11 accepted papers
We present Causal-Adapter, a modular framework that adapts frozen text-to-image diffusion backbones for counterfactual image generation. Our method enables causal interventions on target attributes while preserving all other aspects of the image, including the core identity. In contrast to prior app…
The scene flow estimation methods make significant progress by estimating pixel-wise 3D motion on implicitly learning a motion embedding using an end-to-end differentiable optimization framework. However, the motion embedding learned implicitly is insufficient for grouping pixels into rigid object i…
Open-set image segmentation poses a significant challenge because existing methods often demand extensive training or fine-tuning and generally struggle to segment unified objects consistently across diverse text reference expressions. Motivated by this, we propose Segment Anyword, a novel training-…
The inverse dynamics of the six degree-of-freedom (6-DOF) parallel robot (PR) presents an inherent complexity due to the closed-loop kinematic chains. To derive computational efficient inverse dynamics for real-time control, this study presents a numerical approximation (NA) approach based on the pr
Recent deep learning based visual simultaneous localization and mapping (SLAM) methods have made significant progress. However, how to make full use of visual information as well as better integrate with inertial measurement unit (IMU) in visual SLAM has potential research value. This paper proposes…
A loop closure module plays an important role in visual SLAM systems, which can reduce the accumulat-ed drift. This task faces the challenges of large viewpoint changes and expensive computational costs when optimizing the global map. This paper proposes DH-LC, a novel accurate and robust loop closu…
Adversarial robustness has attracted much attention recently, and the mainstream solution is adversarial training. However, the tradition of generating adversarial perturbations for each input embedding (in the settings of NLP) scales up the training computational complexity by the number of gradien…
Most of the state-of-the-art visual inertial SLAM methods pay less attention to 2D-2D and 3D-2D matching with more reliable features in a long time span, which easily results in continuous estimation drift. In this paper, we propose an efficient drift-free visual-inertial SLAM method by a pose guide…
Most of the state-of-the-art visual-inertial SLAM methods pay less attention to the scene structure of man-made environments. In this paper, based on the assumption of multiple local Manhattan worlds (MWs), we propose a Manhattan frame (MF) re-identification method to build relative rotation constra…
Human dialogue contains evolving concepts, and speakers naturally associate multiple concepts to compose a response. However, current dialogue models with the seq2seq framework lack the ability to effectively manage concept transitions and can hardly introduce multiple concepts to responses in a seq…
Recent studies have shown that cascade cost volume can play a vital role in deep stereo matching to achieve high resolution depth map with efficient hardware usage. However, how to construct good cascade volume as well as effective sampling for them are still under in-depth study. Previous cascade-b…