13 accepted papers
We present MCGS (Markov Chain Gaussian Splatting), a novel approach for high-fidelity dynamic scene reconstruction via combining Markov chain and 3D Gaussian splatting. Our method addresses the critical challenge of artifact-free temporal consistency in dynamic neural rendering. By integrating a Mar
Chinese Search Query Spell Correction is a task designed to autonomously identify and correct typographical errors within queries in the search engine. Despite the availability of comprehensive datasets like Microsoft Speller and Webis, their monolingual nature and limited scope pose significant cha…
Infrared small targets often exhibit small scale and weak semantic features, which makes it a great challenge to their detection. To address this situation, we propose a novel network for infrared small target detection that combines local details information and global contextual information. To pr…
This paper proposes a simple yet effective method to re-move shadows from text document images. It mainly includes several parts. Firstly, we propose a text elimination-based background extraction strategy to estimate shadow map. It indicates the shadow regions accurately and helps to predict global…
Exploring the entity-level (i.e., objects in an image, words in a text) spatial relationship contributes to understanding multimedia content precisely. The ignorance of spatial information in previous works probably leads to misunderstandings of image contents. For instance, sentences `Boats are on…
Recent neural models for image captioning usually employ an encoder-decoder framework with an attention mechanism. However, the attention mechanism in such a framework aligns one single (attended) image feature vector to one caption word, assuming one-to-one mapping from source image regions and tar…
Attention mechanisms are widely used in current encoder/decoder frameworks of image captioning, where a weighted average on encoded vectors is generated at each time step to guide the caption decoding process. However, the decoder has little idea of whether or how well the attended vector and the gi…
Recent neural models on image captioning usually take a encoder-decoder fashion, where the decoder predicts a single word at one step recently with the encoder providing information. The encoder is a pretrained CNN model typically. Thus the decoder, the input to it, and the output from it become the…
Cross-modal retrieval has been recently proposed to find an appropriate subspace where the similarity among different modalities, such as image and text, can be directly measured. In this paper, we propose Multi-step Self-Attention Network (MSAN) to perform cross-modal retrieval in a limited text sp…
Merge prediction is a practical inter-technique in HEVC, which can significantly improve the coding efficiency, especially for homogeneous regions in video sequences. In this paper, a motion aided merge mode (MAMM) is proposed to achieve a better trade-off between the prediction accuracy and bit rat…
Cross-modal retrieval, which aims to solve the problem that the query and the retrieved results are from different modality, becomes more and more essential with the development of the Internet. In this paper, we mainly focus on the exploration of high-level semantic representation of image and text…
Contexts are crucial for action recognition in video. Current methods often mine contexts after extracting hierarchical local features and focus on their high-order encodings. This paper instead explores contexts as early as possible and leverages their evolutions for action recognition. In particul…
Many successful systems for scene recognition transform low-level descriptors into complex representations. This process consists of the two steps: 1) feature coding, which performs a pointwise transformation of the descriptors into a representation adapted to the task, and 2) image pooling, which s…