9 accepted papers
Incorporating human feedback to optimize text-to-image models has demonstrated significant effectiveness. However, the process of collecting high-quality human preference labels is both resource-intensive and time-consuming. To address this challenge, we propose a novel approach that leverages a lar…
The role of high-quality pseudo-labels is pivotal in semi-supervised instance segmentation (SSIS). However, existing SSIS frameworks predominantly produce pseudo-labels at a single resolution, which can introduce noise that adversely affects the quality of learning at both the pixel level and in ter…
Camouflaged object detection (COD) faces unique challenges where target boundaries are intrinsically ambiguous due to their textural similarity to backgrounds. Existing methods relying on single-modality features often produce fragmented predictions due to insufficient boundary constraints.To addres…
Camouflaged Object Detection (COD) is a challenging task in computer vision due to the high similarity between camouflaged objects and their surroundings. Existing COD methods struggle with nuanced object boundaries and overconfident incorrect predictions. In response, we propose a new paradigm that…
Masked autoencoders have become popular training paradigms for self-supervised visual representation learning. These models randomly mask a portion of the input and reconstruct the masked portion according to assigned target representations. In this paper, we show that a careful choice of the target…
The Segment Anything Model (SAM) marks a notable milestone in segmentation models highlighted by its robust zero-shot capabilities and ability to handle diverse prompts. SAM follows a pipeline that separates interactive segmentation into image preprocessing through a large encoder and interactive in…
Video-text retrieval has widespread applications in economic and security domains, making it crucial to evaluate its robustness through adversarial attack. However, the existing research in this field is inadequate. In this paper, we first introduce adversarial attack to this task. By leveraging the…
Video-text retrieval has greatly benefited from the massive web video in recent years, while the performance is still limited to the weak supervision from the uncurated data. In this work, we propose to leverage the well-represented information of each original modality and exploit complementary inf…
Transfer adversarial attack is a non-trivial black-box adversarial attack that aims to craft adversarial perturbations on the surrogate model and then apply such perturbations to the victim model. However, the transferability of perturbations from existing methods is still limited, since the adversa…