Static and Dynamic Concepts for Self-supervised Video Representation Learning

• URL: http://arxiv.org/abs/2207.12795v1
• Date: Tue, 26 Jul 2022 10:28:44 GMT
• Title: Static and Dynamic Concepts for Self-supervised Video Representation Learning
• Authors: Rui Qian, Shuangrui Ding, Xian Liu, Dahua Lin
• Abstract summary: We propose a novel learning scheme for self-supervised video representation learning. Motivated by how humans understand videos, we propose to first learn general visual concepts then attend to discriminative local areas for video understanding.
• Score: 70.15341866794303
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose a novel learning scheme for self-supervised video representation learning. Motivated by how humans understand videos, we propose to first learn general visual concepts then attend to discriminative local areas for video understanding. Specifically, we utilize static frame and frame difference to help decouple static and dynamic concepts, and respectively align the concept distributions in latent space. We add diversity and fidelity regularizations to guarantee that we learn a compact set of meaningful concepts. Then we employ a cross-attention mechanism to aggregate detailed local features of different concepts, and filter out redundant concepts with low activations to perform local concept contrast. Extensive experiments demonstrate that our method distills meaningful static and dynamic concepts to guide video understanding, and obtains state-of-the-art results on UCF-101, HMDB-51, and Diving-48.

Related papers

• ConceptExpress: Harnessing Diffusion Models for Single-image Unsupervised Concept Extraction [20.43411883845885]
  We introduce a novel task named Unsupervised Concept Extraction (UCE) that considers an unsupervised setting without any human knowledge of the concepts. Given an image that contains multiple concepts, the task aims to extract and recreate individual concepts solely relying on the existing knowledge from pretrained diffusion models. We present ConceptExpress that tackles UCE by unleashing the inherent capabilities of pretrained diffusion models in two aspects.
  arXiv  Detail & Related papers  (2024-07-09T17:50:28Z)
• A Self-explaining Neural Architecture for Generalizable Concept Learning [29.932706137805713]
  We show that present SOTA concept learning approaches suffer from two major problems - lack of concept fidelity and limited concept interoperability. We propose a novel self-explaining architecture for concept learning across domains. We demonstrate the efficacy of our proposed approach over current SOTA concept learning approaches on four widely used real-world datasets.
  arXiv  Detail & Related papers  (2024-05-01T06:50:18Z)
• Advancing Ante-Hoc Explainable Models through Generative Adversarial Networks [24.45212348373868]
  This paper presents a novel concept learning framework for enhancing model interpretability and performance in visual classification tasks. Our approach appends an unsupervised explanation generator to the primary classifier network and makes use of adversarial training. This work presents a significant step towards building inherently interpretable deep vision models with task-aligned concept representations.
  arXiv  Detail & Related papers  (2024-01-09T16:16:16Z)
• ConceptX: A Framework for Latent Concept Analysis [21.760620298330235]
  We present ConceptX, a human-in-the-loop framework for interpreting and annotating latent representational space in Language Models (pLMs) We use an unsupervised method to discover concepts learned in these models and enable a graphical interface for humans to generate explanations for the concepts.
  arXiv  Detail & Related papers  (2022-11-12T11:31:09Z)
• Automatic Concept Extraction for Concept Bottleneck-based Video Classification [58.11884357803544]
  We present an automatic Concept Discovery and Extraction module that rigorously composes a necessary and sufficient set of concept abstractions for concept-based video classification. Our method elicits inherent complex concept abstractions in natural language to generalize concept-bottleneck methods to complex tasks.
  arXiv  Detail & Related papers  (2022-06-21T06:22:35Z)
• Visual Concepts Tokenization [65.61987357146997]
  We propose an unsupervised transformer-based Visual Concepts Tokenization framework, dubbed VCT, to perceive an image into a set of disentangled visual concept tokens. To obtain these concept tokens, we only use cross-attention to extract visual information from the image tokens layer by layer without self-attention between concept tokens. We further propose a Concept Disentangling Loss to facilitate that different concept tokens represent independent visual concepts.
  arXiv  Detail & Related papers  (2022-05-20T11:25:31Z)
• Learning from Untrimmed Videos: Self-Supervised Video Representation Learning with Hierarchical Consistency [60.756222188023635]
  We propose to learn representations by leveraging more abundant information in unsupervised videos. HiCo can generate stronger representations on untrimmed videos, it also improves the representation quality when applied to trimmed videos.
  arXiv  Detail & Related papers  (2022-04-06T18:04:54Z)
• Human-Centered Concept Explanations for Neural Networks [47.71169918421306]
  We introduce concept explanations including the class of Concept Activation Vectors (CAV) We then discuss approaches to automatically extract concepts, and approaches to address some of their caveats. Finally, we discuss some case studies that showcase the utility of such concept-based explanations in synthetic settings and real world applications.
  arXiv  Detail & Related papers  (2022-02-25T01:27:31Z)
• Interactive Disentanglement: Learning Concepts by Interacting with their Prototype Representations [15.284688801788912]
  We show the advantages of prototype representations for understanding and revising the latent space of neural concept learners. For this purpose, we introduce interactive Concept Swapping Networks (iCSNs) iCSNs learn to bind conceptual information to specific prototype slots by swapping the latent representations of paired images.
  arXiv  Detail & Related papers  (2021-12-04T09:25:40Z)
• Unsupervised Learning of Compositional Energy Concepts [70.11673173291426]
  We propose COMET, which discovers and represents concepts as separate energy functions. Comet represents both global concepts as well as objects under a unified framework.
  arXiv  Detail & Related papers  (2021-11-04T17:46:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.