Source codes for Improved Few-Shot Visual Classification (CVPR 2020), Enhancing Few-Shot Image Classification with Unlabelled Examples

Overview

Improved Few-Shot Visual Classification

This repository contains source codes for the following papers:

The code base has been authored by Peyman Bateni, Jarred Barber, Raghav Goyal, Vaden Masrani, Dr. Jan-Willemn van de Meent, Dr. Leonid Sigal and Dr. Frank Wood. The source codes build on the original code base for CNAPS authored by Dr. John Bronskill, Jonathan Gordon, James Reqeima, Dr. Sebastian Nowozin, and Dr. Richard E. Turner. We would like to thank them for their help, support and early sharing of their work. To see the original CNAPS repository, visit https://github.com/cambridge-mlg/cnaps.

Simple CNAPS

Simple CNAPS proposes the use of hierarchically regularized cluster means and covariance estimates within a Mahalanobis-distance based classifer for improved few-shot classification accuracy. This method incorporates said classifier within the same neural adaptive feature extractor as CNAPS. For more details, please refer to our paper on Simple CNAPS: Improved Few-Shot Visual Classification. The source code for this paper has been provided in the simple-cnaps-src directory. To reproduce our results, please refer to the README.md file within that folder.

Global Meta-Dataset Rank (Simple CNAPS): https://github.com/google-research/meta-dataset#training-on-all-datasets

Global Mini-ImageNet Rank (Simple CNAPS):

PWC PWC PWC PWC

Global Tiered-ImageNet Rank (Simple CNAPS):

PWC PWC PWC PWC

Transductive CNAPS

Transductive CNAPS extends the Simple CNAPS framework to the transductive few-shot learning setting where all query examples are provided at once. This method uses a two-step transductive task-encoder for adapting the feature extractor as well as a soft k-means cluster refinement procedure, resulting in better test-time accuracy. For additional details, please refer to our paper on Transductive CNAPS: Enhancing Few-Shot Image Classification with Unlabelled Examples. The source code for this work is provided under the transductive-cnaps-src directory. To reproduce our results, please refer to the README.md file within this folder.

Global Meta-Dataset Rank (Transductive CNAPS): https://github.com/google-research/meta-dataset#training-on-all-datasets

Global Mini-ImageNet Rank (Transductive CNAPS):

PWC PWC PWC PWC

Global Tiered-ImageNet Rank (Transductive CNAPS):

PWC PWC PWC PWC

Active and Continual Learning

We additionally evaluate both methods within the paradigms of "out of the box" active and continual learning. These settings were first proposed by Requeima et al., and studies how well few-shot classifiers, trained for few-shot learning, can be deployed for active and continual learning without any problem-specific finetuning or training. For additional details on our active and continual learning experiments and algorithms, please refer to our latest paper: Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning. For code and instructions to reproduce the experiments reported, please refer to the active-learning and continual-learning folders.

Meta-Dataset Results

| Dataset | Simple CNAPS | Simple CNAPS | Transductive CNAPS | Transductive CNAPS |

--shuffle_dataset False False True False True
In-Domain Datasets --- --- --- ---
ILSVRC 58.6±1.1 56.5±1.1 58.8±1.1 57.9±1.1
Omniglot 91.7±0.6 91.9±0.6 93.9±0.4 94.3±0.4
Aircraft 82.4±0.7 83.8±0.6 84.1±0.6 84.7±0.5
Birds 74.9±0.8 76.1±0.9 76.8±0.8 78.8±0.7
Textures 67.8±0.8 70.0±0.8 69.0±0.8 66.2±0.8
Quick Draw 77.7±0.7 78.3±0.7 78.6±0.7 77.9±0.6
Fungi 46.9±1.0 49.1±1.2 48.8±1.1 48.9±1.2
VGG Flower 90.7±0.5 91.3±0.6 91.6±0.4 92.3±0.4
Out-of-Domain Datasets --- --- --- ---
Traffic Signs 73.5±0.7 59.2±1.0 76.1±0.7 59.7±1.1
MSCOCO 46.2±1.1 42.4±1.1 48.7±1.0 42.5±1.1
MNIST 93.9±0.4 94.3±0.4 95.7±0.3 94.7±0.3
CIFAR10 74.3±0.7 72.0±0.8 75.7±0.7 73.6±0.7
CIFAR100 60.5±1.0 60.9±1.1 62.9±1.0 61.8±1.0
--- --- --- --- ---
In-Domain Average Accuracy 73.8±0.8 74.6±0.8 75.2±0.8 75.1±0.8
Out-of-Domain Average Accuracy 69.7±0.8 65.8±0.8 71.8±0.8 66.5±0.8
Overall Average Accuracy 72.2±0.8 71.2±0.8 73.9±0.8 71.8±0.8

