37 accepted papers
High-resolution visual encoders in multimodal large language models (MLLMs) substantially improve fine-grained perception, yet incur prohibitive computational costs.Existing token pruning methods are effective on natural images but struggle with spatially sparse structured inputs (e.g., charts), whe…
Latent Factorization-of-Tensors (LFT) models are an effective approach for relation extraction. Existing LFT models assume each mode of the target tensor corresponds to a entity set and the relationships between entity sets are bipartite graphs to explore the relationships among entities within a mo…
Instruction-guided image editing has achieved remarkable progress, yet current models still face challenges with complex instructions and often require multiple samples to produce a desired result. Reinforcement Learning (RL) offers a promising solution, but its adoption in image editing has been se…
Few-shot fine-grained image classification (FSFG) aims to recognize novel fine-grained categories from only a few labeled samples. Existing FSFG methods primarily focus on fine-grained feature extraction and modeling query-support interactions within training episodes containing a small number of cl
Few-shot class-incremental learning (FSCIL) aims at recognizing novel classes continually with limited novel class samples. A mainstream baseline for FSCIL is first to train the whole model in the base session, then freeze the feature extractor in the incremental sessions. Despite achieving high ove…
Automatic pronunciation assessment plays a crucial role in computer-assisted pronunciation training systems. Due to the ability to perform multiple pronunciation tasks simultaneously, multi-aspect multi-granularity pronunciation assessment methods are gradually receiving more attention and achieving
Multimodal generative models can process instructions in various modalities and demonstrate outstanding performance across a wide range of image generation tasks. However, their robustness in complex real-world scenarios remains limited due to insufficient generalized instruction alignment. We intro
Hypergraphs effectively model complex interactions in structured Electronic Health Records (EHR). Consequently, Hypergraph Neural Networks (HGNNs) are commonly applied to EHR-based diagnosis prediction. However, existing HGNNs struggle to capture patient-visit long-range dependencies when processing
The quadratic computational complexity of self-attention remains a fundamental bottleneck for scaling Large Language Models (LLMs) to long contexts, particularly during the pre-filling phase. In this paper, we rethink the causal attention mechanism from the perspective of information flow. Due to ca…
Long video question answering requires locating sparse, time-scattered visual evidence within highly redundant content. Although current MLLMs perform well on short videos, long videos introduce long-horizon search and verification, which often necessitates multi-turn, agentic interaction. We show t…
Remote Sensing Image (RSI) segmentation has made significant strides, emerging as a leading solution for interpreting remote sensing data. However, due to the substantial domain gap between different remote sensors and limited computational resources, existing RSI segmentation methods often suffer f…
Recently proposed Fine-Grained Few-Shot Class-Incremental Learning (FG-FSCIL) offers a practical and efficient solution for enabling models to incrementally learn new fine-grained categories under limited data conditions. However, existing methods still settle for the fine-grained feature extraction…
Unpaired training has been verified as one of the most effective paradigms for real scene dehazing by learning from unpaired real-world hazy and clear images. Although numerous studies have been proposed, current methods demonstrate limited generalization for various real scenes due to limited featu…
Recently, a number of effective methods have been proposed to tackle the challenging task of Few-Shot Fine-Grained Image Classification (FS-FGIC). However, how to fully leverage the backbone network to discover and extract detailed features to generate more discriminative class prototypes, as well a…
The increasing adoption of large-scale models under 7 billion parameters in both language and vision domains enables inference tasks on a single consumer-grade GPU but makes fine-tuning models of this scale, especially 7B models, challenging. This limits the applicability of pruning methods that req…
Deepfake detection models often struggle with generalization to unseen datasets, manifesting as misclassifying real instances as fake in target domains. This is primarily due to an overreliance on forgery artifacts and a limited understanding of real faces. To address this challenge, we propose a no
Point cloud extraction (PCE) and ego velocity estimation (EVE) are key capabilities gaining attention in 3D radar perception. However, existing work typically treats these two tasks independently, which may neglect the interplay between radar's spatial and Doppler domain features, potentially introd
Diffusion-based image compression has shown remarkable potential for achieving ultra-low bitrate coding (less than 0.05 bits per pixel) with high realism, by leveraging the generative priors of large pre-trained text-to-image diffusion models. However, current approaches require a large number of de…
