TWC #14

TWC #14
State-of-the-art papers with Github avatars of researchers who released code, models (in most cases) and demo apps (in few cases) along with their paper. Image created from papers described below - 1 of 2

State-of-the-art papers with Github avatars of researchers who released code, models (in most cases) and demo apps (in few cases) along with their paper. Image created from papers described below - 2 of 2

State-of-the-art  (SOTA)  updates  for 31 Oct– 6 Nov 2022

TasksWithCode weekly newsletter highlights the work of SOTA researchers. Researchers in figure above produced state-of-the-art work breaking existing records on benchmarks. They also

  • authored their paper
  • released their code
  • released models in most cases
  • released notebooks/apps in few cases

It is worth noting a significant proportion of the code release licenses allow commercial use. The only ask of these researchers from us in return is attribution. Also worth noting many of these SOTA researchers have little to no online presence.

The selected researchers broke existing records on the following tasks

  • Weakly Supervised Object Detection
  • Science Question Answering
  • Face swapping
  • Graph Regression
  • Text Summarization
  • Q&A
  • Riddle Sense
  • Semantic correspondence - The task of semantic correspondence aims to establish reliable visual correspondence between different instances of the same object category.
  • Occluded object detection
  • Continual Pretraining
  • Self-supervised image classification

This weekly is  a consolidation of daily twitter posts tracking SOTA researchers. Starting this week, daily SOTA updates are also done on - "a twitter alternative by and for the AI community"

To date,  27.4% (90,391) of total papers (329,773) published have code released along with the papers (source).

SOTA details below are snapshots of  SOTA models at the time of  publishing this newsletter. SOTA details in the  link provided below the snapshots would most likely be different from the snapshot over  time as new SOTA models emerge.

#1 on Weakly Supervised Object Detection

Paper:Object Discovery via Contrastive Learning for Weakly Supervised Object Detection
SOTA details
Github code released by Jinhwan Seo (first author in paper) Models on Github page

Model Name:  OD-WSCL

Notes:    Weakly Supervised Object Detection (WSOD) is a task that detects objects in an image using a model trained only on image-level annotations. Current state-of-the-art models benefit from self-supervised instance-level supervision, but since weak supervision does not include count or location information, the most common 'argmax' labeling method often ignores many instances of objects. To alleviate this issue, this paper  proposes a  multiple instance labeling method called object discovery. They also introduce a contrastive loss variant under weak supervision where no instance-level information is available for sampling, called weakly supervised contrastive loss (WSCL). WSCL aims to construct a credible similarity threshold for object discovery by leveraging consistent features for embedding vectors in the same class.

Demo page: No demo page to date. Project page has more examples.

License:  This codebase is a derivation of code covered by Nvidia source code license

#1 in Science Question Answering on ScienceQA

Paper: Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering
SOTA details- 1 of 2
SOTA details- 2 of 2
Github code released by  Pan Lu (first author in paper) This uses and image captioning model and a GPT-3 fine tuned model

Model Name:  GPT-3 - CoT (QCM→ALE , 2-shot)

Notes:    When answering a question, humans utilize the information available across different modalities to synthesize a consistent and complete chain of thought (CoT). This process is normally a black box in the case of deep learning models like large-scale language models. Recently, science question benchmarks have been used to diagnose the multi-hop reasoning ability and interpretability of an AI system. However, existing datasets fail to provide annotations for the answers, or are restricted to the textual-only modality, small scales, and limited domain diversity. The paper  introduces  Science Question Answering (ScienceQA), a new benchmark that consists of ~21k multimodal multiple choice questions with a diverse set of science topics and annotations of their answers with corresponding lectures and explanations.  They also tune a language model (GPT-3) to learn to generate lectures and explanations as the chain of thought (CoT) to mimic the multi-hop reasoning process when answering ScienceQA questions.