Mini-ImageNet Results

Setup 5-way 1-shot 5-way 5-shot 10-way 1-shot 10-way 5-shot
Simple CNAPS 53.2±0.9 70.8±0.7 37.1±0.5 56.7±0.5
Transductive CNAPS 55.6±0.9 73.1±0.7 42.8±0.7 59.6±0.5
--- --- --- --- ---
Simple CNAPS + FETI 77.4±0.8 90.3±0.4 63.5±0.6 83.1±0.4
Transductive CNAPS + FETI 79.9±0.8 91.5±0.4 68.5±0.6 85.9±0.3

Tiered-ImageNet Results

Setup 5-way 1-shot 5-way 5-shot 10-way 1-shot 10-way 5-shot
Simple CNAPS 63.0±1.0 80.0±0.8 48.1±0.7 70.2±0.6
Transductive CNAPS 65.9±1.0 81.8±0.7 54.6±0.8 72.5±0.6
--- --- --- --- ---
Simple CNAPS + FETI 71.4±1.0 86.0±0.6 57.1±0.7 78.5±0.5
Transductive CNAPS + FETI 73.8±1.0 87.7±0.6 65.1±0.8 80.6±0.5

Citation

We hope you have found our code base helpful! If you use this repository, please cite our papers:

@InProceedings{Bateni2020_SimpleCNAPS,
    author = {Bateni, Peyman and Goyal, Raghav and Masrani, Vaden and Wood, Frank and Sigal, Leonid},
    title = {Improved Few-Shot Visual Classification},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month = {June},
    year = {2020}
}

@InProceedings{Bateni2022_TransductiveCNAPS,
    author    = {Bateni, Peyman and Barber, Jarred and van de Meent, Jan-Willem and Wood, Frank},
    title     = {Enhancing Few-Shot Image Classification With Unlabelled Examples},
    booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
    month     = {January},
    year      = {2022},
    pages     = {2796-2805}
}

@misc{Bateni2022_BeyondSimpleMetaLearning,
    title={Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning}, 
    author={Peyman Bateni and Jarred Barber and Raghav Goyal and Vaden Masrani and Jan-Willem van de Meent and Leonid Sigal and Frank Wood},
    year={2022},
    eprint={2201.05151},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

**If you would like to ask any questions or reach out regarding any of the papers, please email me directly at [email protected] (my cs.ubc.ca email may have expired by the time you are emailing as I have graduated!).

Issues
  • Some wrong typo in run_transductive_cnaps_mt.py

    Some wrong typo in run_transductive_cnaps_mt.py

    Hi, I have tested this good organized source for my dataset. But, there was some typo in run_transductive_cnaps_mt.py(https://github.com/peymanbateni/simple-cnaps/blob/4e9609b202d52df57dd8d2931bd16a914c8099f8/transductive-cnaps-src/run_transductive_cnaps_mt.py).

    In line 146, 158, "max_cluster_refinement_step_test" need to be "max_cluster_refinement_steps_test" or, it makes error when I tested this source.

    If it is right, can you add 's'?

    opened by jiyang91 1
  • Bump tensorflow-gpu from 2.6.0 to 2.6.1

    Bump tensorflow-gpu from 2.6.0 to 2.6.1

    Bumps tensorflow-gpu from 2.6.0 to 2.6.1.

    Release notes

    Sourced from tensorflow-gpu's releases.

