README.md

metadata

license: mit
pipeline_tag: image-feature-extraction
tags:
  - self-supervised-learning
  - vision-transformer
  - computer-vision
  - video
  - temporal-difference
  - optical-flow
  - stereo-depth
  - dense-prediction
arxiv: 2606.15956

TDV: You Don't Need Strong Assumptions — Visual Representation Learning via Temporal Differences

Ninad Daithankar*, Alexi Gladstone*, Yann LeCun, Heng Ji University of Illinois Urbana-Champaign  ·  New York University (*Equal Contribution)

Paper | Project Page | GitHub | HF Paper

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Overview

TDV is a self-supervised video representation learning method built on a single causal assumption: the past causes the future. Rather than relying on strong inductive biases like augmentations, masking, or cropping, TDV jointly trains a frame encoder and a motion encoder such that:

current frame representation + encoded motion = next frame representation

The motion encoder processes RGB differences between consecutive frames, conditioned on the current frame representation via cross-attention. Predictions are supervised using MSE loss against an EMA teacher encoder, with DINO-style categorical cross-entropy to prevent representation collapse.

TDV matches DINO and iBOT on dense segmentation tasks while surpassing both on optical flow and stereo depth — tasks that directly reward temporally structured representations.

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Checkpoints

All checkpoints are in the checkpoints/ folder. Optimizer states and training metadata have been stripped — these are inference-ready weights.

All models are pretrained on Something-Something v2 (SSv2).

File	Model	Size	Notes
checkpoints/tdv-base.pth	TDV ViT-Base	~1.2 GB	TDV frame encoder (our method)
checkpoints/tdv-small.pth	TDV ViT-Small	~400 MB	TDV frame encoder (our method)
checkpoints/dino-base.pth	DINO ViT-Base	~739 MB	DINO baseline (teacher weights)
checkpoints/dino-small.pth	DINO ViT-Small	~220 MB	DINO baseline (teacher weights)
checkpoints/ibot-base.pth	iBOT ViT-Base	~762 MB	iBOT baseline (teacher weights)
checkpoints/ibot-small.pth	iBOT ViT-Small	~243 MB	iBOT baseline (teacher weights)

DINO and iBOT checkpoints are our reproductions trained with online KNN monitoring, used as baselines in the paper.

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Usage

Loading TDV

TDV checkpoints are PyTorch Lightning checkpoints with the optimizer states stripped. Load the state_dict directly:

import torch

ckpt = torch.load("checkpoints/tdv-base.ckpt", map_location="cpu")
state_dict = ckpt["state_dict"]

# The frame encoder keys are prefixed with "model.frame_encoder."
# Strip the prefix to load into a standalone ViT:
encoder_state_dict = {
    k.replace("model.frame_encoder.", ""): v
    for k, v in state_dict.items()
    if k.startswith("model.frame_encoder.")
}

For the full architecture and eval setup see github.com/ninaddaithankar/tdv.

Loading DINO / iBOT baselines

import torch

ckpt = torch.load("checkpoints/dino-base.pth", map_location="cpu")
teacher_weights = ckpt["teacher"]  # final model weights
# student weights also available: ckpt["student"]

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Results

TDV is evaluated on dense spatial tasks where temporal structure matters most:

• Optical Flow (MPI-Sintel): Lower EPE than DINO and iBOT baselines
• Stereo Depth (SceneFlow): Lower bad-pixel rates at 0.5px and 1px thresholds
• Semantic Segmentation (ADE20K / Cityscapes): Matches DINO and iBOT baselines

See the paper for full tables and ablations.

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Citation

@misc{daithankar2026dontneedstrongassumptions,
      title={You Don't Need Strong Assumptions: Visual Representation Learning via Temporal Differences},
      author={Ninad Daithankar and Alexi Gladstone and Yann LeCun and Heng Ji},
      year={2026},
      eprint={2606.15956},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2606.15956},
}