frequency that implements a multiplicative margin adjustment transformation in the classification layer, our approach can be interpreted as an adaptive distribution alignment strategy using not only the class-wise number distribution but also the sample-wise difficulty distribution in both encoder and classifier. Experiments show that our method can greatly improve performance on imbalanced datasets such as CIFAR100-LT with different imbalance factors, ImageNet-LT, and iNaturelists2018.

Inverse Weight-Balancing for Deep Long-Tailed Learning

Neural Radiance Fields (NeRF) have shown promise in generating realistic novel views from sparse scene images. However, existing NeRF approaches often encounter challenges due to the lack of explicit 3D supervision and imprecise camera poses, resulting in suboptimal outcomes. To tackle these issues, we propose AltNeRF---a novel framework designed to create resilient NeRF representations using self-supervised monocular depth estimation (SMDE) from monocular videos, without relying on known camera poses. SMDE in AltNeRF masterfully learns depth and pose priors to regulate NeRF training. The depth prior enriches NeRF's capacity for precise scene geometry depiction, while the pose prior provides a robust starting point for subsequent pose refinement. Moreover, we introduce an alternating algorithm that harmoniously melds NeRF outputs into SMDE through a consistence-driven mechanism, thus enhancing the integrity of depth priors. This alternation empowers AltNeRF to progressively refine NeRF representations, yielding the synthesis of realistic novel views. Extensive experiments showcase the compelling capabilities of AltNeRF in generating high-fidelity and robust novel views that closely resemble reality.

AltNeRF: Learning Robust Neural Radiance Field via Alternating Depth-Pose Optimization | VIDEO

Text-based audio generation models have limitations as they cannot encompass all the information in audio, leading to restricted controllability when relying solely on text. To address this issue, we propose a novel model that enhances the controllability of existing pre-trained text-to-audio models by incorporating additional conditions including content (timestamp) and style (pitch contour and energy contour) as supplements to the text. This approach achieves fine-grained control over the temporal order, pitch, and energy of generated audio. To preserve the diversity of generation, we employ a trainable control condition encoder that is enhanced by a large language model and a trainable Fusion-Net to encode and fuse the additional conditions while keeping the weights of the pre-trained text-to-audio model frozen. Due to the lack of suitable datasets and evaluation metrics, we consolidate existing datasets into a new dataset comprising the audio and corresponding conditions and use a series of evaluation metrics to evaluate the controllability performance. Experimental results demonstrate that our model successfully achieves fine-grained control to accomplish controllable audio generation. Audio samples and our dataset are publicly available\footnote{\url{https://conditionaudiogen.github.io/conditionaudiogen/}}.

Audio Generation with Multiple Conditional Diffusion Model | VIDEO

Multi-label few-shot aspect category detection (FS-ACD) is a challenging sentiment analysis task, which aims to learn a multi-label learning paradigm with limited training data. The difficulty of this task is how to use limited data to generalize effective discriminative representations for different categories. Nowadays, all advanced FS-ACD works utilize the prototypical network to learn label prototypes to represent different aspects. However, such point-based estimation methods are inherently noise-susceptible and bias-vulnerable. To this end, this paper proposes a novel Variational Hybrid-Attention Framework (VHAF) for the FS-ACD task. Specifically, to alleviate the data noise, we adopt a hybrid-attention mechanism to generate more discriminative aspect-specific embeddings. Then, based on these embeddings, we introduce the variational distribution inference to obtain the aspect-specific distribution as a more robust aspect representation, which can eliminate the scarce data bias for better inference. Moreover, we further leverage an adaptive threshold estimation to help VHAF better identify multiple relevant aspects. Extensive experiments on three datasets demonstrate the effectiveness of our VHAF over other state-of-the-art methods.

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