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Found 20 papers, 2 papers with code
Cell Variational Information Bottleneck Network
In this work, we propose Cell Variational Information Bottleneck Network (cellVIB), a convolutional neural network using information bottleneck mechanism, which can be combined with the latest feedforward network architecture in an end-to-end training method.
Wear-Any-Way: Manipulable Virtual Try-on via Sparse Correspondence Alignment
This paper introduces a novel framework for virtual try-on, termed Wear-Any-Way.
Audio-Visual Segmentation via Unlabeled Frame Exploitation
NFs, temporally adjacent to the labeled frame, often contain rich motion information that assists in the accurate localization of sounding objects.
Turbo: Informativity-Driven Acceleration Plug-In for Vision-Language Models
Vision-Language Large Models (VLMs) have become primary backbone of AI, due to the impressive performance.
Enhancing Cross-domain Click-Through Rate Prediction via Explicit Feature Augmentation
Later the augmentation network employs the explicit cross-domain knowledge as augmented information to boost the target domain CTR prediction.
AttrSeg: Open-Vocabulary Semantic Segmentation via Attribute Decomposition-Aggregation
The results show the superior performance of attribute decomposition-aggregation.
Audio-aware Query-enhanced Transformer for Audio-Visual Segmentation
The goal of the audio-visual segmentation (AVS) task is to segment the sounding objects in the video frames using audio cues.
Multi-Modal Prototypes for Open-Set Semantic Segmentation
In this paper, we define a unified setting termed as open-set semantic segmentation (O3S), which aims to learn seen and unseen semantics from both visual examples and textual names.
Annotation-free Audio-Visual Segmentation
The objective of Audio-Visual Segmentation (AVS) is to localise the sounding objects within visual scenes by accurately predicting pixel-wise segmentation masks.
Image to Multi-Modal Retrieval for Industrial Scenarios
We formally define a novel valuable information retrieval task: image-to-multi-modal-retrieval (IMMR), where the query is an image and the doc is an entity with both image and textual description.
Multi-modal Prompting for Low-Shot Temporal Action Localization
In this paper, we consider the problem of temporal action localization under low-shot (zero-shot & few-shot) scenario, with the goal of detecting and classifying the action instances from arbitrary categories within some untrimmed videos, even not seen at training time.
DiffusionSeg: Adapting Diffusion Towards Unsupervised Object Discovery
However, the challenges exist as there is one structural difference between generative and discriminative models, which limits the direct use.
Constraint and Union for Partially-Supervised Temporal Sentence Grounding
Temporal sentence grounding aims to detect the event timestamps described by the natural language query from given untrimmed videos.
Distilling Vision-Language Pre-training to Collaborate with Weakly-Supervised Temporal Action Localization
And as a result, the dual-branch complementarity is effectively fused to promote a strong alliance.
Weakly-supervised Temporal Action Localization
Weakly Supervised Temporal Action Localization
Exploiting Transformation Invariance and Equivariance for Self-supervised Sound Localisation
We present a simple yet effective self-supervised framework for audio-visual representation learning, to localize the sound source in videos.
Prompting Visual-Language Models for Efficient Video Understanding
Image-based visual-language (I-VL) pre-training has shown great success for learning joint visual-textual representations from large-scale web data, revealing remarkable ability for zero-shot generalisation.
Ranked #5 on
Zero-Shot Action Detection
on ActivityNet-1.3
Adaptive Mutual Supervision for Weakly-Supervised Temporal Action Localization
To solve this issue, we introduce an adaptive mutual supervision framework (AMS) with two branches, where the base branch adopts CAS to localize the most discriminative action regions, while the supplementary branch localizes the less discriminative action regions through a novel adaptive sampler.
Ranked #7 on
Weakly Supervised Action Localization
on THUMOS14
Weakly Supervised Action Localization
Weakly-supervised Temporal Action Localization
+1
Divide and Conquer for Single-Frame Temporal Action Localization
Single-frame temporal action localization (STAL) aims to localize actions in untrimmed videos with only one timestamp annotation for each action instance.
Point-Level Temporal Action Localization: Bridging Fully-supervised Proposals to Weakly-supervised Losses
Point-Level temporal action localization (PTAL) aims to localize actions in untrimmed videos with only one timestamp annotation for each action instance.
Ranked #3 on
Weakly Supervised Action Localization
on BEOID
Bottom-Up Temporal Action Localization with Mutual Regularization
To alleviate this problem, we introduce two regularization terms to mutually regularize the learning procedure: the Intra-phase Consistency (IntraC) regularization is proposed to make the predictions verified inside each phase; and the Inter-phase Consistency (InterC) regularization is proposed to keep consistency between these phases.
