13 accepted papers
Current dehazing methods face two intertwined challenges: (1) the misidentification of haze-related features due to domain-specific interference in both single-domain and empirically integrated multi-domain approaches, and (2) severe chromatic distortion caused by haze-induced color entanglement. To
Multimodal large language models (MLLMs) exhibit strong visual-language reasoning, yet remain confined to their native modalities and cannot directly process structured, non-visual data such as human skeletons. Existing methods either compress skeleton dynamics into lossy feature vectors for text al…
Accurately reconstructing object edges is a key challenge in single image super-resolution (SISR), as it greatly influences our visual perception of image quality. To address this fundamental issue, we propose a novel SISR approach named the deformable attention-based edge-aware (DAE) network. The D…
Human pose estimation in computer vision is particularly challenging with images containing multiple individuals. Existing methods often integrate spatial and channel attention by simply adding them up through a cascade or parallel connection. The features extracted in this way could lead to less ac…
Corner detection is extensively applied across various computer vision tasks. Current corner detectors typically assume that the distance between every two nearby pixels is constant. However, this assumption is invalid in real-world scenarios. As a result, the pixel-based curvature measurements desi…
Existing methods for ellipse detection popularly adopt the edge-linking strategy—i.e., first combining the elliptical arcs extracted from an edge map into groups and then fitting each group of arcs to an ellipse. However, such methods generally use the Canny operator to extract edges, which tends to…
A common practice of existing ellipse detection methods is to produce an ellipse by grouping elliptic arcs. Thus, the quality (i.e., continuity) of elliptic arcs will have a significant impact on the performance of ellipse detection. In this paper, a novel contrast-guided ellipse detection method is…
Low-light Image Enhancement (LIE) aims at improving contrast and restoring details for images captured in low-light conditions. Most of the previous LIE algorithms adjust illumination using a single input image with several handcrafted priors. Those solutions, however, often fail in revealing image…
Malicious applications of deepfakes (i.e., technologies generating target facial attributes or entire faces from facial images) have posed a huge threat to individuals' reputation and security. To mitigate these threats, recent studies have proposed adversarial watermarks to combat deepfake models,…
Cross-modal hashing retrieval approaches maps heterogeneous multi-modal data into a common hamming space to achieve efficient and flexible retrieval performance. However, existing cross-modal methods mainly exploit feature-level similarity between multi-modal data, the label-level similarity and rel…
Existing methods for optical flow estimation can perform well on images with small offsets of large objects. However, they could fail when there are a number of small and fast-moving objects. In particular, current deep learning methods often impose a down-sampling of the input data for a reduction…
Due to the low scan rate of LiDAR sensors, LiDAR point cloud streams usually have a low frame rate, which is far below that of other sensors such as cameras. This could incur frame rate mismatch while conducting multi-sensor data fusion. LiDAR point cloud temporal interpolation aims to synthesize th…
"A main challenge faced in the deep learning-based Underwater Image Enhancement (UIE) is that the ground truth high-quality image is unavailable. Most of the existing methods first generate approximate reference maps and then train an enhancement network with certainty. This kind of method fails to…