Vision Foundation Models (VFMs) have delivered remarkable performance in Domain Generalized Semantic Segmentation (DGSS). However, recent methods often overlook the fact that visual cues are susceptible, whereas the underlying geometry remains stable, rendering depth information more robust. In this…
Weakly-supervised online hashing has garnered significant attention recently, yet several challenges remain unresolved, such as how to effectively denoise tags, and how to efficiently learn hash functions in dynamic online scenarios. To tackle these challenges, we propose a novel method named Tag-Aw…
Recent advancements in unpaired dehazing, particularly those using GANs, show promising performance in processing real-world hazy images. However, these methods tend to face limitations due to the generator's limited transport mapping capability, which hinders the full exploitation of their effectiv…
The rapidly growing scale of data in practice poses demands on the efficiency of retrieval models. However for fine-grained image retrieval task there are inherent contradictions in the design of hashing based efficient models. Firstly the limited information embedding capacity of low-dimensional bi…
"Real-world image super-resolution deals with complex and unknown degradations, making it challenging to produce plausible results in a single step. In this work, we propose a transformer model with an iterative generation process that iteratively refines the results based on predicted confidences.…
Recently, a number of Few-Shot Fine-Grained Image Classification (FS-FGIC) methods have been proposed, but they primarily focus on better fine-grained feature extraction while overlooking two important issues. The first one is how to extract discriminative features for Fine-Grained Image Classificat…
Taking incompatible multiple drugs together may cause adverse interactions and side effects on the body. Accurate prediction of drug-drug interaction (DDI) events is essential for avoiding this issue. Recently, various artificial intelligence-based approaches have been proposed for predicting DDI ev…
Visual SLAM estimates the camera's pose by tracking feature points between consecutive frames. Static feature points are essential for accurately estimating the camera's pose, whereas moving feature points, due to their inherent motion, can lead to inaccuracies. To address this issue, this paper int
Single domain generalization plays a vital role in signal processing tasks, which is capable of extracting domain-invariant knowledge from a single source domain such that the learned model can be generalizable to unseen domains. However, many existing methods require the help of auxiliary tasks, br…
Despite the great success achieved, existing video moment retrieval (VMR) methods are developed under the assumption that data are centralizedly stored. However, in real-world applications, due to the inherent nature of data generation and privacy concerns, data are often distributed on different si…
Several recent studies advocate the use of spectral discriminators, which evaluate the Fourier spectra of images for generative modeling. However, the effectiveness of the spectral discriminators is not well interpreted yet. We tackle this issue by examining the spectral discriminators in the contex…
Recently, deep cross-modal hashing has gained increasing attention. However, in many practical cases, data are distributed and cannot be collected due to privacy concerns, which greatly reduces the cross-modal hashing performance on each client. And due to the problems of statistical heterogeneity,…
Trajectory optimization (TO) combined with whole-body control (WBC) have been a widely accepted approach for dynamic gait control of quadruped robots. However, there are still open issues in this framework, one is the lack of a unified description of intrinsic inter-limb coordination for wide range
Domain adaptation is common but challenging in signal processing tasks due to the intrinsic discrepancy, especially in difficult-to-label medical image segmentation application scenarios. Pseudo labeling methods are widely utilized to compensate for the scarcity of annotation. However, most existing…
Source-free model adaptation (SFMA) plays an important role in robust robot automation, which aims to mitigate distributional inconsistency between source and target data while avoiding accessing source data. SFMA methods generally benefit from self-training, which can be prone to overfitting noisy
With the vigorous development of multimedia equipments and applications, efficient retrieval of large-scale multi-modal data has become a trendy research topic. Thereinto, hashing has become a prevalent choice due to its retrieval efficiency and low storage cost. Although multi-modal hashing has dr…
During the past decades, industrial robot arms have been widely adopted in various fields, like aerospace and medical care. However, an uncalibrated industrial robot arm suffers from its high absolute positioning error, which tremendously restricts its application in high-precision intelligent manuf
Stereo matching has attracted much attention in recent years. Traditional methods can quickly generate a disparity result, but the accuracy is low. On the contrary, methods based on neural networks can achieve a high accuracy level, but they are difficult to reach the real-time level. Therefore, thi…
High-dimensional and sparse (HiDS) matrices from recommender systems contain various useful patterns. A latent factor (LF) analysis is highly efficient in grasping these patterns. Stochastic gradient descent (SGD) is a widely adopted algorithm to train an LF model. Can its extensions be capable of f