    TensorFlow 2.6.1

    Release 2.6.1

    This release introduces several vulnerability fixes:

    • Fixes a code injection issue in saved_model_cli (CVE-2021-41228)
    • Fixes a vulnerability due to use of uninitialized value in Tensorflow (CVE-2021-41225)
    • Fixes a heap OOB in FusedBatchNorm kernels (CVE-2021-41223)
    • Fixes an arbitrary memory read in ImmutableConst (CVE-2021-41227)
    • Fixes a heap OOB in SparseBinCount (CVE-2021-41226)
    • Fixes a heap OOB in SparseFillEmptyRows (CVE-2021-41224)
    • Fixes a segfault due to negative splits in SplitV (CVE-2021-41222)
    • Fixes segfaults and vulnerabilities caused by accesses to invalid memory during shape inference in Cudnn* ops (CVE-2021-41221)
    • Fixes a null pointer exception when Exit node is not preceded by Enter op (CVE-2021-41217)
    • Fixes an integer division by 0 in tf.raw_ops.AllToAll (CVE-2021-41218)
    • Fixes a use after free and a memory leak in CollectiveReduceV2 (CVE-2021-41220)
    • Fixes an undefined behavior via nullptr reference binding in sparse matrix multiplication (CVE-2021-41219)
    • Fixes a heap buffer overflow in Transpose (CVE-2021-41216)
    • Prevents deadlocks arising from mutually recursive tf.function objects (CVE-2021-41213)
    • Fixes a null pointer exception in DeserializeSparse (CVE-2021-41215)
    • Fixes an undefined behavior arising from reference binding to nullptr in tf.ragged.cross (CVE-2021-41214)
    • Fixes a heap OOB read in tf.ragged.cross (CVE-2021-41212)
    • Fixes a heap OOB in shape inference for QuantizeV2 (CVE-2021-41211)
    • Fixes a heap OOB read in all tf.raw_ops.QuantizeAndDequantizeV* ops (CVE-2021-41205)
    • Fixes an FPE in ParallelConcat (CVE-2021-41207)
    • Fixes FPE issues in convolutions with zero size filters (CVE-2021-41209)
    • Fixes a heap OOB read in tf.raw_ops.SparseCountSparseOutput (CVE-2021-41210)
    • Fixes vulnerabilities caused by incomplete validation in boosted trees code (CVE-2021-41208)
    • Fixes vulnerabilities caused by incomplete validation of shapes in multiple TF ops (CVE-2021-41206)
    • Fixes a segfault produced while copying constant resource tensor (CVE-2021-41204)
    • Fixes a vulnerability caused by unitialized access in EinsumHelper::ParseEquation (CVE-2021-41201)
    • Fixes several vulnerabilities and segfaults caused by missing validation during checkpoint loading (CVE-2021-41203)
    • Fixes an overflow producing a crash in tf.range (CVE-2021-41202)
    • Fixes an overflow producing a crash in tf.image.resize when size is large (CVE-2021-41199)
    • Fixes an overflow producing a crash in tf.tile when tiling tensor is large (CVE-2021-41198)
    • Fixes a vulnerability produced due to incomplete validation in tf.summary.create_file_writer (CVE-2021-41200)
    • Fixes multiple crashes due to overflow and CHECK-fail in ops with large tensor shapes (CVE-2021-41197)
    • Fixes a crash in max_pool3d when size argument is 0 or negative (CVE-2021-41196)
    • Fixes a crash in tf.math.segment_* operations (CVE-2021-41195)
    • Updates curl to 7.78.0 to handle CVE-2021-22922, CVE-2021-22923, CVE-2021-22924, CVE-2021-22925, and CVE-2021-22926.
    Changelog

    Sourced from tensorflow-gpu's changelog.

    Release 2.6.1

    This release introduces several vulnerability fixes:

    • Fixes a code injection issue in saved_model_cli (CVE-2021-41228)
    • Fixes a vulnerability due to use of uninitialized value in Tensorflow (CVE-2021-41225)
    • Fixes a heap OOB in FusedBatchNorm kernels (CVE-2021-41223)
    • Fixes an arbitrary memory read in ImmutableConst (CVE-2021-41227)
    • Fixes a heap OOB in SparseBinCount (CVE-2021-41226)
    • Fixes a heap OOB in SparseFillEmptyRows (CVE-2021-41224)
    • Fixes a segfault due to negative splits in SplitV (CVE-2021-41222)
    • Fixes segfaults and vulnerabilities caused by accesses to invalid memory during shape inference in Cudnn* ops (CVE-2021-41221)
    • Fixes a null pointer exception when Exit node is not preceded by Enter op (CVE-2021-41217)
    • Fixes an integer division by 0 in tf.raw_ops.AllToAll (CVE-2021-41218)
    • Fixes a use after free and a memory leak in CollectiveReduceV2 (CVE-2021-41220)
    • Fixes an undefined behavior via nullptr reference binding in sparse matrix multiplication (CVE-2021-41219)
    • Fixes a heap buffer overflow in Transpose (CVE-2021-41216)
    • Prevents deadlocks arising from mutually recursive tf.function objects (CVE-2021-41213)
    • Fixes a null pointer exception in DeserializeSparse (CVE-2021-41215)
    • Fixes an undefined behavior arising from reference binding to nullptr in tf.ragged.cross (CVE-2021-41214)
    • Fixes a heap OOB read in tf.ragged.cross (CVE-2021-41212)
    • Fixes a heap OOB in shape inference for QuantizeV2 (CVE-2021-41211)
    • Fixes a heap OOB read in all tf.raw_ops.QuantizeAndDequantizeV* ops (CVE-2021-41205)
    • Fixes an FPE in ParallelConcat (CVE-2021-41207)
    • Fixes FPE issues in convolutions with zero size filters (CVE-2021-41209)
    • Fixes a heap OOB read in tf.raw_ops.SparseCountSparseOutput (CVE-2021-41210)

