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Classical Transformer-based line segment detection methods have delivered impressive results. However, we observe that some accurately detected line segments are assigned low confidence scores during prediction, causing them to be ranked lower and potentially suppressed. Additionally, these models often require prolonged training periods to achieve strong performance, largely due to the necessity of bipartite matching. In this paper, we introduce RANK-LETR, a novel Transformer-based line segment detection method. Our approach leverages learnable geometric information to refine the ranking of predicted line segments by enhancing the confidence scores of high-quality predictions in a posterior verification step. We also propose a new line segment proposal method, wherein the feature point nearest to the centroid of the line segment directly predicts the location, significantly improving training efficiency and stability. Moreover, we introduce a line segment ranking loss to stabilize rankings during training, thereby enhancing the generalization capability of the model. Experimental results demonstrate that our method outperforms other Transformer-based and CNN-based approaches in prediction accuracy while requiring fewer training epochs than previous Transformer-based models.

AAAI 2025

Improving Transformer Based Line Segment Detection with Matched Predicting and Re-ranking

3d computer vision

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

AAAI 2022

poster

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.



Partially linear models (PLMs) have attracted much attention in the field of statistical machine learning. Specially, the ability of variable selection of PLMs has been studied extensively due to the high requirement of model interpretability. However, few of the existing works concerns the false discovery rate (FDR) controllability of variable selection associated with PLMs. To address this issue, we formuate a new Knockoffs Inference scheme for Linear And Nonlinear Discoverer (called KI-LAND), where FDR is controlled with respect to both linear and nonlinear variables for automatic structure discovery. For the proposed KI-LAND, theoretical guarantees are established for both FDR controllability and power, and experimental evaluations are provided to validate its effectiveness on simulated exmaples and real data.

Knockoffs Inference for Partially Linear Models with Automatic Structure Discovery

Hypergraph neural networks (HGNNs) have shown promise in handling tasks characterized by high-order correlations, achieving notable success across various applications. However, there has been limited focus on heterophilous hypergraph learning (HHL), contrasting with the increasing attention given to graph neural networks designed for graphs exhibiting heterophily. This paper aims to pave the way for HHL by addressing key gaps from multiple perspectives: measurement, dataset diversity, and baseline model development. Firstly, we introduce metrics to quantify heterophily in hypergraphs, providing a numerical basis for assessing the homophily/heterophily ratio. Secondly, we develop diverse benchmark datasets across various real-world scenarios, facilitating comprehensive evaluations of existing HGNNs and advancing research in HHL. Additionally, as a novel baseline model, we propose HyperUFG, a framelet-based hypergraph neural network integrating both low-pass and high-pass filters. Extensive experiments conducted on synthetic and benchmark datasets highlight the challenges current HGNNs face with heterophilous hypergraphs, while showcasing that HyperUFG performs competitively and often outperforms many existing models in such scenarios. Overall, our study underscore the urgent need for further exploration and development in this emerging field, with the potential to inspire and guide future research in HHL.

When Hypergraph Meets Heterophily: New Benchmark Datasets and Baseline

3D color lookup tables (LUTs) enable precise color manipulation by mapping input RGB values to specific output RGB values. 3D LUTs are instrumental in various applications, including video editing, in-camera processing, photographic filters, computer graphics, and color processing for displays.  While an individual LUT does not incur a high {memory} overhead, software and devices may need to store dozens to hundreds of LUTs that can take over 100 MB.  This work aims to develop a neural network architecture that can encode hundreds of LUTs in a single compact representation. To this end, we propose a model with a {memory} footprint of less than 0.25 MB that can reconstruct 512 LUTs with only minor color distortion ($\bar{\Delta}E_M$ $\leq$ 2.0) over the entire color gamut.  We also show that our network can weight colors to provide further quality gains on natural image colors ($\bar{\Delta}{E}_M$ $\leq$ {1.0}). Finally, we show that minor modifications to the network architecture enable a bijective encoding that produces LUTs that are invertible, allowing for reverse color processing.

