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Found 14 papers, 3 papers with code
The Solution for the CVPR2024 NICE Image Captioning Challenge
Subsequently, we propose caption-level strategy for the high-quality caption data generated by the image caption models and integrate them with retrieval augmentation strategy into the template to compel the model to generate higher quality, more matching, and semantically enriched captions based on the retrieval augmentation prompts.
The Solution for the ICCV 2023 1st Scientific Figure Captioning Challenge
In this paper, we propose a solution for improving the quality of captions generated for figures in papers.
Solution for SMART-101 Challenge of ICCV Multi-modal Algorithmic Reasoning Task 2023
At the data level, inspired by the challenge paper, we categorized the whole questions into eight types and utilized the llama-2-chat model to directly generate the type for each question in a zero-shot manner.
The Solution for the CVPR2023 NICE Image Captioning Challenge
In this paper, we present our solution to the New frontiers for Zero-shot Image Captioning Challenge.
NICE: CVPR 2023 Challenge on Zero-shot Image Captioning
In this report, we introduce NICE (New frontiers for zero-shot Image Captioning Evaluation) project and share the results and outcomes of 2023 challenge.
ContentCTR: Frame-level Live Streaming Click-Through Rate Prediction with Multimodal Transformer
However, most previous works treat the live as a whole item and explore the Click-through-Rate (CTR) prediction framework on item-level, neglecting that the dynamic changes that occur even within the same live room.
CoVLR: Coordinating Cross-Modal Consistency and Intra-Modal Structure for Vision-Language Retrieval
To address this challenge, in this paper, we experimentally observe that the vision-language divergence may cause the existence of strong and weak modalities, and the hard cross-modal consistency cannot guarantee that strong modal instances' relationships are not affected by weak modality, resulting in the strong modal instances' relationships perturbed despite learned consistent representations. To this end, we propose a novel and directly Coordinated VisionLanguage Retrieval method (dubbed CoVLR), which aims to study and alleviate the desynchrony problem between the cross-modal alignment and single-modal cluster-preserving tasks.
Generation-Guided Multi-Level Unified Network for Video Grounding
Video grounding aims to locate the timestamps best matching the query description within an untrimmed video.
A Unified Model for Video Understanding and Knowledge Embedding with Heterogeneous Knowledge Graph Dataset
Furthermore, based on this dataset, we propose an end-to-end model that jointly optimizes the video understanding objective with knowledge graph embedding, which can not only better inject factual knowledge into video understanding but also generate effective multi-modal entity embedding for KG.
Learning a Single Near-hover Position Controller for Vastly Different Quadcopters
This paper proposes an adaptive near-hover position controller for quadcopters, which can be deployed to quadcopters of very different mass, size and motor constants, and also shows rapid adaptation to unknown disturbances during runtime.
Improving Video-Text Retrieval by Multi-Stream Corpus Alignment and Dual Softmax Loss
In this paper, we propose a multi-stream Corpus Alignment network with single gate Mixture-of-Experts (CAMoE) and a novel Dual Softmax Loss (DSL) to solve the two heterogeneity.
Ranked #9 on
Video Retrieval
on MSVD
(using extra training data)
Real-time Geo-localization Using Satellite Imagery and Topography for Unmanned Aerial Vehicles
The capabilities of autonomous flight with unmanned aerial vehicles (UAVs) have significantly increased in recent times.
MlTr: Multi-label Classification with Transformer
The task of multi-label image classification is to recognize all the object labels presented in an image.
Ranked #12 on
Multi-Label Classification
on MS-COCO
CAT: Cross Attention in Vision Transformer
In this paper, we propose a new attention mechanism in Transformer termed Cross Attention, which alternates attention inner the image patch instead of the whole image to capture local information and apply attention between image patches which are divided from single-channel feature maps capture global information.