    ... (truncated)

    Commits
    • 3aa40c3 Merge pull request #52889 from tensorflow/fix-build-1-on-r2.6
    • 1a97260 Upper bound tensorflow_estimator to match release
    • b03a4f1 Merge pull request #52874 from tensorflow-jenkins/relnotes-2.6.1-11115
    • 1067732 Update RELEASE.md
    • 7882f9c Merge pull request #52875 from tensorflow-jenkins/version-numbers-2.6.1-6784
    • bf0618d Update version numbers to 2.6.1
    • edf2a35 Insert release notes place-fill
    • ea77035 Merge pull request #52865 from tensorflow/fix-build-2-on-r2.6
    • c42e447 Merge pull request #52864 from tensorflow/fix-build-1-on-r2.6
    • f0258a5 Fix build on tensorflow/core/kernels/boosted_trees/stats_ops.cc
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    dependencies 
    opened by dependabot[bot] 0
  • Bump numpy from 1.19.5 to 1.21.0

    Bump numpy from 1.19.5 to 1.21.0

    Bumps numpy from 1.19.5 to 1.21.0.

    Release notes

    Sourced from numpy's releases.

    v1.21.0

    NumPy 1.21.0 Release Notes

    The NumPy 1.21.0 release highlights are

    • continued SIMD work covering more functions and platforms,
    • initial work on the new dtype infrastructure and casting,
    • universal2 wheels for Python 3.8 and Python 3.9 on Mac,
    • improved documentation,
    • improved annotations,
    • new PCG64DXSM bitgenerator for random numbers.

    In addition there are the usual large number of bug fixes and other improvements.

    The Python versions supported for this release are 3.7-3.9. Official support for Python 3.10 will be added when it is released.

    :warning: Warning: there are unresolved problems compiling NumPy 1.21.0 with gcc-11.1 .

    • Optimization level -O3 results in many wrong warnings when running the tests.
    • On some hardware NumPy will hang in an infinite loop.

    New functions

    Add PCG64DXSM BitGenerator

    Uses of the PCG64 BitGenerator in a massively-parallel context have been shown to have statistical weaknesses that were not apparent at the first release in numpy 1.17. Most users will never observe this weakness and are safe to continue to use PCG64. We have introduced a new PCG64DXSM BitGenerator that will eventually become the new default BitGenerator implementation used by default_rng in future releases. PCG64DXSM solves the statistical weakness while preserving the performance and the features of PCG64.

    See upgrading-pcg64 for more details.

    (gh-18906)

    Expired deprecations

    • The shape argument numpy.unravel_index cannot be passed as dims keyword argument anymore. (Was deprecated in NumPy 1.16.)

    ... (truncated)

    Commits
    • b235f9e Merge pull request #19283 from charris/prepare-1.21.0-release
    • 34aebc2 MAINT: Update 1.21.0-notes.rst
    • 493b64b MAINT: Update 1.21.0-changelog.rst
    • 07d7e72 MAINT: Remove accidentally created directory.
    • 032fca5 Merge pull request #19280 from charris/backport-19277
    • 7d25b81 BUG: Fix refcount leak in ResultType
    • fa5754e BUG: Add missing DECREF in new path
    • 61127bb Merge pull request #19268 from charris/backport-19264
    • 143d45f Merge pull request #19269 from charris/backport-19228
    • d80e473 BUG: Removed typing for == and != in dtypes
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    dependencies 
    opened by dependabot[bot] 0
  • Bump tensorflow from 2.6.0 to 2.6.1

    Bump tensorflow from 2.6.0 to 2.6.1

    Bumps tensorflow from 2.6.0 to 2.6.1.