Demo page: Image captioning model. The fine tuned GPT-3 model access is not available. But the fine tuning data is available to create a fine-tuned GPT-3 model

License:  non-commercial license. CC BY-NC-SA 4.0

#1 in Face Swapping on 2 datasets

Paper: FaceDancer: Pose- and Occlusion-Aware High Fidelity Face Swapping
SOTA details
Github code released by Felix Rosberg (first author in paper) Pretrained models path can be obtained from HuggingFace app page. Author's profile image on huggingFace illustrating his model in action

Model Name:  FaceDancer (Config A)

Notes:    This paper introduces a  single-stage method for subject agnostic face swapping and identity transfer, named FaceDancer. This paper makes two  contributions: Adaptive Feature Fusion Attention (AFFA) and Interpreted Feature Similarity Regularization (IFSR). The AFFA module is embedded in the decoder and adaptively learns to fuse attribute features and features conditioned on identity information without requiring any additional facial segmentation process. In IFSR, they leverage the intermediate features in an identity encoder to preserve important attributes such as head pose, facial expression, lighting, and occlusion in the target face, while still transferring the identity of the source face with high fidelity.

Demo page: HuggingFace space demo app

License:  Non-commercial license

#1 in Graph Regression on PCQM4Mv2-LSC dataset

Paper: One Transformer Can Understand Both 2D & 3D Molecular Data
SOTA details
Github code released by  Shengjie Luo (first author in paper) Checkpoints in Github page

Model Name:  Transformer-M

Notes:    Unlike vision and language data which usually has a unique format, molecules can naturally be characterized using different chemical formulations. One can view a molecule as a 2D graph or define it as a collection of atoms located in a 3D space. For molecular representation learning, most previous works designed neural networks only for a particular data format, making the learned models likely to fail for other data formats. This work proposes  a general-purpose neural network model for chemistry to handle molecular tasks across data modalities. They develop a novel Transformer-based Molecular model called Transformer-M, which can take molecular data of 2D or 3D formats as input and generate meaningful semantic representations. Using the standard Transformer as the backbone architecture, Transformer-M develops two separated channels to encode 2D and 3D structural information and incorporate them with the atom features in the network modules. When the input data is in a particular format, the corresponding channel will be activated, and the other will be disabled. By training on 2D and 3D molecular data with properly designed supervised signals, Transformer-M automatically learns to leverage knowledge from different data modalities and correctly capture the representations. Empirical results show that Transformer-M can simultaneously achieve strong performance on 2D and 3D tasks, suggesting its broad applicability.

Demo page: None to date

License:  MIT license

#1 in Text summarization on 2 datasets

Paper: Adapting Pretrained Text-to-Text Models for Long Text Sequences
SOTA details
Github code released by  Wenhan Xiong (first author in paper) Checkpoints in Github page

Model Name:  BART-LS

Notes:    This  paper performs an empirical study of adapting an existing pretrained text-to-text model for long-sequence inputs. Through a comprehensive study along three axes of the pretraining pipeline -- model architecture, optimization objective, and pretraining corpus, they  propose a recipe to build long-context models from existing short-context models. Specifically, they replace the full attention in transformers with pooling-augmented blockwise attention, and pretrain the model with a masked-span prediction task with spans of varying length. In terms of the pretraining corpus, they find that using randomly concatenated short-documents from a large open-domain corpus results in better performance than using existing long document corpora which are typically limited in their domain coverage. With these findings, they build a long-context model that achieves competitive performance on long-text QA tasks and establishes the new state of the art on five long-text summarization datasets, often outperforming previous methods with larger model sizes.

Demo page: None to date

License:  Non commercial license. CC-BY-NC 4.0

#1 in Q&A and Riddle sense

Paper: Deep Bidirectional Language-Knowledge Graph Pretraining
SOTA details
Github code released by  Michihiro Yasunaga (first author in paper) Checkpoints in Github page

Model Name:  DRAGON, DRAGON + BioLinkBERT

Notes:    Pretraining a language model (LM) on text has been shown to help various downstream NLP tasks. Recent work has  shown that a knowledge graph (KG) can complement text data, offering structured background knowledge that provides a useful scaffold for reasoning. However, these models are not pretrained to learn a deep fusion of the two modalities at scale, limiting the potential to acquire fully joint representations of text and KG. This paper proposes DRAGON (Deep Bidirectional Language-Knowledge Graph Pretraining), a self-supervised approach to pretraining a deeply joint language-knowledge foundation model from text and KG at scale. Specifically, the model takes pairs of text segments and relevant KG subgraphs as input and bidirectionally fuses information from both modalities. This model is pretrained by unifying two self-supervised reasoning tasks, masked language modeling and KG link prediction.