Efficient Neural Network Encoding for 3D Color Lookup Tables

Recent lightweight image captioning models using retrieved data mainly focus on text prompts. However, previous works only utilize the retrieved text as text prompts, and the visual information relies only on the CLIP visual embedding. Because of this issue, there is a limitation that the image descriptions inherent in the prompt are not sufficiently reflected in the visual embedding space. To tackle this issue, we propose ViPCap, a novel retrieval text-based visual prompt for lightweight image captioning. ViPCap leverages the retrieved text with image information as visual prompts to enhance the ability of the model to capture relevant visual information. By mapping text prompts into the CLIP space and generating multiple randomized Gaussian distributions, our method leverages sampling to explore randomly augmented distributions and effectively retrieves the semantic features that contain image information. These retrieved features are integrated into the image and designated as the visual prompt, leading to performance improvements on datasets such as COCO, Flickr30k, and NoCaps. Experimental results demonstrate that ViPCap significantly outperforms prior lightweight captioning models in efficiency and effectiveness, demonstrating the potential for a plug-and-play solution.

ViPCap: Retrieval Text-Based Visual Prompts for Lightweight Image Captioning

Blind image super-resolution (blind SR) aims to restore a high-resolution (HR) image from a low-resolution (LR) image with unknown degradation. Many existing methods explicitly estimate degradation information from various LR images. However, in most cases, image degradations are independent of image content. Their estimations may be influenced by the image content resulting in inaccuracy. Unlike existing works, we design a dual-encoder for degradation representation (DEDR) to preclude the influence of image content from LR images. This benefits in extracting the intrinsic degradation representation more accurately. To the best of our knowledge, this paper is the first work that estimates degradation representation through filtering out image content. Based on the degradation representation extracted by DEDR, we present a novel framework, named \textbf{degradation representation aware transform network (DRAT)} for blind SR. We propose global degradation aware (GDA) blocks to propagate degradation information across spatial and channel dimensions, in which a degradation representation transform module (DRT) is introduced to render features degradation-aware, thereby enhancing the restoration of LR images. Extensive experiments are conducted on three benchmark datasets (including Gaussian 8, DIV2KRK, and real-world datasets) under large scaling factors with complex degradations. The experimental results demonstrate that DRAT surpasses state-of-the-art supervised kernel estimation and unsupervised degradation prediction methods. The code will be released on GitHub.

Unsupervised Degradation Representation Aware Transform for Real-World Blind Image Super-Resolution

Low-Rank Adaptation (LoRA) has become increasingly popular for efficiently fine-tuning large language models (LLMs) with minimal resources. However, traditional methods that serve multiple LoRA models independently result in redundant computation and low GPU utilization. This paper addresses these inefficiencies by introducing Dynamic Operator Optimization (Dop), an advanced automated optimization technique designed to dynamically optimize the Segmented Gather Matrix-Vector Multiplication (SGMV) operator based on specific scenarios. SGMV's unique design enables batching GPU operations for different LoRA models, significantly improving computational efficiency. The Dop approach leverages a Search Space Constructor to create a hierarchical search space, dividing the program space into high-level structural sketches and low-level implementation details, ensuring diversity and flexibility in operator implementation. Furthermore, an Optimization Engine refines these implementations using evolutionary search, guided by a cost model that estimates program performance. This iterative optimization process ensures that SGMV implementations can dynamically adapt to different scenarios to maintain high performance. We demonstrate that Dop can improve throughput by 1.30-1.46 times in a state-of-the-art multi-tenant LoRA serving.

Dynamic Operator Optimization for Efficient Multi-Tenant LoRA Model Serving

As artificial intelligence advances rapidly, particularly with the advent of GANs and diffusion models, the accuracy of Image Inpainting Localization (IIL) has become increasingly challenging. Current IIL methods face two main challenges: a tendency towards overconfidence, leading to incorrect predictions; and difficulty in detecting subtle tampering boundaries in inpainted images. In response, we propose a new paradigm that treats IIL as a conditional mask generation task utilizing diffusion models. Our method, InpDiffusion, utilizes the denoising process enhanced by the integration of image semantic conditions to progressively refine predictions. During denoising, we employ edge conditions and introduce a novel edge supervision strategy to enhance the model's perception of edge details in inpainted objects. Balancing the diffusion model's stochastic sampling with edge supervision of tampered image regions mitigates the risk of incorrect predictions from overconfidence and prevents the loss of subtle boundaries that can result from overly stochastic processes. Furthermore, we propose an innovative Dual-stream Multi-scale Feature Extractor (DMFE) for extracting multi-scale features, enhancing feature representation by considering both semantic and edge conditions of the inpainted images. Extensive experiments across challenging datasets demonstrate that the InpDiffusion significantly outperforms existing state-of-the-art methods in IIL tasks, while also showcasing excellent generalization capabilities and robustness.