    Release notes

    Sourced from tensorflow's releases.

    TensorFlow 2.6.1

    Release 2.6.1

    This release introduces several vulnerability fixes:

    • Fixes a code injection issue in saved_model_cli (CVE-2021-41228)
    • Fixes a vulnerability due to use of uninitialized value in Tensorflow (CVE-2021-41225)
    • Fixes a heap OOB in FusedBatchNorm kernels (CVE-2021-41223)
    • Fixes an arbitrary memory read in ImmutableConst (CVE-2021-41227)
    • Fixes a heap OOB in SparseBinCount (CVE-2021-41226)
    • Fixes a heap OOB in SparseFillEmptyRows (CVE-2021-41224)
    • Fixes a segfault due to negative splits in SplitV (CVE-2021-41222)
    • Fixes segfaults and vulnerabilities caused by accesses to invalid memory during shape inference in Cudnn* ops (CVE-2021-41221)
    • Fixes a null pointer exception when Exit node is not preceded by Enter op (CVE-2021-41217)
    • Fixes an integer division by 0 in tf.raw_ops.AllToAll (CVE-2021-41218)
    • Fixes a use after free and a memory leak in CollectiveReduceV2 (CVE-2021-41220)
    • Fixes an undefined behavior via nullptr reference binding in sparse matrix multiplication (CVE-2021-41219)
    • Fixes a heap buffer overflow in Transpose (CVE-2021-41216)
    • Prevents deadlocks arising from mutually recursive tf.function objects (CVE-2021-41213)
    • Fixes a null pointer exception in DeserializeSparse (CVE-2021-41215)
    • Fixes an undefined behavior arising from reference binding to nullptr in tf.ragged.cross (CVE-2021-41214)
    • Fixes a heap OOB read in tf.ragged.cross (CVE-2021-41212)
    • Fixes a heap OOB in shape inference for QuantizeV2 (CVE-2021-41211)
    • Fixes a heap OOB read in all tf.raw_ops.QuantizeAndDequantizeV* ops (CVE-2021-41205)
    • Fixes an FPE in ParallelConcat (CVE-2021-41207)
    • Fixes FPE issues in convolutions with zero size filters (CVE-2021-41209)
    • Fixes a heap OOB read in tf.raw_ops.SparseCountSparseOutput (CVE-2021-41210)
    • Fixes vulnerabilities caused by incomplete validation in boosted trees code (CVE-2021-41208)
    • Fixes vulnerabilities caused by incomplete validation of shapes in multiple TF ops (CVE-2021-41206)
    • Fixes a segfault produced while copying constant resource tensor (CVE-2021-41204)
    • Fixes a vulnerability caused by unitialized access in EinsumHelper::ParseEquation (CVE-2021-41201)
    • Fixes several vulnerabilities and segfaults caused by missing validation during checkpoint loading (CVE-2021-41203)
    • Fixes an overflow producing a crash in tf.range (CVE-2021-41202)
    • Fixes an overflow producing a crash in tf.image.resize when size is large (CVE-2021-41199)
    • Fixes an overflow producing a crash in tf.tile when tiling tensor is large (CVE-2021-41198)
    • Fixes a vulnerability produced due to incomplete validation in tf.summary.create_file_writer (CVE-2021-41200)
    • Fixes multiple crashes due to overflow and CHECK-fail in ops with large tensor shapes (CVE-2021-41197)
    • Fixes a crash in max_pool3d when size argument is 0 or negative (CVE-2021-41196)
    • Fixes a crash in tf.math.segment_* operations (CVE-2021-41195)
    • Updates curl to 7.78.0 to handle CVE-2021-22922, CVE-2021-22923, CVE-2021-22924, CVE-2021-22925, and CVE-2021-22926.
    Changelog

    Sourced from tensorflow's changelog.