Demo page: None to date

License:  Apache-2.0 license

#1 in Semantic correspondence on PF-PASCAL dataset

Paper: CATs++: Boosting Cost Aggregation with Convolutions and Transformers
SOTA details
Github code released by  Seokju Cho (first author in paper) Checkpoints in Github page

Model Name:  CATS++

Notes:   Cost aggregation is a highly important process in image matching tasks, which aims to disambiguate the noisy matching scores. Existing methods generally tackle this by hand-crafted or CNN-based methods, which either lack robustness to severe deformations or inherit the limitation of CNNs that fail to discriminate incorrect matches due to limited receptive fields and lack of adaptability. This paper introduces Cost Aggregation with Transformers (CATs) to tackle this by exploring global consensus among initial correlation map with the help of some architectural designs that allows it to fully enjoy global receptive fields of self-attention mechanism. Also, to alleviate some of the limitations that CATs may face, i.e., high computational costs induced by the use of a standard transformer that its complexity grows with the size of spatial and feature dimensions, which restrict its applicability only at limited resolution and result in rather limited performance, this paper propose CATs++, an extension of CATs. The proposed methods outperform the previous state-of-the-art methods by large margins.

Demo page: None to date

License: This code based is based on three models that have the following commercial use licenses. Apache-2.0 license and  GNU GPL 3.0 license

#1 in Occluded Object detection (instance segmentation) on COCO dataset

Paper: A Tri-Layer Plugin to Improve Occluded Detection
SOTA details
Github code released by  Guanqi Zhan (first author in paper) Checkpoints in Github page

Model Name:  Swin-B + Cascade Mask R-CNN (tri-layer modelling)

Notes:  This paper addresses the problem of detecting occluded objects.
It makes  four contributions to solve this problem : (1) a simple 'plugin' module for the detection head of two-stage object detectors to improve the recall of partially occluded objects. The module predicts a tri-layer of segmentation masks for the target object, the occluder and the occludee, and by doing so is able to better predict the mask of the target object. (2) a scalable pipeline for generating training data for the module by using amodal completion of existing object detection and instance segmentation training datasets to establish occlusion relationships. (3) They  establish a COCO evaluation dataset to measure the recall performance of partially occluded and separated objects. (4) they show that the plugin module inserted into a two-stage detector can boost the performance significantly, by only fine-tuning the detection head, and with additional improvements if the entire architecture is fine-tuned.

Demo page: None to date. Project page has additional examples

License:  Apache-2.0 license - commercial use permitted

#1 in Continual pretraining on AG News dataset

Paper: Continual Training of Language Models for Few-Shot Learning
SOTA details
Github code released by  Haowei Lin (second author in paper) Checkpoints in HuggingFace

Model Name:  CPT

Notes:  Recent work on applying large language models (LMs) achieves impressive performance in many NLP applications. Adapting or posttraining an LM using an unlabeled domain corpus can produce even better performance for end-tasks in the domain. This paper proposes the problem of continually extending an LM by incrementally post-train the LM with a sequence of unlabeled domain corpora to expand its knowledge without forgetting its previous skills. The goal is to improve the few-shot end-task learning in these domains. The resulting system is called CPT (Continual PostTraining) claims to be  the first continual post-training system.

Demo page: None to date

License:  None to date

#1 in self-supervised image classification on Imagenet (fine tuned)

Paper: Exploring Target Representations for Masked Autoencoders
SOTA details
Github code released by  Tao Kong (third author in paper) Checkpoints in Github page

Model Name:  dBOT

Notes:  Masked autoencoders  randomly mask a portion of the input and reconstruct the masked portion according to the target representations. This paper first shows that a careful choice of the target representation (tokenized version of images using DALL-E - offline teacher -  in the case of BeIT or image pixels in the case of MAE)  is unnecessary for learning good representations, since different targets tend to derive similarly behaved models. Driven by this observation, we propose a multi-stage masked distillation pipeline and use a randomly initialized model as the teacher, enabling us to effectively train high-capacity models without any efforts to carefully design target representations. Interestingly, we further explore using teachers of larger capacity, obtaining distilled students with remarkable transferring ability. On different tasks of classification, transfer learning, object detection, and semantic segmentation, the proposed method to perform masked knowledge distillation with bootstrapped teachers (dBOT) outperforms previous self-supervised methods by nontrivial margins.

Demo page: None to date

License:  Apache-2.0 license