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Found 11 papers, 4 papers with code
HaVTR: Improving Video-Text Retrieval Through Augmentation Using Large Foundation Models
While recent progress in video-text retrieval has been driven by the exploration of powerful model architectures and training strategies, the representation learning ability of video-text retrieval models is still limited due to low-quality and scarce training data annotations.
Pretext Training Algorithms for Event Sequence Data
Pretext training followed by task-specific fine-tuning has been a successful approach in vision and language domains.
InvGC: Robust Cross-Modal Retrieval by Inverse Graph Convolution
However, a recent study shows that multi-modal data representations tend to cluster within a limited convex cone (as representation degeneration problem), which hinders retrieval performance due to the inseparability of these representations.
Balance Act: Mitigating Hubness in Cross-Modal Retrieval with Query and Gallery Banks
In this work, we present a post-processing solution to address the hubness problem in cross-modal retrieval, a phenomenon where a small number of gallery data points are frequently retrieved, resulting in a decline in retrieval performance.
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.
Gradient-Based Word Substitution for Obstinate Adversarial Examples Generation in Language Models
Furthermore, to show the transferability of obstinate word substitutions found by GradObstinate, we replace the words in four representative NLP benchmarks with their obstinate substitutions.
Cooperation or Competition: Avoiding Player Domination for Multi-Target Robustness via Adaptive Budgets
We identify a phenomenon named player domination in the bargaining game, namely that the existing max-based approaches, such as MAX and MSD, do not converge.
Video-Text Retrieval by Supervised Sparse Multi-Grained Learning
While recent progress in video-text retrieval has been advanced by the exploration of better representation learning, in this paper, we present a novel multi-grained sparse learning framework, S3MA, to learn an aligned sparse space shared between the video and the text for video-text retrieval.
Ranked #16 on
Video Retrieval
on MSR-VTT-1kA
Multimodal Federated Learning via Contrastive Representation Ensemble
In this work, we propose Contrastive Representation Ensemble and Aggregation for Multimodal FL (CreamFL), a multimodal federated learning framework that enables training larger server models from clients with heterogeneous model architectures and data modalities, while only communicating knowledge on public dataset.
Multi-View Fusion Transformer for Sensor-Based Human Activity Recognition
In this paper, to extract a better feature for advancing the performance, we propose a novel method, namely multi-view fusion transformer (MVFT) along with a novel attention mechanism.
Nearly Optimal Regret for Stochastic Linear Bandits with Heavy-Tailed Payoffs
In this paper, we study the problem of stochastic linear bandits with finite action sets.