    Release 2.6.1

    This release introduces several vulnerability fixes:

    • Fixes a code injection issue in saved_model_cli (CVE-2021-41228)
    • Fixes a vulnerability due to use of uninitialized value in Tensorflow (CVE-2021-41225)
    • Fixes a heap OOB in FusedBatchNorm kernels (CVE-2021-41223)
    • Fixes an arbitrary memory read in ImmutableConst (CVE-2021-41227)
    • Fixes a heap OOB in SparseBinCount (CVE-2021-41226)
    • Fixes a heap OOB in SparseFillEmptyRows (CVE-2021-41224)
    • Fixes a segfault due to negative splits in SplitV (CVE-2021-41222)
    • Fixes segfaults and vulnerabilities caused by accesses to invalid memory during shape inference in Cudnn* ops (CVE-2021-41221)
    • Fixes a null pointer exception when Exit node is not preceded by Enter op (CVE-2021-41217)
    • Fixes an integer division by 0 in tf.raw_ops.AllToAll (CVE-2021-41218)
    • Fixes a use after free and a memory leak in CollectiveReduceV2 (CVE-2021-41220)
    • Fixes an undefined behavior via nullptr reference binding in sparse matrix multiplication (CVE-2021-41219)
    • Fixes a heap buffer overflow in Transpose (CVE-2021-41216)
    • Prevents deadlocks arising from mutually recursive tf.function objects (CVE-2021-41213)
    • Fixes a null pointer exception in DeserializeSparse (CVE-2021-41215)
    • Fixes an undefined behavior arising from reference binding to nullptr in tf.ragged.cross (CVE-2021-41214)
    • Fixes a heap OOB read in tf.ragged.cross (CVE-2021-41212)
    • Fixes a heap OOB in shape inference for QuantizeV2 (CVE-2021-41211)
    • Fixes a heap OOB read in all tf.raw_ops.QuantizeAndDequantizeV* ops (CVE-2021-41205)
    • Fixes an FPE in ParallelConcat (CVE-2021-41207)
    • Fixes FPE issues in convolutions with zero size filters (CVE-2021-41209)
    • Fixes a heap OOB read in tf.raw_ops.SparseCountSparseOutput (CVE-2021-41210)

    ... (truncated)

    Commits
    • 3aa40c3 Merge pull request #52889 from tensorflow/fix-build-1-on-r2.6
    • 1a97260 Upper bound tensorflow_estimator to match release
    • b03a4f1 Merge pull request #52874 from tensorflow-jenkins/relnotes-2.6.1-11115
    • 1067732 Update RELEASE.md
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    dependencies 
    opened by dependabot[bot] 0
  • Bump pillow from 8.3.2 to 9.0.0

    Bump pillow from 8.3.2 to 9.0.0

    Bumps pillow from 8.3.2 to 9.0.0.

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    9.0.0

    https://pillow.readthedocs.io/en/stable/releasenotes/9.0.0.html

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    9.0.0 (2022-01-02)

    • Restrict builtins for ImageMath.eval(). CVE-2022-22817 #5923 [radarhere]

    • Ensure JpegImagePlugin stops at the end of a truncated file #5921 [radarhere]

    • Fixed ImagePath.Path array handling. CVE-2022-22815, CVE-2022-22816 #5920 [radarhere]

    • Remove consecutive duplicate tiles that only differ by their offset #5919 [radarhere]

    • Improved I;16 operations on big endian #5901 [radarhere]

    • Limit quantized palette to number of colors #5879 [radarhere]

    • Fixed palette index for zeroed color in FASTOCTREE quantize #5869 [radarhere]

    • When saving RGBA to GIF, make use of first transparent palette entry #5859 [radarhere]

    • Pass SAMPLEFORMAT to libtiff #5848 [radarhere]

    • Added rounding when converting P and PA #5824 [radarhere]

    • Improved putdata() documentation and data handling #5910 [radarhere]

    • Exclude carriage return in PDF regex to help prevent ReDoS #5912 [hugovk]

    • Fixed freeing pointer in ImageDraw.Outline.transform #5909 [radarhere]

    • Added ImageShow support for xdg-open #5897 [m-shinder, radarhere]

    • Support 16-bit grayscale ImageQt conversion #5856 [cmbruns, radarhere]

    • Convert subsequent GIF frames to RGB or RGBA #5857 [radarhere]

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    dependencies 
    opened by dependabot[bot] 0
  • Adding code for all 3 papers (including all additional details)

    Adding code for all 3 papers (including all additional details)

    This pull request contains code spanning all 3 papers, including additional checkpoints, test documents, other details and a complete restructuring of the entire repository.

    opened by peymanbateni 0
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PLAI Group at UBC
PLAI Group at UBC
We present a framework for training multi-modal deep learning models on unlabelled video data by forcing the network to learn invariances to transformations applied to both the audio and video streams.

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