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Found 12 papers, 3 papers with code
BadCLIP: Trigger-Aware Prompt Learning for Backdoor Attacks on CLIP
Contrastive Vision-Language Pre-training, known as CLIP, has shown promising effectiveness in addressing downstream image recognition tasks.
Dual-Stream Knowledge-Preserving Hashing for Unsupervised Video Retrieval
Hence, we address this problem by decomposing video information into reconstruction-dependent and semantic-dependent information, which disentangles the semantic extraction from reconstruction constraint.
Tencent Text-Video Retrieval: Hierarchical Cross-Modal Interactions with Multi-Level Representations
With multi-level representations for video and text, hierarchical contrastive learning is designed to explore fine-grained cross-modal relationships, i. e., frame-word, clip-phrase, and video-sentence, which enables HCMI to achieve a comprehensive semantic comparison between video and text modalities.
Ranked #1 on
Video Retrieval
on MSR-VTT-1kA
(using extra training data)
Dual Progressive Prototype Network for Generalized Zero-Shot Learning
This enables DPPN to produce visual representations with accurate attribute localization ability, which benefits the semantic-visual alignment and representation transferability.
Cross-Modal Attention Consistency for Video-Audio Unsupervised Learning
Cross-modal correlation provides an inherent supervision for video unsupervised representation learning.
Task-Independent Knowledge Makes for Transferable Representations for Generalized Zero-Shot Learning
First, DCEN leverages task labels to cluster representations of the same semantic category by cross-modal contrastive learning and exploring semantic-visual complementarity.
Hierarchical Granularity Transfer Learning
In this paper, we introduce a new task, named Hierarchical Granularity Transfer Learning (HGTL), to recognize sub-level categories with basic-level annotations and semantic descriptions for hierarchical categories.
Attribute-Induced Bias Eliminating for Transductive Zero-Shot Learning
Then, an attentional graph attribute embedding is proposed to reduce the semantic bias between seen and unseen categories, which utilizes the graph operation to capture the semantic relationship between categories.
Domain-aware Visual Bias Eliminating for Generalized Zero-Shot Learning
Recent methods focus on learning a unified semantic-aligned visual representation to transfer knowledge between two domains, while ignoring the effect of semantic-free visual representation in alleviating the biased recognition problem.
Multi-Objective Matrix Normalization for Fine-grained Visual Recognition
In this paper, we propose an efficient Multi-Objective Matrix Normalization (MOMN) method that can simultaneously normalize a bilinear representation in terms of square-root, low-rank, and sparsity.
Domain-Specific Embedding Network for Zero-Shot Recognition
In contrast to previous methods, the DSEN decomposes the domain-shared projection function into one domain-invariant and two domain-specific sub-functions to explore the similarities and differences between two domains.
A Two-Stream Mutual Attention Network for Semi-supervised Biomedical Segmentation with Noisy Labels
\begin{abstract} Learning-based methods suffer from a deficiency of clean annotations, especially in biomedical segmentation.
