FADE: A Task-Agnostic Upsampling Operator for Encoder-Decoder Architectures

• URL: http://arxiv.org/abs/2407.13500v1
• Date: Thu, 18 Jul 2024 13:32:36 GMT
• Title: FADE: A Task-Agnostic Upsampling Operator for Encoder-Decoder Architectures
• Authors: Hao Lu, Wenze Liu, Hongtao Fu, Zhiguo Cao,
• Abstract summary: FADE is a novel, plug-and-play, lightweight, and task-agnostic upsampling operator. We show that FADE is task-agnostic with consistent performance improvement on a number of dense prediction tasks. For the first time, we demonstrate robust feature upsampling on both region- and detail-sensitive tasks successfully.
• Score: 18.17019371324024
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The goal of this work is to develop a task-agnostic feature upsampling operator for dense prediction where the operator is required to facilitate not only region-sensitive tasks like semantic segmentation but also detail-sensitive tasks such as image matting. Prior upsampling operators often can work well in either type of the tasks, but not both. We argue that task-agnostic upsampling should dynamically trade off between semantic preservation and detail delineation, instead of having a bias between the two properties. In this paper, we present FADE, a novel, plug-and-play, lightweight, and task-agnostic upsampling operator by fusing the assets of decoder and encoder features at three levels: i) considering both the encoder and decoder feature in upsampling kernel generation; ii) controlling the per-point contribution of the encoder/decoder feature in upsampling kernels with an efficient semi-shift convolutional operator; and iii) enabling the selective pass of encoder features with a decoder-dependent gating mechanism for compensating details. To improve the practicality of FADE, we additionally study parameter- and memory-efficient implementations of semi-shift convolution. We analyze the upsampling behavior of FADE on toy data and show through large-scale experiments that FADE is task-agnostic with consistent performance improvement on a number of dense prediction tasks with little extra cost. For the first time, we demonstrate robust feature upsampling on both region- and detail-sensitive tasks successfully. Code is made available at: https://github.com/poppinace/fade

Related papers

• PAGE: Parametric Generative Explainer for Graph Neural Network [16.350208494261913]
  PAGE is capable of providing faithful explanations for any graph neural network without necessitating prior knowledge or internal details. We introduce an additional discriminator to capture the causality between latent causal features and the model's output. Compared to existing methods, PAGE operates at the sample scale rather than nodes or edges.
  arXiv  Detail & Related papers  (2024-08-26T06:39:49Z)
• Refine, Discriminate and Align: Stealing Encoders via Sample-Wise Prototypes and Multi-Relational Extraction [57.16121098944589]
  RDA is a pioneering approach designed to address two primary deficiencies prevalent in previous endeavors aiming at stealing pre-trained encoders. It is accomplished via a sample-wise prototype, which consolidates the target encoder's representations for a given sample's various perspectives. For more potent efficacy, we develop a multi-relational extraction loss that trains the surrogate encoder to Discriminate mismatched embedding-prototype pairs.
  arXiv  Detail & Related papers  (2023-12-01T15:03:29Z)
• Efficient Controllable Multi-Task Architectures [85.76598445904374]
  We propose a multi-task model consisting of a shared encoder and task-specific decoders where both encoder and decoder channel widths are slimmable. Our key idea is to control the task importance by varying the capacities of task-specific decoders, while controlling the total computational cost. This improves overall accuracy by allowing a stronger encoder for a given budget, increases control over computational cost, and delivers high-quality slimmed sub-architectures.
  arXiv  Detail & Related papers  (2023-08-22T19:09:56Z)
• ASAG: Building Strong One-Decoder-Layer Sparse Detectors via Adaptive Sparse Anchor Generation [50.01244854344167]
  We bridge the performance gap between sparse and dense detectors by proposing Adaptive Sparse Anchor Generator (ASAG) ASAG predicts dynamic anchors on patches rather than grids in a sparse way so that it alleviates the feature conflict problem. Our method outperforms dense-d ones and achieves a better speed-accuracy trade-off.
  arXiv  Detail & Related papers  (2023-08-18T02:06:49Z)
• Object Segmentation by Mining Cross-Modal Semantics [68.88086621181628]
  We propose a novel approach by mining the Cross-Modal Semantics to guide the fusion and decoding of multimodal features. Specifically, we propose a novel network, termed XMSNet, consisting of (1) all-round attentive fusion (AF), (2) coarse-to-fine decoder (CFD), and (3) cross-layer self-supervision.
  arXiv  Detail & Related papers  (2023-05-17T14:30:11Z)
• Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Driving [74.28510044056706]
  Existing methods usually adopt the decoupled encoder-decoder paradigm. In this work, we aim to alleviate the problem by two principles. We first predict a coarse-grained future position and action based on the encoder features. Then, conditioned on the position and action, the future scene is imagined to check the ramification if we drive accordingly.
  arXiv  Detail & Related papers  (2023-05-10T15:22:02Z)
• FADE: Fusing the Assets of Decoder and Encoder for Task-Agnostic Upsampling [21.590872272491033]
  We present FADE, a novel, plug-and-play, and task-agnostic upsampling operator. We first study the upsampling properties of FADE on toy data and then evaluate it on large-scale semantic segmentation and image matting.
  arXiv  Detail & Related papers  (2022-07-21T10:06:01Z)
• Hyperdecoders: Instance-specific decoders for multi-task NLP [9.244884318445413]
  We investigate input-conditioned hypernetworks for multi-tasking in NLP. We generate parameter-efficient adaptations for a decoder using a hypernetwork conditioned on the output of an encoder.
  arXiv  Detail & Related papers  (2022-03-15T22:39:53Z)
• TRACER: Extreme Attention Guided Salient Object Tracing Network [3.2434811678562676]
  We propose TRACER, which detects salient objects with explicit edges by incorporating attention guided tracing modules. A comparison with 13 existing methods reveals that TRACER achieves state-of-the-art performance on five benchmark datasets.
  arXiv  Detail & Related papers  (2021-12-14T13:20:07Z)
• ESAD: End-to-end Deep Semi-supervised Anomaly Detection [85.81138474858197]
  We propose a new objective function that measures the KL-divergence between normal and anomalous data. The proposed method significantly outperforms several state-of-the-arts on multiple benchmark datasets.
  arXiv  Detail & Related papers  (2020-12-09T08:16:35Z)

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.