10 accepted papers
Few-shot multimodal industrial anomaly detection is a critical yet underexplored task, offering the ability to quickly adapt to complex industrial scenarios. In few-shot settings, insufficient training samples often fail to cover the diverse patterns present in test samples. This challenge can be mi
Affective Forecasting is an psychology task that involves predicting an individual
Text-guided object segmentation requires both cross-modal reasoning and pixel grounding abilities. Most recent methods treat it as a single forward pass, where the model directly predicts pixel prompts to a segmentation model, which limits verification, refocusing and refinement when initial localiz…
Previous works have attempted to improve tracking efficiency through lightweight architecture design or knowledge distillation from teacher models to compact student trackers. However, these solutions often sacrifice accuracy for speed to a great extent, and also have the problems of complex trainin…
Video Camouflaged Object Detection (VCOD) aims to segment objects whose appearances closely resemble their surroundings, posing a challenging and emerging task. Existing vision models often struggle in such scenarios due to the indistinguishable appearance of camouflaged objects and the insufficient…
This paper introduces an effective and tiny model for real-time Camouflaged Object Detection (COD) named Tiny-COD. It achieves high performance with very low costs (Parameters < 5M, FLOPs < 1.5G), which can be applied on mobile devices. Specifically, we introduce a simple but effective Adjacent Scal…
Current benchmarks for facial expression recognition (FER) mainly focus on static images, while there are limited datasets for FER in videos. It is still ambiguous to evaluate whether performances of existing methods remain satisfactory in real-world application-oriented scenes. For example, the "Ha…
Named entity recognition and relation extraction are two important core subtasks of information extraction, which aim to identify named entities and extract relations between them. In recent years, span representation methods have received a lot of attention and are widely used to extract entities a…
Current state-of-the-art saliency detection models rely heavily on large datasets of accurate pixel-wise annotations, but manually labeling pixels is time-consuming and labor-intensive. There are some weakly supervised methods developed for alleviating the problem, such as image label, bounding box…
High-level features can help low-level features eliminate semantic ambiguity, which is crucial for obtaining the precise salient object. Some methods use high-level features to provide global guidance for some layers of the network. However, there remain several problems: (1) the global guidance has…