InpDiffusion: Image Inpainting Localization via Conditional Diffusion Models

In the pursuit of efficient vision architectures, substantial efforts have been devoted to optimizing operator efficiency. Depth-wise separable operators, such as DWConv, are find cheap in both FLOPs and parameters. As a result, they are increasingly incorporated into efficient backbones, trading for deeper and wider architectures to enhance performance. However, separable operators are not really fast on devices due to the discontinuous memory access requirements. In this paper, we propose \textbf{FreeNets}, a family of simple and efficient backbones that free the separable operation to further accelerate the running speed. We introduce sparse sampling mixers (S2-Mixer) to supersede existing separable token mixers. The S2-Mixer samples multiple segments of partially continuous signals across spatial and channel dimensions for convolutional processing, achieving extremely fast on-device speed. The sparse sampling also enables S2-Mixer to capture long-range pixel relationships from dynamic receptive fields. Furthermore, we introduce a Shift Feed-Forward Network (ShiftFFN) as a faster alternative to existing channel mixers. It utilizes a shift neck architecture that aggregates global information to shift features, enabling faster channel mixing while incorporating global pixel information. Extensive experiments demonstrate that FreeNet offers a superior accuracy-efficiency tradeoff compared to the latest efficient models. On ImageNet-1k, FreeNet-S4 outperforms the leading model, FastViT-SA12, by 1.0\% in top-1 accuracy, while running 1.3$\times$ faster on GPU and 1.7$\times$ faster on Mobile.

FreeNet: Liberating Depth-Wise Separable Operations for Building Faster Mobile Vision Architectures

Current benchmarks for video segmentation aim to segment and track objects; however, they are limited to annotating
only salient objects (i.e., foreground instances). As a result, despite their impressive architectural designs, previous works
have struggled to adapt to real-world scenarios, since current benchmarks only handle salient objects. We believe that developing a new video segmentation dataset aimed at tracking multi-granularity segmentation target in the video scene is necessary for the research community. In this work, we aim to generate new video segmentation dataset that is annotated for both salient and non-salient masks. To achieve this, we propose a large-scale, densely annotated Multi-Granularity Video Object Segmentation(MUG-VOS) dataset that includes various types and granularities of mask annotations. We automatically collected a training set that assists in tracking both salient and non-salient objects, and we also curated a human-annotated test set for reliable evaluation. As a result, Memory-based Mask Propagation Model(MMPM) trained on MUG-VOS dataset leads to the best performance on MUG-VOS dataset among the existing video object segmentation methods and Segment Anything Model(SAM)-based video segmentation methods. The project page with MUG-VOS dataset and code will be made available soon.

Multi-Granularity Video Object Segmentation

The importance of time series forecasting drives continuous research and the development of new approaches to tackle this problem. Typically, these methods are introduced through empirical studies that frequently claim superior accuracy for the proposed approaches. Nevertheless, concerns are rising about the reliability and generalizability of these results due to limitations in experimental setups. This paper addresses a critical limitation: the number and representativeness of the datasets used.
We investigate the impact of dataset selection bias, particularly the practice of cherry-picking datasets, on the performance evaluation of forecasting methods. Through empirical analysis with a diverse set of benchmark datasets, our findings reveal that cherry-picking datasets can significantly distort the perceived performance of methods, often exaggerating their effectiveness.
Furthermore, our results demonstrate that by selectively choosing just four datasets — what most studies report — 46\% of methods could be deemed best in class, and 77\% could rank within the top three. Additionally, recent deep learning-based approaches show high sensitivity to dataset selection, whereas classical methods exhibit greater robustness.
Our study highlights the critical need for comprehensive evaluation frameworks that more accurately reflect real-world scenarios. Adopting such frameworks will ensure the development of robust and reliable forecasting methods.

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